Information Systems Engineering and e-Business

Information system can be defined as an application of computing and communications technology to meet specific requirements. The purpose of information systems is to collect, process, store, transfer and disseminate information. The range of types of information system covers both hardware systems, with embedded software, and pure software systems. The users should have attributes like fitness for purpose, range of functionality, economy and efficiency, reliability, cost effectiveness, security and safety, and life cycle support.

Information Systems Engineering is the application of engineering principles, founded on appropriate scientific and technological disciplines, to the creation, use and support of information systems for the solution of practical problems. The scope includes hardware components (such as processors, networks and interface devices) and software components (including operating systems software, information structure software, communications protocols and application software). It also includes the design, development and use of standards and tools essential for the engineering of information systems.

Topics covered in IS engg.

A) e-Business concept

B) Information Synthesis and Decision Support Methods.

C) e-Collaboration and Networking Tools.

  1. Class Reports and Project.
  1. e-Business Concept will have following topics (9 weeks)
    1. Basics and successful stories (ch 1-3)
    2. e-Business Architecture and Model (ch 4)
    3. e-Technology: e-commerce, e-collaboration and e-Logistics (ch 5-9)
    4. Enterprise Management Tools, e.g. CRM, ERP, supply chain management and e-procurement. (ch 5-9)
    5. Decision Support Systems
    6. e-Business design

B) Information synthesis and DSS (4 weeks)

1. Distributed Information Synthesis

 

A-1) E-Business can be defined as the integration of systems, processes, organizations, value chains and entire markets using Internet-based and related technologies and concepts. Electronic Commerce is merely a part of E-Business and is limited essentially to marketing and sales processes.

An e-business is an organization that connects critical business systems directly to their critical constituencies (e.g., customers, employees, intermediaries, vendors, and suppliers) via intranets, extranets, and the World Wide Web. e-business, then, looks at that spectrum of communications handled over the Internet and using some set of Internet-enabled tools between businesses and consumers (B2C), and businesses and other businesses (B2B). The e-business definition still, however, defines only that portion of Internet usage which is for business purposes -- it does not include the personal traffic that does not have a business context.

A good website to look at (B2B concepts and solutions by IBM) http://www.as400.ibm.com/campaign/roadshow/extreme/present/b2b.htm

 

 

3) e-Technology

CRM: Customer Relationship Management

Customer Realtionship Management (CRM) is a term used to describe the technology and business philosophy that organizations are using to place the customer in the very center of their business model. The legitimacy of CRM has been proven as customer loyalty depends on a high level of personal service. CRM gives the ability to anticipate and react to the needs of their customers.

 

Marketing

Sales

Field Service

Customer Support

Identify and target best customers (based on recency, frequency, and monetary (RFM) scoring)

Improve telesales, field sales and sales management through real time information sharing among multiple employees

Ensure customer satisfaction and retention by solving customer problems quickly

Strengthen shared relationships with individualized customer care based on specific customer history and preferences

Manage marketing campaigns with clear goals and quantifiable objectives

Increase Sales efficiency through wireless and Internet-based order entry, etc.

Smoothly integrate the management of people and materials within your service organization

Improve call center efficiency and help desk support through automated scripting based on known solutions

Create and manage solid sales leads for field and telesales representatives

Improve territory management with up to date with real time account information updates

Ensure customer satisfaction by allocating, scheduling, and dispatching the right people, with the right parts, at the right time.

Increase call center efficiency through automated call routing, call tracking, entitlement processing, workflow, problem resolution

Increase in marketing and cross-selling opportunities

Improve the entire sales force by capturing, distributing and leveraging the success and expertise of your highest performers

 

Decrease support and service costs while increasing customer satisfaction by extending web-based support functionality directly to your customers,

Increase returns on marketing investments through tight and accurate targeting and one-to-one marketing

Increase revenue per call by focusing on growing the best accounts

 

Centralize all customer contact - from sales, support, field service and marketing - to deliver excellent customer service

Improve product development process with knowledge gained directly from customer interaction

 

e-CRM: The increased reliance on the internet as a customer interaction channel has led to the expansion of the traditional CRM model.

eCRM gives customers the power to research, configure, and make purchases on-line. Additionally, it enables them to seek support 24 x7 in a friendly, personalized environment.

 

The Intersection of CRM and
e-Business...

By combining technologies from CRM and e-Business applications, Apex IT can help organizations offer personalized customer interaction on-line. Implementing eCRM involves a process of selecting from a vast array of software applications and engineering integration between them.

The Components of eCRM

1) Analytical Applications analyze customer interactions to help direct marketing efforts, product development, proactive service, and sales initiatives

Analytical Applications analyze customer and product preference data collected during customer interactions.

When an organization has access to better data than its competition a high ROI can be achieved. But to really maximize return, this data must be instantly accessible in a relevant format that is easy to understand. Selecting and integrating the right solutions are essential.

Analytical applications serve to answer several key questions:

  • Which customers are using the web site to conduct business with you?
  • From where do your customers find you?
  • Are specific web pages more helpful or visited more frequently than others?
  • What products are purchased most?
  • Are certain products being dropped more frequently from shopping carts?
  • What questions do web customers ask most frequently during their self-service interactions?
  • Which customers should you target for a specific marketing campaign?
  • Which customers are likely to require an upgrade or complimentary product?

 

 

 

 

Internet-based "eMarketing" uses the Internet and e-mail functionality to reach out to highly qualified prospects

eMarketing combines the Internet and e-mail applications to conduct highly targeted marketing programs. Not only is this form of marketing inexpensive, the response rates are dramatically higher than traditional marketing methods.

In fact, response rates have been calculated to be in excess of 10 times higher than traditional direct marketing. This is due to one of the most powerful features of eMarketing.

Campaign analysis is simple and accurate resulting in continuous improvement in targeting the audience with an increasingly precise marketing message.

eMarketing derives its ultimate value from its unique ability to send targeted messages to customers whose past interactions make them highly qualified prospects of new or complimentary products.

 

 

What is it?

  • Highly targeted marketing to customers and prospects using e-mail and the Internet.

Benefits

  • Highly targeted campaigns (based on individual customer profiles and interactions)
  • Much less expensive than traditional marketing channels
  • Higher response rates (up to 10 times higher than direct mail accordng to analysts)
  • Campaign analysis is simple, accurate and iterative
  • Campaigns undergo continuous improvement, targeting the audience with an increasingly precise marketing message

 

 

 

 

 

 

 

Internet-based "eSales" uses a variety of innovative technology to assist on-line shoppers find, configure, and purchase even the most complex products on-line

A successful Internet-based sales (eSales) strategy will rely on combining four separate technologies:

  1. Content Management Applications
  2. Real-time Personalization Engines
  3. Commerce Servers
  4. Configurators

 

Each of these applications within the eSales component is crucial for the success of a web-based sales strategy.

Like the Analytical and eMarketing applications described previously, there are many software vendors vying for leadership in each of these four categories.

Below is a summary of the functionality of each of the four "sub-components" of eSales.

Content Management Applications

Embedded into the infrastructure of eCRM is the content management functionality.

These applications increase an organization's ability to manage their web sites as massive amounts information are added and/or deleted.

Leading vendors in this category include Documentum, Eprise, Interwoven, Open Market and Vignette.

 

Real-time Personalization Engines

eCRM effectiveness depends upon the ability to deliver personalized service and content to individuals based upon that customer's profile and internally determined business rules.

It is the personalization functionality that dramatically improves the success rate of cross-sell / up-sell opportunities.

Leading vendors in this category include Art Technology, Blue Martini, BroadVision Data Sage, Interworld, NetPerceptions, RightPoint, and Vignette.

Commerce
Servers


Commerce servers represent the enabling technology that give organizations the ability to use their web sites as a virtual "store" at which their customers can purchase products and services.

Leading vendors in this category include Art Technology, Blue Martini, BroadVision, IBM, Interworld, Intershop, Microsoft, Netscape.

Configurators


Configurators assist the customer (or even corporate sales representatives) to select and purchase products that have been tailored to their specific needs.

Configurators are an absolute necessity when the product in question is complex in nature (i.e. computer systems, automobiles, etc.). Configurators create value for both the buyer and the seller. Both parties save time as the purchase of a complex product is quick.

Additionally, the customer is guaranteed that the product purchased will meet his or her specific needs.

Perhaps the most important internal benefit is that all the data regarding customer preferences is captured and can be leveraged during future marketing and sales efforts.

Leading vendors in this category include BT Squared, Calico, Clarify, FirePond, Pangaea, Siebel .

 

Product Delivery Systems insure that the web-based customer receives their purchases as expected

To ensure the timely delivery of products to "e-customers," eCRM enlists Product Delivery technology. Product Delivery Technology begins the moment the order is placed. It coordinates order processing, picking, packing, and preferred method of shipping.

After the customer successfully configures and purchases a product on-line, customer satisfaction will hinge upon the timely fulfillment of that order. Product delivery technology becomes an obvious key component of eCRM.

 

 

Internet-based Customer Service uses e-mail management and response technologies to offer legitimate real-time multi-channel customer service

Internet-based Customer Service is derived from the use of two types of support technologies:

  1. E-Mail Management and Auto Response Applications
  2. Self-service / Proactive Service Systems

 

E-mail Management and Auto Response Applications

As the preferred communication channel for customers who use the Internet, e-mail has become a critical link between customers and business. Therefore, it is essential to offer customers real-time service in response to e-mail inquiries.

E-mail management applications receive customer inquiries and place them in queue. Further it routes them to the correct response agent, accesses the appropriate knowledge base, notifies all concerned with the resolution of the problem, and produces all corresponding reports.

The organization that can adequately handle its customer e-mail strengthens its image in the eyes of their customers. The organization that cannot will often loses their customers to more responsive competition.

Key Developers of E-Mail Management and Auto Response Applications include:

  • Aptex
  • Brightware
  • Clarify
  • eGain
  • General Interactive
  • Genesys
  • GI
  • Kana Communications
  • netDialog
  • ServiceSoft
  • Siebel
  • Webline and others…


Self-service / Proactive Service Systems Self-service Applications

Self-service applications save the organization money in reduced support costs, while simultaneously increasing customer satisfaction with quick solutions to individual problems…

Through web-based self-service, customers can get the level of support they need at anytime 24/7/365.

Using knowledge bases, customers can solve problems on their own using simple technology-enabled techniques such as keyword searches, decision tree analysis and the like. Proactive Service uses technology to send preventative service advice to customers based upon the individual customer's profile.

Overall, this technology empowers the organization to constantly improve the knowledge bases from which these solutions are derived, and content management applications can add and update information as customer interaction data in obtained and analyzed.

Key Developers of Self-service and Proactive Service Systems include:

  • Clarify
  • Inference
  • Motive
  • netDialog
  • Octane
  • Primus
  • ServiceSoft
  • ServiceWare
  • Siebel and others…

 

 

 

eSales / eService Hybrid allows members of the sales and support teams to collaborate in real-time over the Internet to solve support issues and capitalize on sales opportunities

The duties of sales representatives and service professionals are increasing overlapping. To effectively increase sales while providing a world-class level of customer service on-line, technologies that enable an innovative hybrid of web-based sales and service are necessary. Examples of these technologies are referred to as Collaboration Tools.

Collaboration tools offer live interaction during service or sales inquiries. To a large extent, this functionality is missing from today's e-business world.

Applications that allow the organization to communicate via "chat" format will allow representatives to communicate with multiple customers with similar problems simultaneously. Further, "Teleweb" applications allow representatives from sales or support to communicate with customers via the phone and on-line at once. This type of support provides a very deep level of service during instances when highly detailed discussions or explanations are needed.

 

Benefits of CRM

Ultimately, by implementing CRM, a business can expect lower cost of sales and customer service and increased sales revenue because of the following benefits:

Systemization - Enables the business to implement processes and methodologies on a company-wide basis. With a CRM system in place, selling to and supporting customers is no longer a "shoot from the hip" routine.

Team Approach – Because all employees are utilizing one system to support and service customers, everyone is "in the know" when the need arises to respond quickly to an issue or concern that cannot easily be answered by a single contact person. CRM provides the summary information needed, accumulated from each department in order to view information as a whole and not as a segmented piece of data.

Company Valuation – Increasingly, companies hoping to announce IPO’s and or solicit venture funding are relying on CRM in order to boost the company’s valuation. CRM enable a business to demonstrate the ability to quickly supply critical figures such as number of customers, leads, sales forecasts, sales results, etc. at a moment’s notice.

Improved Efficiency for Customers and Vendors – With CRM and, especially with new technology available to incorporate the Internet, a single application engine can support a seamless front end which can be customized to accommodate the various levels of end users (customers, VAR’s, Sales Teams, etc.) This design will not only eliminate multiple applications within the company but will also empower all levels of users to contribute and extract information. Customers can enter and retrieve data using the same front end application as customer service, sales or distribution and have access to that data on a 24/7 basis.

Improved Focus on Selling and Service – Because CRM is handling the repetitive tasks involved during the sales and support process, agents can focus their energy and talents on doing what they were hired to do.

Competitive Edge – With all the above advantages, which company do you think will be better equipped to leverage its resources in a competitive environment? The one with CRM or without CRM? It’s a "no brainer"!

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ERP

The ABCs of ERP
Compiled from reports by Christopher Koch, Derek Slater and E. Baatz

(http://www.cio.com/forums/erp/edit/122299_erp.html)



What is ERP?
How long will an ERP project take?
What will ERP fix in my business?
Will ERP fit the ways I do business?
What does ERP really cost?
When will I get payback from ERP—and how much will it be?
What are the unforeseen costs of ERP?
How do you configure ERP software?
How do companies organize their ERP projects?
How does ERP fit with electronic commerce?

What is ERP?

Enterprise resource planning software, or ERP, doesn't live up to its acronym. Forget about planning—it doesn't do that—and forget about resource, a throwaway term. But remember the enterprise part. This is ERP's true ambition. It attempts to integrate all departments and functions across a company onto a single computer system that can serve all those different departments' particular needs.

That is a tall order, building a single software program that serves the needs of people in finance as well as it does the people in human resources and in the warehouse. Each of those departments typically has its own computer system, each optimized for the particular ways that the department does its work. But ERP combines them all together into a single, integrated software program that runs off a single database so that the various departments can more easily share information and communicate with each other.

That integrated approach can have a tremendous payback if companies install the software correctly. Take a customer order, for example. Typically, when a customer places an order, that order begins a mostly paper-based journey from in-basket to in-basket around the company, often being keyed and re-keyed into different departments' computer systems along the way. All that lounging around in in-baskets causes delays and lost orders, and all the keying into different computer systems invites errors. Meanwhile, no one in the company truly knows what the status of the order is at any given point because there is no way for the finance department, for example, to get into the warehouse's computer system to see whether the item has been shipped. "You'll have to call the warehouse," is the familiar refrain heard by frustrated customers.

How can ERP improve a company's business performance?

ERP automates the tasks involved in performing a business process—such as order fulfillment, which involves taking an order from a customer, shipping it and billing for it. With ERP, when a customer service representative takes an order from a customer, he or she has all the information necessary to complete the order (the customer's credit rating and order history, the company's inventory levels and the shipping dock's trucking schedule). Everyone else in the company sees the same computer screen and has access to the single database that holds the customer's new order. When one department finishes with the order it is automatically routed via the ERP system to the next department. To find out where the order is at any point, one need only log into the ERP system and track it down. With luck, the order process moves like a bolt of lightning through the organization, and customers get their orders faster and with fewer errors than before. ERP can apply that same magic to the other major business processes, such as employee benefits or financial reporting.

That, at least, is the dream of ERP. The reality is much harsher.

Let's go back to those inboxes for a minute. That process may not have been efficient, but it was simple. Finance did its job, the warehouse did its job, and if anything went wrong outside of the department's walls, it was somebody else's problem. Not anymore. With ERP, the customer service representatives are no longer just typists entering someone's name into a computer and hitting the return key. The ERP screen makes them business people. It flickers with the customer's credit rating from the finance department and the product inventory levels from the warehouse. Will the customer pay on time? Will we be able to ship the order on time? These are decisions that customer service representatives have never had to make before and which affect the customer and every other department in the company. But it's not just the customer service representatives who have to wake up. People in the warehouse who used to keep inventory in their heads or on scraps of paper now need to put that information online. If they don't, customer service will see low inventory levels on their screens and tell customers that their requested item is not in stock. Accountability, responsibility and communication have never been tested like this before.

How long will an ERP project take?

Companies that install ERP do not have an easy time of it. Don't be fooled when ERP vendors tell you about a three or six month average implementation time. Those short (that's right, six months is short) implementations all have a catch of one kind or another: the company was small, or the implementation was limited to a small area of the company, or the company only used the financial pieces of the ERP system (in which case the ERP system is nothing more than a very expensive accounting system). To do ERP right, the ways you do business will need to change and the ways people do their jobs will need to change too. And that kind of change doesn't come without pain. Unless, of course, your ways of doing business are working extremely well (orders all shipped on time, productivity higher than all your competitors, customers completely satisfied), in which case there is no reason to even consider ERP.

The important thing is not to focus on how long it will take—real transformational ERP efforts usually run between one to three years, on average—but rather to understand why you need it and how you will use it to improve your business.

What will ERP fix in my business?

There are three major reasons why companies undertake ERP:
To integrate financial data. —As the CEO tries to understand the company's overall performance, he or she may find many different versions of the truth. Finance has its own set of revenue numbers, sales has another version, and the different business units may each have their own versions of how much they contributed to revenues. ERP creates a single version of the truth that cannot be questioned because everyone is using the same system.
To standardize manufacturing processes. —Manufacturing companies—especially those with an appetite for mergers and acquisitions—often find that multiple business units across the company make the same widget using different methods and computer systems. Standardizing those processes and using a single, integrated computer system can save time, increase productivity and reduce headcount.
To standardize HR information. —Especially in companies with multiple business units, HR may not have a unified, simple method for tracking employee time and communicating with them about benefits and services. ERP can fix that.

In the race to fix these problems, companies often lose sight of the fact that ERP packages are nothing more than generic representations of the ways a typical company does business. While most packages are exhaustively comprehensive, each industry has its quirks that make it unique. Most ERP systems were designed to be used by discreet manufacturing companies (who make physical things that can be counted), which immediately left all the process manufacturers (oil, chemical and utility companies that measure their products by flow rather than individual units) out in the cold. Each of these industries has struggled with the different ERP vendors to modify core ERP programs to their needs.

Will ERP fit the ways I do business?

It's critical for companies to figure out if their ways of doing business will fit within a standard ERP package before the checks are signed and the implementation begins. The most common reason that companies walk away from multimillion dollar ERP projects is that they discover that the software does not support one of their important business processes. At that point there are two things they can do: They can change the business process to accommodate the software, which will mean deep changes in long-established ways of doing business (that often provide competitive advantage) and shake up important peoples' roles and responsibilities (something that few companies have the stomach for). Or they can modify the software to fit the process, which will slow down the project, introduce dangerous bugs into the system and make upgrading the software to the ERP vendor's next release excruciatingly difficult, because the customizations will need to be torn apart and rewritten to fit with the new version.

Needless to say, the move to ERP is a project of breathtaking scope, and the price tags on the front end are enough to make the most placid CFO a little twitchy. In addition to budgeting for software costs, financial executives should plan to write checks to cover consulting, process rework, integration testing and a long laundry list of other expenses before the benefits of ERP start to manifest themselves. Underestimate the price of teaching users their new job processes can lead to a rude shock down the line, So can failure to consider data warehouse integration requirements and the cost of extra software to duplicate the old report formats. A few oversights in the budgeting and planning stage can send ERP costs spiraling out of control faster than oversights in planning almost any other information system undertaking.

What does ERP really cost?

Meta Group recently did a study looking at the Total Cost of Ownership (TCO) of ERP, including hardware, software, professional services, and internal staff costs. The TCO numbers include getting the software installed and the two years afterward, which is when the real costs of maintaining, upgrading and optimizing the system for your business are felt. Among the 63 companies surveyed—including small, medium and large companies in a range of industries—the average TCO was $15 million (the highest was $300 million and lowest was $400,000). While it’s hard to draw a solid number from that kind of a range of companies and ERP efforts, Meta came up with one statistic that proves that ERP is expensive no matter what kind of company is using it. The TCO for a "heads-down" user over that period was a staggering $53,320.

When will I get payback from ERP—and how much will it be?

Don’t expect to revolutionize your business with ERP. It is a navel gazing exercise that focuses on optimizing the way things are done internally rather than with customers, suppliers or partners. Yet the navel gazing has a pretty good payback if you’re willing to wait for it—a Meta group study of 63 companies found that it took eight months after the new system was in (31 months total) to see any benefits. But the median annual savings from the new ERP system was $1.6 million per year.

The Hidden Costs of ERP

Although different companies will find different land mines in the budgeting process, those who have implemented ERP packages agree that certain costs are more commonly overlooked or underestimated than others. Armed with insights from across the business, ERP pros vote the following areas as most likely to result in budget overrun.

Training

Training is the near-unanimous choice of experienced ERP implementers as the most elusive budget item. It's not so much that this cost is completely overlooked as it is consistently underestimated. Training expenses are high because workers almost invariably have to learn a new set of processes, not just a new software interface.

Integration and Testing

Testing the links between ERP packages and other corporate software links that have to be built on a case-by-case basis is another often underestimated cost. A typical manufacturing company may have add-on applications for logistics, tax, production planning and bar coding. If this laundry list also includes customization of the core ERP package, expect the cost of integrating, testing and maintaining the system to skyrocket.

As with training, testing ERP integration has to be done from a process-oriented perspective. Instead of plugging in dummy data and moving it from one application to the next, veterans recommend running a real purchase order through the system, from order entry through shipping and receipt of payment-the whole order-to-cash banana-preferably with the participation of the employees who will eventually do those jobs.

Data conversion

It costs money to move corporate information, such as customer and supplier records, product design data and the like, from old systems to new ERP homes. Although few CIOs will admit it, most data in most legacy systems is of little use. Companies often deny their data is dirty until they actually have to move it to the new client/server setups that popular ERP packages require. Consequently, those companies are more likely to underestimate the cost of the move. But even clean data may demand some overhaul to match process modifications necessitated—or inspired—by the ERP implementation.

Data analysis

Often, the data from the ERP system must be combined with data from external systems for analysis purposes. Users with heavy analysis needs should include the cost of a data warehouse in the ERP budget—and they should expect to do quite a bit of work to make it run smoothly. Users are in a pickle here: Refreshing all the ERP data in a big corporate data warehouse daily is difficult, and ERP systems do a poor job of indicating which information has changed from day to day, making selective warehouse updates tough. One expensive solution is custom programming. The upshot is that the wise will check all their data analysis needs before signing off on the budget.

Consultants Ad Infinitum

When users fail to plan for disengagement, consulting fees run wild. To avoid this, companies should identify objectives for which its consulting partners must aim when training internal staff. Include metrics in the consultants' contract; for example, a specific number of the user company's staff should be able to pass a project-management leadership test—similar to what Big Five consultants have to pass to lead an ERP engagement.

Replacing Your Best and Brightest

It is accepted wisdom that ERP success depends on staffing the project with the best and brightest from the business and IS. The software is too complex and the business changes too dramatic to trust the project to just anyone. The bad news is, a company must be prepared to replace many of those people when the project is over. Though the ERP market is not as hot as it once was, consulting firms and other companies that have lost their best people will be hounding yours with higher salaries and bonus offers than you can afford—or that your HR policies permit. Huddle with HR early on to develop a retention bonus program and to create new salary strata for ERP veterans. If you let them go, you'll wind up hiring them—or someone like them—back as consultants for twice what you paid them in salaries.

Implementation Teams Can Never Stop

Most companies intend to treat their ERP implementations as they would any other software project. Once the software is installed, they figure, the team will be scuttled and everyone will go back to his or her day job. But after ERP, you can't go home again. You're too valuable. Because they have worked intimately with ERP, they know more about the sales process than the salespeople do and more about the manufacturing process than the manufacturing people do. Companies can't afford to send their project people back into the business because there's so much to do after the ERP software is installed. Just writing reports to pull information out of the new ERP system will keep the project team busy for a year at least. And it is in analysis—and, one hopes, insight—that companies make their money back on an ERP implementation. Unfortunately, few IS departments plan for the frenzy of post-ERP installation activity, and fewer still build it into their budgets when they start their ERP projects. Many are forced to beg for more money and staff immediately after the go-live date, long before the ERP project has demonstrated any benefit.

Waiting for ROI

One of the most misleading legacies of traditional software project management is that the company expects to gain value from the application as soon as it is installed; the project team expects a break, and maybe a pat on the back. Neither expectation applies to ERP. Most don't reveal their value until after companies have had them running for some time and can concentrate on making improvements in the business processes that are affected by the system. And the project team is not going to be rewarded until their efforts pay off.

Post-ERP Depression

ERP systems often wreak cause havoc in the companies that install them. In a recent Deloitte Consulting survey of 64 Fortune 500 companies, one in four admitted that they suffered a drop in performance when their ERP systems went live. The true percentage is undoubtedly much higher. The most common reason for the performance problems is that everything looks and works differently from the way it did before. When people can't do their jobs in the familiar way and haven't yet mastered the new way, they panic, and the business goes into spasms.

How do you configure ERP software?

Even if a company installs ERP software for the so-called right reasons and everyone can agree on the optimal definition of a customer, the inherent difficulties of implementing something as complex as ERP is like, well, teaching an elephant to do the hootchy-kootchy. The packages are built from database tables, thousands of them, that IS programmers and end users must set to match their business processes; each table has a decision "switch" that leads the software down one decision path or another. By presenting only one way for the company to do each task—say, run the payroll or close the books—a company's individual operating units and far-flung divisions are integrated under one system. But figuring out precisely how to set all the switches in the tables requires a deep understanding of the existing processes being used to operate the business. As the table settings are decided, these business processes are reengineered, ERP's way. Most ERP systems are not shipped as a shell system in which customers must determine at the minutia level how all the functional procedures should be set, making thousands of decisions that affect how their system behaves in line with their own business activities. Most ERP systems are preconfigured, allowing just hundreds-rather than thousands-of procedural settings to be made by the customer.

How do companies organize their ERP projects?

Based on our observations, there are three commonly used ways of installing ERP.

The Big Bang—In this, the most ambitious and difficult of approaches to ERP implementation, companies cast off all their legacy systems at once and implement a single ERP system across the entire company.

Though this method dominated early ERP implementations, few companies dare to attempt it anymore because it calls for the entire company to mobilize and change at once. Most of the ERP implementation horror stories from the late '90s warn us about companies that used this strategy. Getting everyone to cooperate and accept a new software system at the same time is a tremendous effort, largely because the new system will not have any advocates. No one within the company has any experience using it, so no one is sure whether it will work. Also, ERP inevitably involves compromises. Many departments have computer systems that have been honed to match the ways they work. In most cases, ERP offers neither the range of functionality, nor the comfort of familiarity that a custom legacy system can offer. In many cases, the speed of the new system may suffer because it is serving the entire company rather than a single department. ERP implementation requires a direct mandate from the CEO.

Franchising strategy—This approach suits large or diverse companies that do not share many common processes across business units. Independent ERP systems are installed in each unit, while linking common processes, such as financial book keeping, across the enterprise.

This has emerged as the most common way of implementing ERP. In most cases, the business units each have their own "instances" of ERP—that is, a separate system and database. The systems link together only to share the information necessary for the corporation to get a performance big picture across all the business units (business unit revenues, for example), or for processes that don't vary much from business unit to business unit (perhaps HR benefits). Usually, these implementations begin with a demonstration or "pilot" installation in a particularly open-minded and patient business unit where the core business of the corporation will not be disrupted if something goes wrong. Once the project team gets the system up and running and works out all the bugs, the team begins selling other units on ERP, using the first implementation as a kind of in-house customer reference. Plan for this strategy to take a long time.

Slam-dunk—ERP dictates the process design in this method, where the focus is on just a few key processes, such as those contained in an ERP system's financials module. The slam-dunk is generally for smaller companies expecting to grow into ERP.

The goal here is to get ERP up and running quickly and to ditch the fancy reengineering in favor of the ERP system's "canned" processes. Few companies that have approached ERP this way can claim much payback from the new system. Most use it as an infrastructure to support more diligent installation efforts down the road. Yet many discover that a slammed in ERP system is little better than a legacy system, because it doesn't force employees to change any of their old habits. In fact, doing the hard work of process reengineering after the system is in can be more challenging than if there had been no system at all, because at that point few people in the company will have felt much benefit.

How does ERP fit with electronic commerce?

After all of that work inventing, perfecting and selling ERP to the world, the major ERP vendors are having a hard time shifting gears from making the applications that streamline business practices inside a company to those that face outward to the rest of the world. These days, the hottest areas for outward-looking (that is, Internet) post-ERP work are electronic commerce, planning and managing your supply chain, and tracking and serving customers. Most ERP vendors have been slow to develop offerings for these areas, and they face stiff competition from niche vendors. ERP vendors have the advantage of a huge installed base of customers and a virtual stranglehold on the "back office" functions—such as order fulfillment. Recently ERP vendors have begun to shrink their ambitions and focus on being the back-office engine that powers electronic commerce, rather than trying to own all the software niches that are necessary for a good electronic commerce Website. Indeed, as the niche vendors make their software easier to hook into electronic commerce Web sites, and as middleware vendors make it easier for IS departments to hook together applications from different vendors, many people wonder how much longer ERP vendors can claim to be the primary software platform for the Fortune 500.

 

http://www.usc.edu/schools/business/atisp/ERP/

ERP Cases:

Information Flows in Manufacturing Under SAP R/3, Stanford OIT 13, Seungjin Whang, Wendell Gilland and Hau Lee, June 1995

Not an issues case. However, the case allows the ability to address issues concerning the flow of information and the representation of that information in an ERP system.

SAP America Harvard 9-397-057, Artemis March and David Garvin, 1996

Case provides an indepth understanding of the dominant ERP firm. The case allows access to understanding SAP's growth, what kinds of problems that SAP faced at the time and how they addressed those problems. This is a great case for use early in an ERP course to provide students with an understanding of a specific firm and the industry.

Vandelay Industries, Harvard 9-697-037, Andrew McAfee and David Upton, 1996

Case allows access to a wide range of issues. Provides access to the following (and many more) issues such as

What is an ERP System?

What is SAP and why have they been successful?

Why do companies need ERP systems?

To what extent do firms let divisions implement ERP systems differently?

What is clean sheet vs. technology enable ERP?

What is the impact of ERP on competitive balance?

Dow Corning A, B, and C, Jeanne Ross, MIT, 1998.

Sequence of cases that traces Dow Corning's implementation of SAP during the 1990's. Describes how the firm captured its learnings from the pilot implementation and successfully implemented SAP on a global basis. Describes some of the issues transitioning from R/2 to R/3.

Cisco Systems, Inc.: Implementing ERP, Harvard 9-699-022, Mark Cottelear, Robert Austin and Richard Nolan 1998.

I have had some great discussions with this case. Allows access to a wide range of issues, in a firm that gathers great interest from most students.

Timberjack Parts: Packaged Software Selection Project, Harvard 9-398-085, Darryl Romanow, Mark Keil and Warren McFarland, 1998.

Provides insight into the classic process of choosing software, with an application to ERP systems. Mentions Lawson, CA, Oracle and QAD. Done in the setting of a smaller company, and provides access to many issues regarding different company branches having the same software.

Siemens Power Corporation I and II, Sabine Hirt (see teaching plan and class 11) and E. Burton Swanson, 1998

Adopting SAP at Siemens Power Corporation. I is a great case to study some issues in risk management. II gets into project management and training.

Tektronix, Inc.: Implementing ERP, Harvard, 9-699-043, George Westerman, Mark Cottelear, Robert Austin and Richard Nolan, February 1999

Allows analysis of stages vs. big bang and how to manage risk in ERP systems.

Harley Davidson Motor Company: Enterprise Software Selection, Harvard 9-600-006, Deborah Sole, Mark Cotteleer and Robert Austin, 1999.

Provides real drill down into management issues in the process of actually choosing an ERP System. Case provides depth to a critical portion of the ERP life cycle.

Implementing SAP R/3 at the University of Nebraska, Tim Sieber, Keng Siau, Fiona Nah and Michelle Sieber

Provides some interesting data regarding implementation at a University and how the implementation took place. http://www.uncc.edu/icis99/program/TC9904.PDF

Sieber, T., Siau, K., Nah, F., Sieber, M., "SAP Implementation at the University of Nebraska," Journal of Information Technology Cases and Applications, Vol. 2., No. 1, 2000, pp. 41-72.

SAP Implementation at Geneva Chemical, Anol Bhattacherjee, 1999

A "freeware" case available at http://www.cob.asu.edu/fac/ABhatt/cases/Geneva.pdf

ERP Papers

Thomas H. Davenport, "Putting the Enterprise into the Enterprise System," Harvard Business Review, July - August 1998, pp. 121-131

Cedric Escalle, Mark Cotteleer and Robert Austin, "Enterprise Resource Planning," Harvard 9-699-020, 1999

ERP Sites

SAP University Links, Maintained by Ron MacKinnon, St. Fancis Xzvier University, http://juliet.stfx.ca/~infosys/sap_univ.htm

SAP University Alliance Program, http://wwwext03.sap.com/usa/education/alliance/alliancemem.asp

SAP Portal, Maintained by Ron MacKinnon, St. Fancis Xzvier University, http://juliet.stfx.ca/~infosys/sapindex.htm

SAP Frequently Asked Questions - http://www.sapfaq.com/

Tech Republic - http://www.erpsupersite.com/

SAP Resource Center - http://src.thehub.com.au/

BAAN-- http://www.baan.com/

PeopleSoft -- http://www.peoplesoft.com/

Oracle -- http://www.oracle.com/

SAP -- http://www.sap.com

HP - http://www.sap.hp.com/public/

IBM - http://houns54.clearlake.ibm.com/solutions/erp/erppub.nsf/detailcontacts/erp_software_developer_business_partners

IDS Scheer - http://www.ids-scheer.de/english/index2.htm

 

 

 

 

 

 

ARTICLE (http://www.expressindia.com/newads/bsl/advant.htm)

 

ERP Systems -- Using IT to gain a competitive advantage

Shankarnarayanan S

In the past decade the business environment has changed dramatically. The world has become a small and very dynamic marketplace. Organizations today confront new markets, new competition and increasing customer expectations. This has put a tremendous demand on manufacturers to; 1) Lower total costs in the complete supply chain 2) Shorten throughput times 3) Reduce stock to a minimum 4) Enlarge product assortment 5) Improve Product quality 6) Provide more reliable delivery dates and higher service to the customer 7) Efficiently coordinate global demand, supply and production. Thus today's organization have to constantly re-engineer their business practices and procedures to be more and more responsive to customers and competition. In the 1990's Information technology and Business Process re-engineering, used in conjunction with each other, have emerged as important tools which give organisations the leading edge.

ERP Systems - Evolution
The focus of manufacturing systems in the 1960's was on Inventory control. Most of the software packages then (usually customized) were designed to handle inventory based on traditional inventory concepts. In the 1970's the focus shifted to MRP (Material Requirement Planning) systems which translated the Master Schedule built for the end items into time-phased net requirements for the sub-assemblies, components and raw materials planning and procurement.

In the 1980's the concept of MRP-II (Manufacturing Resources Planning) evolved which was an extension of MRP to shop floor and Distribution management activities. In the early 1990's, MRP-II was further extended to cover areas like Engineering, Finance, Human Resources, Projects Management etc i.e. the complete gamut of activities within any business enterprise. Hence, the term ERP (Enterprise Resource Planning) was coined.

In addition to system requirements, ERP addresses technology aspects like client/server distributed architecture, RDBMS, object oriented programming etc. ERP Systems - Bandwidth ERP solutions address broad areas within any business like Manufacturing, Distribution, Finance, Project Management. Service and Maintenance, Transportation etc. A seamless integration is essential to provide visibility and consistency across the enterprise.

An ERP system should be sufficiently versatile to support different manufacturing environments like make-to-stock, assemble-to-order and engineer-to-order. The customer order decoupling point (CODP) should be flexible enough to allow the co-existence of these manufacturing environments within the same system. A typical example here could be Godrej & Boyce Mfg.Co., which has businesses spread over all these manufacturing environments. It is also very likely that the same product may migrate from one manufacturing environment to another during its produce life cycle.

The system should be complete enough to support both Discrete as well as Process manufacturing scenario's. The efficiency of an enterprise depends on the quick flow of information across the complete supply chain i.e. from the customer to manufacturers to supplier. This places demands on the ERP system to have rich functionality across all areas like sales, accounts receivable, engineering, planning, Inventory Management, Production, Purchase, accounts payable, quality management, production, distribution planning and external transportation. EDI (Electronic Data Interchange) is an important tool in speeding up communications with trading partners.

More and more companies are becoming global and focusing on down-sizing and decentralizing their business. ABB and Northern Telecom are examples of companies which have business spread around the globe. For these companies to manage their business efficiently, ERP systems need to have extensive multi-site management capabilities. The complete financial accounting and management accounting requirements of theorganization should be addressed. It is necessary to have centralized or de-centralized accounting functions with complete flexibility to consolidate corporate information.

For companies undertaking large scale and complex EPC projects, tools should be available for cost-effective project management, project planning and project control. After-sales service should be streamlined and managed efficiently. A strong EIS (Enterprise Information System) with extensive drill down capabilities should be available for the top management to get a birds eye view of the health of their organisation and help them to analyze performance in key areas.

Evaluation Criteria
Some important points to be kept in mind while evaluating an ERP software include: 1) Functional fit with the Company's business processes 2) Degree of integration between the various components of the ERP system 3) Flexibility and scalability 4) Complexity; user friendliness 5) Quick implementation; shortened ROI period 6) Ability to support multi-site planning and control 7) Technology; client/server capabilities, database independence, security 8) Availability of regular upgrades 9) Amount of customization required 10) Local support infrastructure 11) Availability of reference sites 12) Total costs, including cost of license, training, implementation, maintenance, customization and hardware requirements.

ERP Systems -- Implementation
The success of an ERP solution depends on how quick the benefits can be reaped from it. This necessitates rapid implementations which lead to shortened ROI periods. Traditional approach to implementation has been to carry out a Business Process Re-engineering exercise and define a ``TO BE'' model before the ERP system implementation. This led to mismatches between the proposed model and the ERP functionality, the consequence of which was customizations, extended implementation time frames, higher costs and loss of user confidence.

The BAAN approach is to conduct a concurrent Business Process Re-engineering during the ERP implementation and aim to shorten the total implementation time frame. Two scenario's can be distinguished:
1) Comprehensive Implementation Scenario: Here the focus is more on business improvement than on technical improvement during the implementation. This approach is suitable when: Improvements in business processes are required. Customizations are necessary Different alternative strategies need to be evaluated High level of integration with other systems are required Multiple Sites have to be implemented.
2) Compact Implementation Scenario: Here the focus is on technical migration during the implementation with enhanced business improvements coming at a later stage. This approach is suitable when; Improvements in business processes are not required immediately Change-minded organization with firm decision making process Company operating according to common business practices. Single site has to be implemented.

ERP Systems - The Future
The Internet represents the next major technology enabler which allows rapid supply chain management between multiple operations and trading partners. Most ERP systems are enhancing their products to become ``Internet Enabled'' so that customers worldwide can have direct to the supplier's ERP system. ERP systems are building in the Workflow Management functionally which provides a mechanism to manage and control the flow of work by monitoring logistic aspects like workload, capacity, throughout times, work queue lengths and processing times.

Recognizing the need to go beyond the MRP-II and ERP vendors are busy adding to their product portfolio. BAAN for example has already introduced concepts like IRP (Intelligence Resource Planning), MRP-III (Money Resources Planning) and has acquired companies for strategic technologies like Visual Product configuration, Product Data Management and Finite Scheduling.

ARTICLE # 2ERP Process Definition :

Integrate various aspects of manufacturing operations, so as to improve quality and uniformity, minimize cycle times, and effort, and thus reduce labor costs.

Improve productivity by reducing manufacturing costs through better control of production.

Reduce human involvement, boredom, and possibilities of human error.

Reduce workplace damage caused by manual handling of parts.                       

Economize on floor space in the manufacturing plant by arranging machines, material movements, and related equipment more efficiently.

Consideration Of Products, Market Conditions And Trends: K.R Industries is a manufacturing industry and has products ranging from helmets, bike parts, seats, bowling bags, exercise mats and tire covers and custom made goods. It is a small-scale industry with an approximate 15 million in sales turnover. Their primary customers are K-Mart, Wall Mart, and similar super stores. Besides they also take contracts from various sporting goods stores all over the country. They have a small but fast growing market overseas. Entrepreneurs Bob Handelman and Stuart Wolkoff founded the company in 1970. The sales force consists of direct sales representatives operating from the company and agents all over the country. K.R has annual market growth of approximately 10 % every year. The organization is in the process of adding more to its product line. The organization aims to be amongst top players in its segment by year 2005. It is currently exploring new markets overseas, mainly Europe and Southeast Asia.  

Need for Reengineering: K.R Industry had its   organizational structure dictated by the market demands. Prior to Dec 1996 there was no IT/MIS department. Dependence on computers was minimal. According to the vice president of the company Mr. Phil Bishaf the organization was loaded with paperwork. They were handling hundreds of important paperwork everyday and the number was rising with the passage of each day. The paper work involved important documents like bill of lading, purchase orders, inventory reports, and finance reports, sales invoices, and credit memos. The company had a UNIX based system for ORDER ENTRY (called PILOT SYSTEM) but it could not handle other work. Most had to be done manually and it consumed lot of time and was highly error prone. This also led to significant rise in staff and hence the costs. Many a times there was a delay in order to processing cycle, which caused frustration amongst customers. At times the customer would want the products to be delivered at a very short notice and due to large time consumption in paper work, the company had to refuse the customer. Many organizations had their own format for documents and lot of time was taken to convert the information by the EDI department, which was still in its formation stage. K.R’s competitors were changing to new technology and the customers demanded information that matched their system. (E.g. various bar codes, inventory form/report format). Also as K.R expanded the managers had to develop reports on daily, weekly, monthly, quarterly and yearly basis and it took significant amount of time to do it manually and often the data would get lost or misplaced. K.R had to either boost up its IT technology or lose business to competitors. Thus the dynamic changes in the market environment forced K.R to think in terms of adopting an IT technology that could solve its problems and help it meet the demand of the growing market. K.R decided to automate all its processes using efficient computerized systems. The EDI department and the MIS department were formed. It was decided that they go in for the Client Server technology, which will divide the applications between clients and servers. Hence clearly the Client server was the emerging technology at K.R. Industries.It would help in:    

Migration to ERP and Its Role in Supporting   Organization Performance (with numerous examples): In case of hardware the choice was that a high-end server be used as a database server. The application was to be on this server from where all the clients can access. But K.R also wanted to install software that would help automate its manufacturing, finance and sales and inventory processes. So it decided to go in for an ERP package which would be outsourced. After intensive market survey K.R decided to go for an ERP package called MSS for Objects provided by the Fourth Shift Corporation. They provided the hardware along with the software and had good service support. More than 3,400 manufacturers around the world use Manufacturing System Software (MSS) For OBJECTS to manage information and run operations more efficiently. Smaller manufacturers believe Fourth Shift is the safe choice because it is an affordable, quickly implemented turnkey system. MSS for OBJECTS is implemented in three to six months. The industry average is seven to 12 months. Fast growing, mid-size manufacturers say MSS for OBJECTS scalability; deep functionality and integration with legacy systems help them stay ahead of the competition. Enterprise solutions include: 

Easy to use, yet ready for power users. Leverages an award-winning on-line help system and intuitive task-driven system navigation. Integrates seamlessly with third party applications. Runs with Microsoft Office and interfaces with industry standard financial systems, including SAP and JD Edwards. Provides on-line supply chain visibility. WebPartner allows customers and suppliers to view critical information over the Internet using any browser. Delivers full functionality over the Internet. The NetUI thin client interface allows the Internet to extend he LAN as the system backbone. Dramatically enhances network performance and reduces operating costs. Solves Y2K issues. MSS for OBJECTS is Year 2000 Certified by the Information Technology Association of America.  

Hardware Features include:

Software Support by the ERP Package: The ERP package makes use of the TITANIUM Database. Its features include unmatched fault tolerance, on-line backup, small memory footprint, stored procedures, scalability from stand-alone Windows to large networks, BLOBs, user defined triggers, and support for up to one terabyte of data. It was available in both client/server and stand-alone versions, with support for all major platforms.                     

Titanium is scalable from DOS PCs with as little as 640k RAM to large UNIX multiprocessor systems. Titanium offers peak performance in situations where efficiency is essential. Performance differential increases dramatically with increase in hardware power.

 

The ERP software package includes:        

Other benefits include 32-bit graphical user interface, remote user access using modem, remote user access using Internet, local user access with a standard client, local user access using a thin client. Also ability for check and payment processing and module communication file processing. Batch processes can be set up which can be run automatically. It also has template browse feature, which allows browsing from one screen to another.

The Supply Chain (How ERP helps) :ERP helps in attaining the following :-

1. Segment customers based on service needs. Companies traditionally have grouped customers by industry, product, or trade channel and then provided the same level of service to everyone within a segment. Effective supply-chain management, by contrast, groups customers by distinct

Service needs--regardless of industry--and then tailors services to those particular segments.                    

2. Customize the logistics network. In designing their logistics network, companies need to focus intensely on the service requirements and profitability of the customer segments identified. The conventional approach of creating a "monolithic" logistics network runs counter to successful supply-chain management.

3. Listen to signals of market demand and plan accordingly. Sales and operations planning must span the entire chain to detect early warning signals of changing demand in ordering patterns, customer promotions, and so forth. This demand-intensive approach leads to more consistent forecasts and optimal resource allocation.                                               

4. Differentiate product closer to the customer. Companies today no longer can afford to stockpile inventory to compensate for possible forecasting errors. Instead, they need to postpone product differentiation in the manufacturing process closer to actual consumer demand.

5. Strategically manage the sources of supply. By working closely with their key suppliers to reduce the overall costs of owning materials and services, supply-chain management leaders enhance margins both for themselves and their suppliers. Beating multiple suppliers over the head for the lowest price is out.

6. Develop a supply-chain-wide technology strategy. As one of the cornerstones of successful supply-chain management, information technology must support multiple levels of decision making. It also should afford a clear view of the flow of products, services, and information.                   

 7. Adopt channel-spanning performance measures. Excellent supply-chain measurement systems do more than just monitor internal functions. They adopt measures that apply to every link in the supply chain. Importantly, these measurement systems embrace both service and financial metrics, such as each account's true profitability.

The principles are not easy to implement, because they run counter to ingrained functionally oriented thinking about how companies organize, operate, and serve customers. The organizations that do persevere and build a successful supply chain have proved convincingly that you can please customers and enjoy growth by doing so.                        

Conclusion: Hence a research was done as regards business process that is supported by ERP package at K.R industry.

Reference:                                        

Article #3

ERP Failure


Update: Failed ERP gamble haunts Hershey

By CRAIG STEDMAN
(October 29, 1999) A$112 million ERP project has blown up in the face of Hershey Foods Corp., which said Tuesday it's still struggling to fix order-processing problems that are hampering its ability to ship candy and other products to retailers.

Analysts and industry sources said the Hershey, Pa., manufacturer appears to have lost a gamble that it could install a wide swath of SAPAG's R/3 enterprise resource planning applications, plus companion packages from two other vendors, in a single rollout during one of its busiest shipping seasons.

The sources said Hershey squeezed what was originally expected to be a four-year project into just 30 months before going live with the ERPsystem in July, which is when retailers begin ordering big amounts of candy for back-to-school and Halloween sales.

But the company said in mid-September that it was having trouble pushing orders through the new system, resulting in shipment delays and deliveries of incomplete orders. When Hershey announced a 19% drop in third-quarter profits this week, CEO Kenneth Wolfe said the system fixes are taking longer than expected and are requiring more extensive changes (see story).

When the difficulties first came to light, Hershey expected to get shipments back to normal by the end of October. But during a conference call with financial analysts, Wolfe said he now doesn't expect that to happen until the end of the year, if not later. "This is a difficult bear that we have here," he said.

The company recently spent two days reviewing the new system. It developed a list of changes that need to be made to improve things such as the view of product inventories that end users get and the way information flows between different applications, Wolfe said. "But they need to be tested before we put them in, and we can't get that done [right away]," he added.

Hershey wouldn't specify whether the problems stem from its configuration of the system or the software itself, which also includes planning applications developed by Manugistics Group Inc. in Rockville, Md., and a pricing promotions package from Siebel Systems Inc. in San Mateo, Calif.

Tom Crawford, general manager of the consumer products business unit at SAP America Inc. in Newtown Square, Pa., said the company has consultants on-site at Hershey to help resolve problems. But no revisions of R/3 are in the works for Hershey, he added.

"There are really no software issues per se, in terms of bugs or fixes that need to be applied to make [R/3]work any differently than it is now," Crawford said. The SAP workers "are just making sure they're using the business processes [built in to the software]correctly."

Hershey's plants continue to churn out products such as Hershey's Kisses and the company's namesake candy bars, but the chocolates are piling up in warehouses instead of sitting on store shelves. Product inventories at the end of September were up 29% from last year's levels because of the order-processing problems, according to Hershey executives.

"They've missed Halloween, they're probably going to miss Christmas, and they might even start missing Easter," said William Leach, a financial analyst at Donaldson, Lufkin & Jenrette Inc. in New York.

Jim Shepherd, an analyst at AMR Research Inc. in Boston, said most companies install ERP systems in stages, especially when applications from multiple vendors are involved. But an all-at-once rollout is tempting because it's faster and potentially cheaper.

"People hate the idea of a phased deployment," Shepherd said. "But these systems tie together in very intricate ways, and things that work fine in testing can turn out to be a disaster [when you go live]."

At Hershey, the system problems will likely result in lost market share and could lead fed-up retailers to drop some of the company's products from their shelves, Wolfe said.

"Clearly, our customer relations have been strained," said Michael Pasquale, senior vice president of Hershey's confectionery and grocery operations. Some warehouses are doing better than others at shipping products, he added. But after missing the promised October fix date, Pasquale said, the company faced the prospect of having to tell many retailers that a quick improvement in delivery times isn't likely.

 

 

 

 

 

 

 

 

 

 

 

SUPPLY CHAIN MANAGEMENT 

The supply chain is now a supply community. Different "links" in the supply chain -- such as manufacturers, materials vendors, and retailers -- are using Web-enabled technologies to collaborate on an unprecedented scale. And, Net Markets -- industry-specific online communities where buyers and sellers meet -- promise even more dramatic changes. (For example, instead of relying on a single supplier for a critical product or service, your enterprise might accept bids from hundreds of suppliers.)

To succeed in tomorrow's e-marketplace, you need supply chain management (SCM) solutions that deliver many-to-many communications -- and the flexibility to manage rapid change. Moreover, your SCM solution must add value as part of an integrated enterprise, communicating seamlessly with ERP, CRM and e-business systems.

The shared value chain

The supply chain comprises a wide variety of processes, which include:

A tightly integrated supply chain combined with collaborative technologies becomes a shared value chain that delivers increased efficiency, reduced costs and greater customer satisfaction.

Extending SCM through "e-communities"

Internet technologies are enabling networks of suppliers, vendors, and distributors to dynamically interact within secure "e-communities" 24 x 7. Through customized extranets, Web application servers and groupware, community members can share sales forecasts, manage inventories, schedule labor, optimize deliveries, and improve productivity.

Connecting SCM to the global e-business

Today's value chain is inextricably connected with ERP, CRM and e-business. Enterprise Java Beans (EJBs), XML, MQSeries messaging and other connectivity tools, let businesses seamlessly integrate SCM with other business critical systems. The result is an agile enterprise that can respond rapidly to changing e-markets and new opportunities.

 

A good website on scm http://www.eil.utoronto.ca/profiles/rune/node5.html#SECTION00500000000000000000

Benefits of SCM

Companies implementing Supply Chain Management may realise benefits including:

Benefits of SCM (with use of Electronic Commerce)

By integrating suppliers and customers into the SCM strategic planning process the benefits include:

 

 

 

 

 

 

 

 

 

 

 

 

Selling Chain Management

Selling-chain management can be defined as an integrated strategy for achieving sales, involving the application of technology to all the activities in the order process, from the initial enquiry right through to the placing of the order. A business has to perform a variety of activities to acquire and fulfil orders, using information scattered through the organisation. Selling chain application software enables the business to integrate and streamline these activities.

The following goals for companies’ selling-chain application frameworks:

Selling-chain management, is essential in the current tough selling environment. Customers are becoming more sophisticated and discerning, product life cycles are reducing and competition is growing. Combined with the increasing complexity of pricing, promotions and commissions, sales organisations have the challenge of operating more quickly and more accurately, cutting costs and adding more value for the customer.

Businesses that have taken the plunge and become early adopters of selling-chain software have benefited from increasing sales and growing market share, resulting in a payback on investment in as short a period as six months. Kalakota and Robinson expect the selling-chain application market to take off in a spectacular way.

 

 

Case Study

How Cisco Mastered the Net

 

You think Amazon.com is big? The real money online is business-to-business sales, and the king of routers is writing the manual.

Maybe it was the Cisco golf tee that inspired Chris Sinton's big idea. Or was it the Cisco coffee mug? Or the Cisco baseball cap? Anyway, it wasn't the routers and switches and other networking gear that are the San Jose company's main stock-in-trade. After all, Sinton's specialty was distributing marketing materials to customers, everything from technical brochures to trinkets, all of which, in typical Cisco fashion, customers had to pay for. Yet on the moth wings of that big idea--selling a gamut of goods and services to other businesses over the Internet, which Cisco does more of now than anybody else--the company has been able to protect its impressive margins, allowing it to underwrite a stream of new products. And the market is cheering: In three years Cisco's value has swelled from $15 billion to $100 billion today.

Sinton was an unlikely apostle for the Internet. By his own admission, the Southern Oregon University marketing dropout is one of the lowest-tech people at Cisco. "I don't talk in acronyms," he confesses. But he does speak the language of customers. He knew they hated wasting manpower phoning and faxing in orders, and ordering hats and mugs from printed, out-of-date price lists. Just as e.Schwab was getting started, Sinton began selling his golf tees and brochures on the Internet. And the Net was telling him something: Demand for souvenirs more than quintupled, and ten times as many people requested technical material as before.

Meanwhile, managers from other corners of Cisco were discovering their own ingenious ways of exploiting the medium. These experiments weren't mandated by top management. Typically, a department head would set up a skunkworks, asking a couple of engineers, working on a shoestring, to see how far a daring idea might take them.

By interesting marketing people two levels above him, Sinton was granted 15 minutes to make his pitch at a meeting of senior executives. He praised these other experiments and urged the company to turn them into a whole new way of doing business. Cisco could save a hell of a lot of money and serve customers far better, he asserted, by E-selling not only $10 promotional headgear and technical-support services but even its precious $1.5 million routers.

Sinton's presentation roused the room. E-commerce quickly became a corporate crusade. Today, Cisco--which sells 80% of all Internet hardware--has turned experiments like Sinton's into the backbone of its business. Since mid-1996 it has moved 57% of its sales--or around $1.3 billion this quarter--in routers, switches, and other gear onto the Web. Its goal for next year is 80%. Though the e.Schwabs and other business-to-consumer sites get the spotlight, companies last year sold other companies around $8 billion of everything from computers to airplane parts--four times the total sales to consumers. Forrester Research predicts sales will rise to $17 billion this year and will mushroom to $327 billion by 2000. Cisco, along with pioneers like Boeing and Dell, is proving that business-to-business selling is E-commerce's killer app.

Sinton may have been the first to see how it all fit together and grab top management's attention. But Cisco's first Internet breakthrough--without which massive E-selling couldn't have happened--came in its Technical Assistance Center, which provides after-sales service. Networking gear is so complex that after a sale, customers are in continual contact with their supplier. Knowing that, they buy products as much for the quality of after-sales service as for the items' price or features.

By 1994 the Technical Assistance Center was facing a staffing crisis. Service engineers are highly trained and extremely scarce, yet at Cisco they spent most of their day fielding routine questions about minor malfunctions, leaving not enough time to deal with the really tough technical challenges. Customers even called them to order software. This created a company-strangling constraint: If Cisco couldn't hire new engineers fast enough or free up the ones it had, it would have to cut back sales of routers and switches.

The solution, thought Brad Wright, the center's head, was to automate all the routine stuff on the Internet and let buyers serve themselves. With the backing of Doug Allred, the head of all sales and support services, Wright started his own skunkworks, charging several of his engineers with developing programs that could answer queries online. The system would translate a network engineer's fuzzy inquiry ("Cannot connect to remote server'') into a standard description of a familiar problem; then it would provide, say, the four most likely explanations onscreen, allowing the engineer to avoid a wilderness of blind alleys. Within 90 days Wright's team had put the most frequent questions on the Web. It also set up a program that let customers choose and download software.

The reaction was astounding. Weary of playing phone tag with busy engineers from nine to five, customers flocked to the Internet for effective 24-hour service. Calls and faxes dwindled. Cisco's sales have jumped fourfold since 1995, while its engineering support staff has merely doubled, to 800. Without automated sales support, Cisco estimates it would need well over 1,000 additional engineers. Estimated savings: $75 million a year, plus another $250 million it keeps by distributing support software over the Internet rather than transferring it to disks and mailing them to customers. Not to mention the billions in sales the company might have forgone if it hadn't been able to find those 1,000 extra engineers.

In a different corner of the company, another adventurous manager was at work. In early 1995, Linda Thom Rosiak, the new head of customer service, was fretting over Cisco's ordering process. The "dissatisfier," as Rosiak puts it, was the time and manpower it took to push orders from Cisco's customers to its plants or suppliers.

The cause of the delays was the multitude of errors in the orders, which invariably arrived by fax. All of Cisco's products are custom-built. Each has about a dozen major elements, including memory, power supply, software, and cables. Cisco offers dozens of choices for each, but many of the combinations don't work together. For example, customers frequently chose insufficient memory to handle their choice of software. Making matters worse, customers found the prices for 13,000 parts in often out-of-date catalogs the thickness of phone books. When Cisco received orders with the wrong prices or configurations, which was 40% of the time, it simply faxed them back to the customers.

"It was a disaster," says Frank Santafemia, head of network installations for Sprint, which, like most of Cisco's customers, schedules delivery of the products that go into the giant networking systems that it builds and operates for its corporate clients. "We'd be faxing incorrect purchase orders back and forth. Meanwhile, we'd need that router to build the project. Those errors could delay jobs for weeks."

Putting the sales process on the Internet, Rosiak realized, would eliminate screwups. Customers could complete their projects much faster, and Cisco would avoid the cost of hiring an avalanche of new people to catch the errors.

So Rosiak helped start yet more skunkworks. A key collaborator was Cisco's chief information officer, Peter Solvik. "If we could leverage the Internet in technical support, I knew we could use it for every step in selling and servicing our products," he says. Solvik assigned Rosiak a couple of his own engineers to help design programs linking customers to Cisco. The first one, called Status Agent, let customers track the progress of their orders online. The next involved posting the prices of all Cisco products. The third was the most momentous of all--but that one was going to require the support of top management.

Around this time, a venture capital firm had introduced Solvik to Calico Technology, a tiny startup. Calico made software that enabled customers to unerringly select compatible parts not just for simple machines like PCs but also for big-ticket items like routers. At a conference in Monaco, Solvik asked CEO John Chambers for a few million dollars to buy Calico. He told Chambers he could make the key ingredient needed for Internet selling--online configuration of Cisco products--customer-friendly. Chambers, by now an E-commerce zealot, said yes.

Cisco offered routers and switches for the first time over its new business-to-business Website, Cisco Connection Online, two years ago. Customers quickly saw the advantage of picking out prices and configuring products electronically. They simply click onto a program called Configuration Agent, which walks them through the dozen major components that go into a router. If they choose the wrong combination of circuitboards, for example, the program posts an error message and guides them to an acceptable choice. Once the right item is selected, its current price pops up automatically.

Resellers are flocking to CCO because it lets them get their equipment and finish their jobs much more quickly. At Sprint it used to take 60 days from the signing of a contract to complete a networking project. Now, thanks partly to the efficiency of ordering Cisco equipment online, it takes 35 to 45 days. Sprint has also been able to cut its order-processing staff from 21 to six, allowing the other 15 employees to work instead on installing networks, a business that has doubled at Sprint since 1996.

Like its customers, Cisco is saving tons on order-processing workers. Rosiak has just 300 service agents handling all customer accounts; she says she would need 900 without the help of CCO. The difference represents another $20 million or so a year.

Is the Net bringing Cisco new business or just handling orders the company would have received anyway? Cisco's sales are growing by 30% a year, far more quickly than those of rivals like 3Com and Bay Networks. Resellers who used to spread their orders around are buying mainly from Cisco, thanks to the convenience and speed of CCO. Confirms Sprint's Santafemia: "We are doing more and more projects where Cisco is supplying all the equipment. It's just faster that way."

But E-commerce's biggest impact is on profits. At most high-tech companies, profitability shrinks as sales take off. That's because prices fall as new players enter the market. Though Cisco has not escaped that iron law, its Net operations have kept its selling and servicing costs--from personnel to printing to software distribution--far lower than its competitors'. All told, including savings harvested from manufacturing, the Internet is saving Cisco about $360 million a year and helping preserve its 32% operating margins.

Chris Sinton, the man who blazed the trail, is now a member of top management, overseeing the operation and design of CCO, his brainchild. He reflects: "I just knew the Net could be our business, that it could be a portal to our company." And to the history books, one might say. The visionary white paper he wrote in 1995 on the future of E-commerce is now preserved in the Smithsonian Institution archives. Not bad for a former souvenir salesman.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

e-Procurement

 

Key Benefits
Metiom ConnectTrade™ key benefits for e-Procurement include the ability to:

Key Advantages:

 

 

 

 

 

 

 

 

 

The Benefits of e-Procurement Solutions



Reduce "Total System Cost" for commodities

Reduce "Transaction Costs"

 

Improve internal controls

 

 

 

 

 

 

 

 

 

 

 

Three e-Procurement Implementation Strategies



Postpone implementation

 

Trial implementation


Global implementation

 

The Seven C's of E-Procurement

by Steve Hornyak and Todd Ostrander

Why seven catalog management techniques should be integrated for advanced business-to-business online buying solutions.

Enterprise-wide electronic procure-to-pay solutions truly deliver on the promise of the Internet to provide streamlined solutions for business. Web technology now provides a way to purchase critical goods and services at preferred pricing rates and put strategic buying power in the hands of front-line employees. By granting end users freedom to choose among a subset of pre-approved and budgeted items and allowing management to maintain control over the process and supplier relationships, company executives can now track immediate results and positively impact the bottom line.

Deployment of electronic procurement systems is rapidly accelerating in 1999 and beyond; but in evaluating the choices, buyers need to be wary of the long-term needs of their entire employee base. One of the most misunderstood components of e-procurement is catalog management, the process of consolidating and presenting goods and services offered for online purchase. Early adopters of online ordering primarily opt for employees to shop in virtual megastores such as Office Max or Staples. This approach solves part of the procurement challenge but does not meet the needs of all purchasing activities or provide for pre-purchase routing and approval. For example, nobody wants to sacrifice patronizing less Web-savvy local businesses such as Joe's Corner Catering or Sue's Mid-City Flower Shoppe that have been used for years. Nor do they want to miss customization features such as the "build-your own-computer" option offered by Dell or the prospect of purchasing the wares of specialty companies when desired.

Given the wide array of suppliers, companies offering Web-based buying solutions to their employees must accommodate a variety of content choices. Whether ordering gift-wrapped Bavarian chocolates for clients or purchasing standard-issue headsets for the telemarketing department, employees are best served by a procurement portal that offers simultaneous support for all catalog management approaches. To truly impact the bottom line for executive officers and front-line employees alike, an ideal e-procurement application must navigate all "seven C's" of catalog management.

Business Requirements for Catalog-Based E-Procurement
For enterprise-wide e-commerce procurement to effectively eliminate maverick buying (when employees circumvent corporate purchasing policies by buying materials outside of authorized channels-at retail prices-from non-contracted suppliers) and reduce the cycle time associated with the procure-to-pay process, the solution must follow a set of key rules:

Offer simple ways to find products.
An e-procurement solution must make it easy for an end-user to search for products, without knowing the name, line-item or product code of contracted suppliers. The user must feel as if he or she is shopping-not entering a requisition as typically required by ERP vendors. Through an intuitive graphical interface, someone can type in the key word "paper" and get categories for all sorts of paper, from laser-printing paper to paper towels, as well as items related to paper, such as printers, shredders and notepads. Better yet, a user can click to a supplier's Web site to obtain rich content with illustrations and detailed descriptions of the product he or she seeks.

Facilitate event-based purchasing.
Employees must be able to purchase across the supplier base in accordance with routine events. For instance, a "new employee kit" example can be populated with a requisition file for installing modem and phone services, buying new office equipment and ordering customized business cards. For recurring events such as a training workshop for which event-based online procurement captures necessities such as manuals, flip charts, overhead markers, catering and facilities set-up in one location for easy access. Such "events" must seamlessly transcend supplier and catalog boundaries.

Engage suppliers in the process.
Some solutions aggregate entire supplier catalogs of goods and services, including data and multimedia content, so suppliers compete on price alone. This methodology could result in adversarial supplier relationships and service levels, neither of which is good for your company. An advanced e-procurement solution, on the other hand, lets suppliers control dynamically changing information regarding issues such as limited-time pricing promotions ("buy a computer workstation this month and get a free scanner"). This approach enables both the person doing the selling and the person doing the buying to carry out the precise transaction desired, creating a win-win relationship.

Follow the 30-second rule.
In the whirlwind of an average workday, users want the e-procurement system to be fast: A standard transaction should be accomplished in half a minute. Users don't want to go through a shopping experience for each product needed or to go to the Internet every time they make a purchase; consequently, today's best e-procurement systems keep vital information bookmarked for easily-accessible lists of previously ordered goods as well as frequently ordered items.

Provide easy administration and maintenance.
An effective e-procurement system is not only easy for the end user to accomplish objectives, but it is also simple to administer in the procurement organization. It offers procurement professionals "control without hassle" through auditing functionality and a visual graphical point-and-click interface linked directly to back office systems. In addition, business rules management, catalog index updates and management intelligence should be easy for the procurement organization and require little to no effort on the part of the IT organization.

Advanced e-procurement systems are using new technologies to represent data to end users. These features include technologies such as XML (extensible mark-up language) to tag elements of data, allowing Web crawlers to push or pull information to the system proactively rather than reactively. Turnkey tools can even capture data from supplier Web sites, create maps of the sites and pull information into a product index with no human intervention. With these powerful tools aiding the process, managing catalogs can be a simple process.

Complete Catalog Management System for E-Procurement
Each of the "seven C's" of catalog management offers distinct advantages for the purchasing of certain types of goods and services. These different attributes will help a company streamline a comprehensive procure-to-pay solution. By themselves, each method solves specific needs of specific users but never the total solution. In varying combinations, these methods of catalog management can meet all of a company's operational purchasing needs.

Custom Catalog
The first approach to catalog management is the customized approach in which 100 percent of content is developed and stored in-house at a company. This approach was developed before the advent of Web technology but is still required to meet the needs of many suppliers. Since content changes often, however, this approach used exclusively can place a burden on suppliers. In addition, content based on old technologies such as formatted disks or tapes cannot be dynamically updated. But this approach often helps organizations maintain internal catalogs for services such as internal facilities or central catering. The custom approach is good for internal suppliers, local suppliers and those suppliers that can readily feed a company electronic data.

Connecting to Supplier Web Sites
Linking to a supplier's Web site is a second way to handle catalog management. Using this approach, operations management must keep every employee informed about who the contracted suppliers are and the appropriate use of each. Because this approach requires extensive time on the Internet and does not integrate with internal business back office systems, smart companies will avoid using this as the only option for cataloguing. This technique does not allow for event-based purchasing and dramatically increases the time required to process an order for goods that transcend a single supplier.

Cooperative OBI
OBI, or Open Buying Over the Internet, is another transaction methodology. OBI allows users to trigger a shopping basket-style transaction on a supplier's Web site, transfers the basket to the buyer's system allowing approval routing prior to the issuing of a purchase order. This approach is most effective for purchases like customizing a Dell computer. But it is not applicable for all transactions, particularly non-dynamic products; and it doesn't allow for event-based purchasing. To date, very few suppliers have actually adopted OBI for standard ordering interchange.

Content Aggregation
Content aggregation is the behind-the-scenes process of gathering data from multiple suppliers and normalizing it for consistency. It rationalizes and normalizes data the way Yahoo! and Alta Vista do for consumer Web sites. When you type a vertical market-specific requirement such as "chemical supplies," the content aggregator seeks the highest probability of the word "chemical supplies" as a product name and accesses all items associated with "chemical supplies" that an end-user may be seeking. This is a vital but expensive component of any catalog management program, and some level of normalization is required for any system. However, the degree to which this is done varies based on the category of product being requested. Commodity items are much more commonly defined than specialty and industry-specific items.

Contingency Plan
Then there's a contingency program for non-catalog items that typically account for five to 15 percent of all company purchases. This approach is used for rare or specialty items not included in online catalogs. These include corporate gifts, premium or one-of-a-kind novelty items for special events. Think ice sculptures. Think custom embroidered company logo jackets. Or even holiday cards to mail to customers. This contingency technique delivers end users a "free form" in which they make a request to a buyer-the corporate purchasing agent-who then procures the product and can add the vendor to the system, if applicable.

Compiling Proactively
A proactive approach uses leading-edge Web technology to obtain data based on authorized access to supplier Web sites and/or accessible supplier databases. To reduce the burden on suppliers, this technology invisibly gathers information based on embedded data, cases, assumptions and/or XML. Today a system can proactively compile catalog data from the Office Depot site on behalf of the Florida Board of Tourism and pump useful information directly into the organization's catalog index. The information can be updated based on an agent monitoring the site or updates based on a schedule determined by the customer.

Co-Op Approach
The co-op approach is a pure outsource model on which the e-procurement solution managers rely on third-party transaction providers, sometimes referred to as "supplier malls." Although this system allows some smaller companies without transactional Web sites of their own to band together, it is not a good fit for the megasuppliers who lose the one-to-one touch on which they bet their business. This approach is not yet proven as a standalone method: There's too much possibility for vendor backlash based on transaction fees imposed by the e-procurement or catalog provider and is subject to a proprietary link between buy-side software and the "supplier mall" in order to be efficient. This ultimately limits a customer from applying the other approaches discussed.

The analysts are weighing in as well. Recently, David Alschuler of the Aberdeen Group stated, "In order to support a comprehensive catalog of MRO goods and services, the I/P [Internet Protocol]-enabled buying applications need to support a heterogeneous set of catalog content management strategies which includes both replicated, standardized content and access to content maintained at supplier sites (using the OBI or XML standards)."

A Checklist for E-Procurement Success
E-procurement is fundamentally changing the buying process, allowing employees throughout the organization to order and receive operational supplies and services from their desktop. It significantly streamlines the traditional procurement process through extensive use of Web technology, resulting in cost savings, responsiveness and more strategic supplier relationships.

By applying the business requirements of e-procurement and the "seven C's" of catalog management to solutions selected for businesses today, companies can save like never before. Coupled with Web-based enterprise budgeting and planning, companies can use e-procurement to realize cost savings like never before. E-budgeting and e-procurement together can help the financial department plan directly for a system that significantly impacts the bottom line.

No single catalog management model will meet 100 percent of any customer's needs. To maximize the value proposition of e-procurement, a company must support simultaneous catalog management approaches. With these appropriate components in place, a company can create a comprehensive e-procurement environment for all employees.

Support suppliers large and small.
An effective e-procurement system allows five types of suppliers, regardless of size or technical ability, to participate. Potential suppliers to an e-procurement solution include the following:

1. Companies already conducting e-commerce business with transactional Web sites (BT Office, for example).
2. Suppliers with a Web site for marketing purposes but not for taking orders.
3. Suppliers without a Web site who can still support EDI and/or CD-Rom catalogs (Often these are regional or industry-specific suppliers that market to niche companies, not to the masses via the Web.).
4. Merchants without a Web site (i.e. a local landscaper or a cleaning service).
5. Internal company suppliers (i.e. an "internal company store," central catering or facility requests).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Decision Support Systems

 

A precise definition of decision support systems is hard to find. Ranging from artificial intelligence to executive information systems, it means different things to different people at different times. This is especially true with applications moving over time from mainframes to workstations and costs lowering the 'barriers to entry'. Complicating matters, there are also different types: communications, group, data, document, knowledge,and model-driven decision support systems.

Perhaps a safe starting definition is put forth by El-Najdawi and Stylainou (1)(1993) where "DSS is a computer-based system that can help decision makers address semi-structural problems by allowing them to access and use data and analytic models."

'Semi-structural' used here refers to problems containing some elements that are well defined and quantifiable and some that are not so well defined and must be considered subjectively. This is where, if management is part science and part art, DSS blends the two.

 

Decision Support Systems

1. Introduction

1.1 Concept and definition

The field of decision support systems (DSS) is considered as a third generation of computer-based applications, after transaction processing(TPS) or 'efficiency' systems, and management information systems (MIS) or management reporting systems. DSS evolved from the concept proposed by Gorry and Scott Morton in 1971 [1], who explored the concept of structure in decision making by constructing a matrix showing the interaction between the level of management and the decision making structure at each level. With increasing levels of management, the decision making process becomes less structured. This situation consequently brought about the need for tools and technology, unlike the standard management science models which were more suited to structured decision making. Thus, over the next 25 years, the concept of DSS was formalized into tools and technology derived from the MIS and Operations Research/Management Science fields. The research area has now expanded to include computer science, cognitive psychology, decision sciences and engineering.

The definition of DSS that prevails today was articulated in a conceptual framework paper by Sprague in 1980 [2]. DSS is defined as computer-based systems that aid or support decision makers solve unstructured problems through direct interaction with data and analytical models. A good working definition of DSS is an interactive, flexible and adaptable computer-based information system that utilizes decision rules, models, and model base coupled with a comprehensive database and the decision maker's own insights, leading to specific, implementable decisions in solving problems. The DSS supports complex decision making and increases its effectiveness.[3]

1.2 The need for DSS

In the frenetic, fast changing world of information systems (IS), it is sometimes hard to stay focused on the true objective of the mission of IS within the organizations. It maybe be true that the true objective would be to enlarge the power base of the IS department, enhance the reputation of certain academic and researchers, or increase the market share of IS vendors. These, in actual fact, maybe the by-products of the ultimate objective i.e. the need to increase the performance of information or knowledge workers in organizations through the application of information technology. The level of performance or productivity can be directly attributable to the type of managerial functions carried out - in relation to planning, organization, directing and controlling. Most managerial activities revolve around decision making and this typically runs throughout the entire organizational structure.

Throughout much of the early 1970s, decision making was still considered a pure art - learnt by trial and error through experience. Much of the management styles were based on creativity, judgment, intuition and experience, rather than systematic quantitative methods based on a scientific approach. The trend in businesses and the environment are more complex today. Many of the factors that affect managerial decision making such as technology, organizational size, structural complexity, global competition and markets, consumerism and government intervention, are all increasing. These factors indirectly and directly increase the complexity of decision making by increasing the number of alternatives (through technology and communication systems); increase the difficulty to predict future consequences due to increased uncertainty and the enormous costs of making errors due to the complexity and operations, automation, and the chain reaction that an error may cause throughout the organization. Hence, the need for tools and technology that are not only quantitative in nature, but also effective and timely.

1.3 Scope of the paper

The paper will discuss the impact of DSS to management, the roles it has taken and the evolution of DSS into the sub areas of expert systems, executive information systems, group DSS and expert database systems. Each of the specializations will be briefly detailed to show the research emphasis and current applications.

Although the field of DSS has completely evolved into distinct specializations, the basic underlying components and principle tenet of supporting management decision making, are closely followed. The paper will begin with the research links to relevant areas, especially artificial intelligence (AI). The next section will discuss the evolution of DSS and detail the relevant DSS sub-areas. The second section will highlight the current areas of focus and discuss some of the limitations that have become evident.

2. Research Links

The growth in artificial intelligence (AI) literature in turn has changed the understanding of DSS tremendously. The main emphasis in AI is on nonprocedurality and on natural language processing. This nonprocedurality can be achieved by developing knowledge-based DSS. [16] Application of AI is one of the major activities in the current research . Building knowledge-based DSS has its own challenges. Main issue here is that of representing knowledge. This can be represented in layers - lower levels define the syntax and higher levels define the semantics. This representation helps in sharing of information among different parts of the organization. The problem of seeking the basic semantic foundations upon which managerial knowledge can be constructed can be addressed by software and information engineering. Information engineering can be guided by modern and classical philosophy. To represent modeling knowledge, it is possible to use first order logic.

DSS can be developed using the tools which are used to build expert systems, but this involves a narrow domain of knowledge, since it is anticipated that only the expert knowledge is captured. A number of recent DSS are developed using expert systems ideas and this has led to a confusion of literature between DSS and expert systems. Although their construction is similar, there is difference in the nature of the problem and the type of user who operates the system. Basically, the managerial user relies on the DSS, whereas in theory, any user can use the expert system to perform the function of an expert. DSS is also used for open ended problems (unstructured decision making) and expert users, unlike users of expert systems who are only restricted by the specific domain of knowledge. The problem of uncertainty in the realm of DSS, is another major research area. This can be handled by probabilistic, fuzzy approaches or even approximate reasoning.

Other relevant research areas include the integration of simulation and AI. The qualitative approaches including 'creativity', to handle the open-ended problems. Developing interactive and intelligent DSS for solving multi-objective (or multi-criteria) decision making is another area of active DSS research.

2.1 DSS Research Focus

A recent study by Teng and Galetta [4] found that nearly one third of the researchers in MIS were doing DSS research. Within the DSS area itself, a view of the intellectual structure and contribution by DSS researchers, showed that two major contributing disciplines accounted for substantial research volume. Management science / operations research and multi-criteria decision making were the major interest. The other contributing areas were: fundamental theory, frameworks and concepts of DSS; group DSS; routing DSS; data base and model base management systems; multi-criteria DSS and marketing DSS. [5]

2.2 DSS Research Subtypes

From the general definition of DSS, several subtypes of DSS have been derived as a result. Many DSS researchers and practitioners point out that most major decisions are made collectively. However, most minor decisions are made by individuals. Group decision making, referred to in the context under the subtype of group decision support systems (GDSS) are decision(s) made by a group of decision makers as a joint action. The term Executive Information Systems (EIS) or Executive Support Systems (ESS) are DSS targeted to the top executives in the organization. The focus on these systems is rapid access to timely information and direct access to management reports. Expert systems (ES), a branch of artificial intelligence, was developed in the early mid-60s. The reasoning during this period was that with few laws of reasoning and powerful computers one could produce an expert capable of superhuman performance.

3. Decision Support Systems

3.1. Alternative Definitions and Previous Research of DSS

Moore and Chang [7] found that the structure of decision making, as proposed by Scott and Morton, would be meaningful only to the individual decision maker, and not to all decision makers. DSS is defined as extendable systems capable of supporting ad hoc data analysis and decision modeling, oriented towards future planning and used at irregular, unplanned intervals.

Bonczek [8] defines a DSS as a system with three interacting components: a language system providing a means of communication between the user and other components; a knowledge system containing the domain knowledge, in the form of data, rules or procedures, and a problem processing system containing the general problem manipulation capabilities required for decision making.

Keen [9] talks about a DSS being able to be developed through the adaptive process of learning and evolution. The DSS is defined as the final product of a development process in which the DSS user, the DSS builder and the DSS itself are all capable of influencing one another, resulting in an evolution of the system and the pattern of its use.

From these definitions, each research tends to limit the population of systems towards those that would fall within the respective definition boundary. Conversely, Keen would omit systems built without an evolutionary strategy, and Moore and Chang would also exclude systems that incorporated automatic and routinized use. The underlying theme among these definitions is the focus on the input side of DSS. This rationale may have come about because of the difficulty of measuring DSS outputs i.e. decision quality. However the central concept of DSS should focus on support and effectiveness of decision making.

3.2 Characteristics of a DSS

Basically the concept of DSS should support several ideas:-

The principle components of a DSS consist of: data manager - a database management system; model manager - analytical tool base providing management science / operations research techniques, and, a dialog manager. [3, 11, 12] A conceptual model is given in Figure 1. The data manager handles both the internal (probably transaction data) and external data (external databases - industry data, marketing research data). In addition to the basic storage, retrieval, control and data dictionary functions, the data manager also handles query processing. The model manager is composed of four basic components: the model base which stores the analytical model sets; the model base management system which creates models, allows model manipulation, interrelate models with appropriate linkages through the database, and performs the basic database management function of storage, access, execution, cataloging and querying. The dialog manager is the software and hardware that provides the user interface for the DSS, dealing with concepts of ease of use, accessibility and human-machine interactions. More often than not, this function will determine the success or failure of a DSS [11].

3.3 DSS Example - Vehicle Routing

The FleetManager, a PC-based DSS addresses the milk tanker routing scenario in the New Zealand dairy industry. This system builds schedules using experience and preference of the schedulers and also for strategic planning. The DSS uses multiple, resizable, overlapping windows to assist schedulers in their tasks. User interaction is through a road map of the area and the location of the milk processing plants and milk suppliers. There is an option for automated or modifiable vehicle routes, and 'what-if' scenarios with potential cost/benefit analysis. [10, p 139-156]

The traditional algorithm for solving vehicle routing problems models the problems as mixed integer programs to be solved by advanced mathematical programming. Dairy vehicle schedulers are often faced with problems which cannot be represented by a single well-defined optimality criterion, together with families of well-defined constraints, expressed in mathematical form. These decisions are not only based on objective data points and formulas which can be optimized, but also subjective judgments. Cost might be only a secondary consideration, whereas the primary objective might to produce a schedule based on timing considerations. Other factors such as customer satisfaction, road inclinations, vehicle accidents and breakdowns, tanker queuing, company and customer policies, etc., need also to be considered. The problem then is a multitude of ill-defined objectives and constraints with differing priorities, which in turn makes it difficult computationally. Available software packages ignore talents of experienced schedulers and minimize on the manual 'tweaking' facilities. Hence the FleetManager incorporates both the qualitative and quantitative aspects to scheduling. The structure of the FleetManager is given in Figure 2.

3.4 Group DSS

Drawing from the component of a generic DSS, a group DSS (GDSS), will comprise a group of decision makers who have access to a database, a model base and appropriate GDSS application software during the course of a decision-related meeting. Basically the minimal hardware requirements would be workstations setup in a LAN (local area network) configuration. The server acts as the facilitator. In addition to the individual workstations, there would be a large wide screen to display the facilitator's screen output. The GDSS controlling software would allow group and individual work. Among the key group features would be numerical and graphical summarizations of group members, ideas and votes. There would also be programs for calculation of weights for decision alternatives; anonymous recording of ideas; progressive rounds towards consensus building or elimination of redundant input during brainstorming. [13]

Based on the framework proposed by DeSanctis and Gallupe [14], the purpose and configuration of a GDSS depends upon the length or duration of the decision related meeting and the degree of physical proximity among group members. Variations range from the decision room - equivalent to a traditional meeting; local decision network - a LAN-like system connecting decision makers in the same room or in the same city; teleconferencing or videoconferencing - where the group members are geographically separated from each other and there is communication between two or more decision rooms, and, remote decision making - which offers uninterrupted communication between remote decision stations, thus removing the constraint of a single meeting location and addresses the needs of decision makers who meet 'remotely' to make joint decisions.

3.5 Expert Systems

The name expert systems was derived from the term 'knowledge-based expert systems'. An expert system is used to preserve and disseminate scarce expertise by encoding the relevant experience of an expert and making the expertise available as a resource to the experienced user. Ultimately, such systems could function better than any single human expert in making judgments in a specific, usually narrow expertise area (called the domain).

In general an expert system comprises the user interface, which allows the system to communicate with the user; a knowledge base of problem related facts and rules, and a set of reasoning methods or an inference engine. Figure 3 represents an expert system architecture The knowledge base comprises of a body of knowledge which has been encoded by the knowledge engineers, for use by the software. Three of the most important techniques for encoding this knowledge are production rules, semantic networks and frames.

3.5.1 Knowledge representation

Knowledge can be organized in one or more configurations (or schemes). This is analogous to a database that can be organized as relational, hierarchical or network. These knowledge representation schemes can be programmed with existing computer languages and stored in memory, and, are designed so that the facts and other knowledge contained within them can be used for reasoning. So, in actual fact, the knowledge base contains a data structure that allows manipulation by an inference engine using search and pattern-matching techniques on the knowledge base to answer questions and draw conclusions. Knowledge representation schemes can be categorized as declarative or procedural. Declarative schemes represents facts and assertions - such as semantic nets and frames. Procedural schemes deals with actions and procedures - includes procedures or subroutines and rules.

3.5.2 Production Rules

Production rules are use for building systems based on heuristic methods (rules of thumb). These simple if-then rules represent the empirical consequence of a given condition or the action that should be taken in a given situation. For example,

IF:

(1) the infection is meningitis

(2) only circumstantial evidence is available

(3) the type of infection is bacterial

(4) the patient is receiving corticosteroids

THEN: There is evidence that the organisms involved are

E.coli (0.4)

Klebsiella pneumoniae (0.2)

Pseudomonas aeruginosa (0.1) .

[Source a simplified version of MYCIN rule no. 543]

3.5.3 Semantic networks

Semantic networks consist of nodes and links. Nodes describe facts like physical objects, concepts or situations, whereas links (or arcs)define the relevant relationships among the facts. Each node may point to a subnode, representing more detailed levels. A common relationship is the 'is-a' link which allows facts to be attached to classes of objects and then inherited by specific objects in the class. This concept establishes a property of inheritance hierarchy in the network. Semantic nets are used to represent non-rule knowledge and the notation is based on associations between concepts. For example, the diagram below shows the property inheritance - since tweety is a bird, and the bird has feathers, then tweety has feathers.

Semantic network diagram

3.5.4 Frames

These are data structures that include all the knowledge about an object. The knowledge is organized in a special hierarchical structure that permits a diagnosis of independence. Frames are basically an application of object-oriented programming for artificial intelligence and expert systems. Each frame represents one object and arranged in a hierarchy. Frames at the bottom represent instances. The frame permits inheritance, usually the lower level frame inherits all the characteristics of related frames of higher levels. These characteristics are expressed in the internal structure of the frame. A frame consists of two basic elements. The slot represents a set of attributes describing the object. Each slot can contain one or more facets or subslots. The facets represent some knowledge or procedures about the attribute in the slot. Facets can comprise of values, default values, ranges of numbers, IF added - action taken when slot is added, IF needed - when no slot value is given, it triggers an IF added operation, and others containing in turn other frames, rules, semantic networks or any type of information. Figure 4 shows an example of a hierarchy of frames.

3.5.5 Example Expert Systems - Maritime Pilot

The need for safe navigation of marine vessels in congested waters is critical especially where in a few moments of in attention can result in a casualty. Normally, most vessels navigate with three people on or about the bridge. Single-handed bridges are also being introduced by most operators ,as long as there is some automated equipment. The introduction of bridge automation can be effective in decreasing shipboard stress and fatigue, by removing the noncritical monitoring task of the decision maker. [24]

This system is a decision aid for cognitive skills of piloting, maneuvering and collision avoidance, and practice of good seamanship. The piloting of a ship involves :-

The piloting expert system (PES) was developed using a Symbolics machine with Knowledge Engineering Environment (KEE). This ES shell uses both frame and rule-based representation schemes. The KEE frames stored the objects of the knowledge base (ships, channels, lighthouse, etc) and propagates reasoning about these objects. For example, a generic class can be subdivided into subclasses, and hence all attributes of the superclass are inherited by the subclass.

The PES knowledge base contains procedures and heuristics whenever the vessel encounters changing conditions. Parameters can be altered due to emergencies, changing conditions; there is the ability to conduct baseline simulation exercises and 'what-if' analysis. The problem facing these maritime pilots can be termed as 'situation assessment' : the gathering of new data and reasons about its implication. Reasoning is carried by moving 'forward' from top-to-bottom, through the hierarchical rule structure.

3.6 Executive Information Systems

Traditionally the major users of DSS have been professionals and middle level managers. Institutional DSS (enterprise-wide DSS installed in specific areas) support mainly planners, analysts and researchers. DSS for top executives, termed executive support systems (ESS) or executive information systems (EIS). This new technology began emerging in response to Rockart and Treacy's classic article in 1982 concerning the use of computer-based information among CEOs (chief executive officers). Although there have been different survey results purporting that DSS are used by very few top executives, a study released in 1986 by MIT's center for information systems research indicated that a third of large U.S. corporations had planned or had installed EIS programs [3]. By 1989, this figure had increased to 50% . Companies have reported significant changes in their organizations through the use of ESS (integrated support system incorporating the EIS). These include; improved communications at all levels of the organization; improved focus on important business goals; shortening of decision-making cycle-time, and flattening of the organizational hierarchy. [22]

EIS can be defined as a computer-based system that handles queries from top management. It gives rapid access to timely information and direct access to management reports. Exception reporting and 'drill down' capabilities are enhanced by user-friendly features and excellent graphical interface. Connections to e-mail and on-line information services are also common features. EIS focuses on the present, usually presenting the executive with information within the budgeting time frame of the organization. It is exclusively a display technology, oriented towards presenting static report and graphs on demand. It offers little or no analytical capabilities to help explain, diagnose and understand information. Forecasting and planning facilities are usually lacking, such that changing external conditions are not factored into the information generated.

An ESS, on the other hand, is a comprehensive support system that includes the elements of an EIS, communications office automation, analysis support and intelligence.

Consequently, the characteristics of an ESS should include: -

3.6.1 Example - Executive Information Systems - Executive Edge

The Executive Edge (from Execucom Systems Corp., Austin, Texas) is a good example of an executive support system. Figure 5 shows that the Executive Edge includes Execucom's DSS generator, IFPS/Plus (Interactive Financial Planing System) and VantagePoint, a PC software tool. IFPS/Plus provides DBMS facilities for storing ESS-specific information and some unique executive-oriented analysis capabilities. VantagePoint can front-end any interactive application on a remote workstation. It provides a high-level of ease of use and open-ended ability to incorporate any computer-based information or functionality into the executive support application. [3, p. 365-387]

The Executive Edge, equipped with AI tools, can answer 'drill-down 'questions, addresses the needs of large organizations, and its open-ended architecture allows linking/integration with existing computer-based information and operational systems. The full featured DSS capability will support management support and information applications and analyst-oriented decision support applications.

3.7 Expert Database Systems

The field of expert database systems (EDS) is an important area for the research and development of intelligent database systems. EDS combine database and expert systems technologies to support the effective management of both rules and data. EDS is linked to the tools and techniques of fields like AI, database management, logic and logic programming, information retrieval and fuzzy systems theory. [17]

There are several ways in which knowledge and expertise can be introduced into database management systems. For example, knowledge representation techniques can enhance data models by increasing semantic modeling capabilities such as inheritance hierarchies and object-oriented structures and functions. The semantics of an application may be specified as an evolving set of rules, rather than being hard-coded in host-language application programs which are more difficult to maintain. Hence, a knowledge-based system might use knowledge and data on secondary storage.

Expertise can be internal or external. Internal expertise are system specific knowledge concerning meta-data (data about data), contained in the data dictionary, integrity constraints, query processing schemes, indexing and storage strategies. Such knowledge can be used to make databases intelligent and to manage knowledge-based systems.

Two factors determine EDS architecture choice - application and performance requirements. Some applications will access existing, large databases, while others will be knowledge intensive and access small databases. Architectures include : either a loosely or tightly-coupled expert system and database system; a database system with reasoning capabilities to support knowledge-based systems; an AI or logic programming system augmented with both data access primitives and knowledge management functions; and, intelligent cooperative front-end interfaces that provide intelligent access to large data/ knowledge systems.

3.7.1 The Development of expert database systems

Rule management has been of major concern of expert systems, and can be found in expert system shells of OPS5, Prolog and KEE [18]. These rule systems had no interaction with DBMS systems. Much of the research in expert database systems (EDS) has been about finding techniques to integrate both rules and data and to ensure consistency. The understanding that the knowledge base can be integrated into the DBMS, will ensure consistency and improve performance.

3.7.2 Integration possibilities

A knowledge base of rules can associated with a database of facts. For example, in a company there will normally exist a personnel database, containing attributes of salary levels, job title, vacation entitlements, pension status etc. There will also exist a personnel policy guideline concerning the specific wage levels for a given job designation, rules regarding vacation quotas, etc., which is the rule base. So, there are several ways in which data and knowledge can be integrated together. First, the rules can be written down on paper and distributed to all the personnel. The problem here will be that there may not be consistency between the data and the knowledge base. Second, the knowledge base can be stored in an application program which accesses the database. Again, there is the problem of inconsistency between data and knowledge since the database can be updated without going through the application program. There is also the problem of encoding rules in an application program which maybe difficult to change.

3.7.3 Research on integration

The focus of research in integrating rules and data has been on supporting production type systems. These rules are of the form :

On event; Do action.

These rules can be grouped into 4 categories based on :- 1) the event can either be an update / retrieve and 2) the action can be either update / retrieve.

The first category contains rules which have an update event and an update action. This rule instructs the DBMS engine to watch for an event. When this occurs the engine will perform the corresponding action. However if another rule performs an update action, this would trigger the first rule to perform the update action. This in turn triggers the second rule, leading to a forward chaining control flow. The forward chaining rule system are now operational in INGRES and Sybase. [18]

The second category of rules contain an update event and a retrieve action. This rule acts as an alerter - i.e. whenever an update event occurs the second user defining the rule will be alerted whenever an event of consequence or interest, occurs.

The third category of rules contain an update event and a retrieve action. The semantics of this rule is to seek an update event. When the event occurs, the action is to be performed instead of the event. So, if the retrieve action is derived from a second rule of the same form, then the retrieve action would activate the first rule, which in turn activates the second rule. This results in a backward chaining control flow. This category of rules also allows portions of the database to be virtual or derived data items. Most commercial DBMS support views, which are virtual tables. The major impact of backward chaining systems have been exclusively on processing recursive queries. A recursive execution occurs when the same attribute appears in both the event and the action part of the rule.

The fourth category of rules contains an retrieve event and an update action. This rule provides a valuable function, i.e. an audit trail.

3.7.4 Current Implementation of rules

Stonebraker [18] identifies three basic techniques to integrate rules and the DBMS. First, brute force consists of having a list of all rules that affect each table in a database. A matching is done between the update and the condition part of each rule, to determine which rule to be activated. The downside here is that this sequential search is applicable to very small number of rules per table. For larger tables, the list has to organized for efficient access. Second, discrimination networks have been widely used in expert system shells to improve search capabilities. Third, marking can speed up rule activation by processing each rule against every record. Appropriate records will then be marked by a flag identifying the rule to be activated. Although this requires more space, there is the convenience of speed . The problem of space becomes acute when an event covers a large number of records.

3.7.5 Architectural Design Options

There are two options for EDS architectures. [19]The first is termed loose coupling. The expert system shells are used for this purpose, such as OPS5, KEE and ART, to support application logic, presentation services and the rules which control navigation. These shell systems manage the rule base as an external data manager, hence the database portion of the application is managed as a second system. The two systems are terms loosely coupled since the shell access database facilities just like any other DBMS user. Several disadvantages are highlighted. Dynamic changes to the rule base are not possible since the rule base is main memory resident and rule changes may not reach other users. There is also the problem of dynamic data - shell extracting data from the data base is liable to value changes in the data. For a shell maintaining a cache of objects, consistency is necessary. There is also the problem of non-partitionable applications. If a query is run using the entire fact base, the shell's cache will extremely large resulting in a degradation of performance.

Tight coupling is the design of choice since it integrates a rule system into a data manager. This architecture has several advantages : the DBMS manages both the data and rule base; rule base is automatically shared and persistent; the problem of dynamic data is solved by activating the rules only when the data it requires, actually changes. The problem of non-partitionable data will be handled by DBMS itself, as part of its standard operations.

4. Current Applications

This final section highlights some of the state-of-art DSS products being used by businesses, and its implications on decision making strategy. The section concludes with some of current limitations.

4.1 Groupware

The term groupware also called group decision support systems, is a somewhat more focused range software products that provide support for strategic planning sessions, focus groups, determining information requirements, setting budget and sales tracking. These product vary from GroupSystems by Ventana (a licensed TeamFocus software from IBM) which is used for electronic meetings to Lotus Notes which supports electronic communications and workflow applications. Basically these systems provide computerized support for groups of people who work together on collaborative endeavors.

Boeing has cut the time needed to complete a wide range of team project by an average of 91% of what similar work took in the past. Groupware has been the focus of its productivity gains, not only making gains around the conference table but linking departments, or colleagues indifferent locations, or even entire corporations to vastly improve the efficiency and speed of collaborative projects. IBM, a first at installing an electronic meeting room at a manufacturing and development facility in the fall of 1987 at Oswego, NY, found meeting times cut by 56%. The Boeing study last year, saved the company $6,700 per meeting, mainly in employee time.

This technology is useful for not only for generating ideas and reaching consensus quickly, but helps enhance cultural diversity. The meeting software generate ideas from all the participants in the 'room' regardless of rank, race and gender. The technology makes people equal in terms of being heard. There is also a the added advantage of managerial control - there is potential to monitor exactly what is going on in the organization since the software can keep a track of documents during interpersonal communication. This will alert managers to communication bottlenecks and other inefficiencies. The trend now is in virtual meetings with video communications (groupware version of video conferencing). This will be the next step to open entirely new productivity horizons. [23]

4.2 EIS State of Art

Since much of executive processing involves complicated problem domains, a single AI agent effort maybe insufficient when information is broad in scope and complicated in nature. Turban and Chi [21] propose a framework called a distributed intelligent EIS to illustrate how multiple resources such as knowledge, reasoning, filtering, and presentation can be combined for information processing in an EIS environment. For example, a particular piece of information maybe refined and presented based on past experiences and current practices with the aid of an expert system and neural computing.

4.3 Intelligent Support Systems

All business enterprises face global competition, and this puts tremendous pressure on enterprises to reduce their product times and increase quality in order to meet customer demands. As a result, enterprises are becoming smaller (i.e. downsizing), organizational structures are becoming flatter, and more decentralized (global reach and diversity) in order to reduce coordination costs, to improve communication and information flow, and to put decision making capabilities to sites where there is need to respond to market changes. Support for decision makers has come in the form of EIS, which delivers management data and information in a highly abbreviated form. However, these systems have not been able to stem the flood of information. Software agents are now beginning to be at the center of this data reduction need. These agents have been called software robots, knowbots, mediators, cyberservants and digital butlers. The functionality has ranged from an automated procedure (making a hard disk backup) to a comprehensive intelligence gathering software.

The term intelligent support systems here, will mean the software processes that routinely seek out and filter data according to some user defined rule and delivers this information in a predefined suitable format. The concept of software agency is an old one and can be traced to Vannevar Bush's 1945 vision of a machine called the memex that enabled users to navigate through oceans of information. John McCarthy's Advice Taker, in the 1950s, had the idea of a 'soft robot' existing in a computer network of communication utilities, that could perform tasks and ask for and receive from the user, whenever it reaches a dead-end. [10, p 182-205]

The software agent is characterized as :-

4.3.1 Examples of Intelligent Support Systems

E-mail filtering agents - considered a standard feature among most e-mail systems. It uses AI rules and a graphical user interface to create intelligent e-mail assistants that automatically sorts and stores incoming message, alert users to urgent messages and respond to those messages that fulfill a specific pre-determined condition.

Database filtering agents - Oracle Mobile Agents and Collabra's Query-by-mail are mail agents which picks up a query and stores it in a special table in the database. This table has attributes for the user's e-mail, text of the query, fields for the schedule. A second agent will invoke the query at scheduled times, collate the results, store the results for the first agent to deliver to the user.

Comshare's Robot OLAP searches for trends and patterns within the on-line analytical processing database and deliver results to the user's e-mail.

4.4 Limitations

4.4.1 Decision Support Systems

In general, much of the limitations of DSS has hinged on the development phase. This has been overcome of late through the extensive participation of end-users in all development phases beginning with the initial design, testing and implementation. The generation of information from DSS can be voluminous and daunting. For the busy decision maker, the main focus should be to find interesting patterns and trends on which to make enterprise-wide decisions relating to new product development or identifying new competitive strategies.

4.4.2 Expert System

Available ES methodologies are often complex and ineffective for applications in the generic categories. Most ES code is generally difficult to understand, debug, extend and maintain. There are several limitations to the development and research of ES :- knowledge representation is difficult and not readily available; expert knowledge is hard to extract from humans; each expert gives a particular situation assessment; ES have natural cognitive limits; ES work well within in a very specific and narrow domain; expert opinion is difficult to validate and verify; expert terminology is limited to the respective expert.

4.4.3 Groupware

The downside of groupware is it might prove threatening to some managers because employees can independently identify and connect with others working on similar tasks. Although it may increase efficiency, there is also the chance that it will upset organizational hierarchies by identifying obsolete managerial layers.

5. Future Challenges of Decision Support Systems and Expert Database Systems

This section highlights the important challenges facing the field of decision support systems (DSS) and expert database systems (EDS). Both of these areas will be discussed independently of each other. A screening methodology as proposed by Shuey, Spooner and Frieder [25] will help to formalize a research plan for these two areas. An industrial / business approach will be used in the screening process since the report above emphasizes the application of DSS and EDS in the business environment.

Challenges Facing Decision Support Systems

The ultimate mission or objective of DSS would be in the application of information technologies to effectively enhance the productivity of information workers in dealing with problems which vary in complexity and structure.

5.1 Integration

The initial challenge is one of integration - there should be a common dialog interface to allow access to all information resources. The window of connectivity should be transparent to the user and will enable integration of various architectures and platforms on which separate resources might reside. The graphical user interface should be standardized so that applications and data are compatible and easily accessible.

The components of expert systems and other AI approaches have been integrated into DSS. The knowledge base becomes a form of data/model base, the inference engine can be viewed as a knowledge base management system (DBMS and the dialog management system) and the language system is a part of the dialog. The future challenge will be to introduce more integration and extend the intelligence quotient of the DSS.

5.2 Connectivity

Connectivity is an important aspect, especially in terms of communications. The ability to connect workstations/computers through LANs (local area networks), WANs (wide area networks) and gateways is an important starting step. The next challenge would be to standardize communications protocols, communications channels, band widths, data transfer rates to accommodate the interchange of large datasets, graphical databases, digital images and video.

5.3 Document Processing

The access and management of document resources in addition to data records is necessary for the future strength and effectiveness of DSS. New search and structuring technologies such as concept retrieval, hypertext and multimedia should be developed through research efforts into effective enabling technologies.

5.4 Knowledge Acquisition and Representation

In conjunction with the intelligence component in DSS, mention should also be made of knowledge acquisition and representation. Modeling expert behavior is a difficult task. Not all knowledge can be captured by production rules. Knowledge capture can be through causal information or mathematical relationships and not just production rules. The ultimate design goal for knowledge acquisition is to allow the experts to encode their own knowledge directly into the computer removing the role of the knowledge engineer from the knowledge acquisition phase. However this may be difficult a goal to achieve in the near future since the function of the knowledge engineer maybe more of an art than a science. The transfer of knowledge involves asking the expert to introspect about the decision making process, which would conversely involve codifying experiences based on sensations, thought, sense, memories and feelings.

5.5 Screening Process

The initial step here would be to identify the critical technical areas. These can be classified as :-

5.5.1 Organizational needs

A major problem area in DSS, is the lack of appropriate GUIs to help integrate the various architectures and platforms on which separate resources or databases might reside. To the decision maker, the GUI can determine whether the DSS will succeed or fail when it comes to implementing the system within the work environment. The ability to connect to other networks is an important issue to the organization, since it provides a diverse and global source for resources. The drawback here is the potential security problem attributable to open links through insecure gateways. One way to minimize database security breaches would be to standardize communication protocols such that these disparities are minimized. Expert knowledge and its representation and acquisition is an important aspect to businesses where certain specialized skills are scarce. The idea of screening documents for important information through document retrieval and search systems might prove crucial in the near future, especially so since the decision makers may not have the time to go through voluminous documents to extract important information. The issue may not able to be addressed in the immediate future since this technology is still developing.

5.5.2 Competitive impact

The proper representation of knowledge will enable systems to mimic experts, and this can translate into competitive gains especially so if these expert systems are used in critical areas where the profit margins are very high. Sourcing of documents from various sources with the use of quick search algorithms can also enhance competitiveness. Connectivity to other networks, especially the World Wide Web (WWW) provides a means to stay abreast of changes occurring in the information superhighway. Ensuring that communications are upgraded so that the DSS systems can interface with the Internet and WWW, will enable that organizations are locked into emerging technologies and thereby maintain their competitive edge in the emerging electronic marketplace.

5.5.3 Research and development areas

Continued R&D in each of the technical areas will be important for the organization. GUI should be developed with the underlying emphasis on usability and functionality. Work flows of decision makers needs to incorporated into the interfaces such that they enhance productivity and not impose high learning curves on the users of these systems. R&D in this technical area will be of immediate importance to the organization and will continue to have substantial impact on the continued use of DSS systems in general. R&D in communications protocols although promising, might be difficult to achieve results since the standardization of protocols continues to be a major problem among vendors and technical steering committees. Document search and structuring technologies will be key technologies to focus R&D efforts in the future, since text processing capabilities have not kept pace with traditional numerical processing. Language processing is difficult given the problems of syntax and meaning, so efforts might not materialize until processing systems can address these issues. There exists a large body of work on the use of production rules, semantic networks, frames to represent knowledge. Although these methods are not completely accurate, they appear to be the most promising R&D areas which might prove feasible in the short run.

5.5.4 Available resources

The major resources needed for each of these technical areas could be expressed in terms of the hardware systems, software systems and personnel resources. High performance graphical workstations would be ideal for the development of GUIs. Text processing languages to handle document searches and structuring into meaningful information would be needed such that not only relevant information extracts can be derived, but may also lead to the automatic scanning for information and subsequent extraction based on some intelligent agent approach. Appropriate communication channels with the required bandwidths would be just some of the required resources to needed to handle the transfer of large databases, graphical objects and images.

Organizations using expert systems would need the required expertise in knowledge engineering. Similar expertise would also be required in the other technical areas. Especially so in the area of GUIs, where the need would be greatest since these would be critical systems interacting together to produce a unified integration of various architectures and platforms, such that the user sees one view of the resources in order to make a decision in the shortest possible time frame.

5.6 The Proposed Research Program

Two streams should be addressed in the immediate future. GUIs and knowledge acquisition and representation. These two areas have the greatest impact on the competitive strength of business enterprises. Research into GUIs should be able to develop newer sophisticated systems with an emphasis on standardization such that common applications and data are made compatible and accessible, not only among the various business units within the organization, but also with customers and business partners / suppliers. Several important concepts should be addressed :- what sort of information representation is required - reporting formats like charts, maps, diagrams; management tracking of productivity / performance; analysis, planning and scheduling; types of graphics - graphics in motion - especially used in dynamic modeling; and, the graphics dimensionality - whether 3-D or tabular display.

Expert knowledge which can be coded into machine language by knowledge engineers will be an important topic to consider since the procedures used currently only try to mimic as best as possible. Several issues need to addressed here :- direct interaction with the expert as opposed through the knowledge engineer; can the knowledge represented be applied to a variety of domains; can multiple knowledge sources be incorporated and are these able to interact with other expert systems tools with respect to the problem domain. Automation of the knowledge acquisition process, i.e. machine learning maybe another research topic to be investigated.

6. Challenges Facing Expert Database Systems

6.1 Architectural Design

Several directions are occurring in architectural design and rule implementation. In architectural design, tight coupling between rule and data management has been proposed over loose coupling. Loose coupling occurs when an expert system shell coordinates the application logic, presentation services and navigation; and also support calls or access to an external data management system, which in turn is handled by a separate system. The disadvantages here include the fact the rule base is main memory resident; there are also the problems of dynamic data (changes occur to the data during the inference session) and the non-partitionability of applications. Tight coupling avoids these problems since the DBMS manages both the data and rules base, i.e. the rule system is integrated with the data manager.

6.2 Rule Implementation

Rule implementation has basically focused on finding efficient solutions (i.e. query optimization) of rules systems that are recursive in nature. Where recursion is absent, the problem has been to find out when to 'fire' rules from a large rule set. Stonebraker [19] highlights three approaches : theorem proving, indexing and flags. Theorem proving will sequence all rules so as to prove that the rule applies to a query. If it does not, then the rule is discarded. The problem here is that the theorem prover might need extra information from the DBMS to apply the rule. Indexing the predicates in the rules makes the index multidimensional and this can be generalized as a kind of spatial index structure. The use of flags actually involves running the qualification from the rule through the execution system of the DBMS, and marking all data items which are accessed or updated by the rule. This method offers a very fine granularity discrimination of rule activation.

6.3 New Research Directions in Expert Database Systems

There are several research issues that should be addressed for the future of expert database systems. These can be grouped together under:- constraint-based systems, future AI requirements for intelligent databases and knowledge management. [26,27,28]

6.3.1 Constraints-based systems

Constraints, which express complex interdependencies, have had a major impact in their use in spreadsheets. Much of these constraints are implicitly defined in applications software. They have been used in factory scheduling, architectural design, construction, electrical and mechanical systems, version control and financial analysis.

Unlike traditional database relationships, which are based on actual tuple instances and contain real information of a database relation, constraints provides information about all current and future instances. Database schema provides information on the basic structure of the data, whereas constraints provide both declarative and operational knowledge of the data.

Semantic databases and knowledge representations systems can represent certain types of semantic constraints explicitly. The problems here involve the fixed nature of the constructs that are provided; stronger relationships are needed for data instances and their mutual consistencies and inter-object constraints are not strictly enforced. The work of constraint logic programming can benefit EDS, since symbolic constraints can enhance the representational and operational capabilities of databases. These constraints-based systems can thus offer inter-relational and inter-object consistency enforcement; automate external actions in response to changing external circumstances; and, help to simplify DBMS related applications.

The use of constraint logic programming provides a means to integrate the concept of constraint solving and logic programming. This presents a unified framework of formal semantics from which languages specific to an application can be derived. This framework supports the concept of a mathematical relation - i.e. input and output variables need not be defined. For example, a constraints-based description of financial mortgages can determine the monthly payment, given other parameters; or how much a borrower can borrow, given the amount paid out each month. The system is also able to derive reduced constraint specifications, given a subset of the variables, providing a legitimate query, which can in turn produce an implicit answer. Thus, the unique combination of logic programming and constraint solving can provide the necessary expressive and computational power for EDS.

6.3.2 Intelligent database systems

A concept of intelligent database systems can be best illustrated by a health-care system. Here, the health-care providers such as doctors, nurses, medical technicians can be considered as intelligent agents, each with individual and shared knowledge and reasoning abilities, to provide the best possible care for their patients. The level of care will depend on the complex relationships concerning authority, roles and responsibilities as these agents interact and cooperate with one another. The knowledge that these agents have, need to be applied within the rules and regulations defined by the health-care environment.

Conversely, the problems or tasks will be solved by agents cooperating to determine the best solution within the available resources. Intelligent distributed cooperative work (DCW) requires a variety of knowledge and associated reasoning capabilities. This knowledge and reasoning capability is characterized by the nature and amount of knowledge and reasoning required; distribution and sharing amongst agents; precision, completeness and means for acquisition, augmentation and learning. The representation, modeling, controlling and managing of large volumes and differing data types of persistent, shared, distributed knowledge and associated reasoning pose major challenges for intelligent database systems. DCW over vast computer networks is the ideal environment since it tries to overcome the disjoint technologies and research areas.

However the benefits and importance of DCW brings about several challenges. The challenges include technology integration in order to apply knowledge from disjoint technologies. DBMS principles could be used to manage knowledge, objects and data distributed spatially. Since the extending of data independence to the DCW would require considerable intelligence, an intelligent database system could be a system level agent to provide transparent access and manage private and shared objects across the network.

There is also the issue of interconnectivity (databases interconnected with other knowledge-based systems, spreadsheets, etc.), which only ensures communication between applications. There is also a need for a detailed understanding of the specific systems interfaces and functionality among the applications.

The ability for two or more agents to cooperate intelligently to achieve a common goal, evokes the problem and issue of interoperability. The intelligent database system will provide systems support for interoperability (management and access to shared and private objects). Interoperability in turn, requires intelligent task planning and monitoring, object sharing and management in a distributed environment. Synthesis of systems might include the integration of AI knowledge representation and reasoning with database data models and processing to produce a super object model. But then again, synthesis might be unrealistic compared to cooperation, since models and systems continue to be developed under heterogeneous paradigms.

6.3.3 Knowledge management

In anticipation of the size and usefulness of EDS, problems of maintenance and management tend to arise. There are several approaches to extend and capture knowledge for EDS - i.e. knowledge acquisition. The first approach involves using software engineering practices that are useful for large conventional programs for the maintenance of the knowledge base. The second approach builds support for rules into an efficient system for managing large relational databases. The problem of scaling occurs when there is a need to compute a million rules over several million data facts. If the rule support mechanisms (either data driven or query driven rules) are tightly coupled within the DBMS, then the rules can be used efficiently for large applications, integrity control, referential integrity, transition constraints and protection. The third approach would be to use the declarative form of rule specification such that EDS implementers have some form of abstract standard which will detail and define the type of answers from the knowledge base. The fourth approach represents and manages changes in the rule and data base through declarative and procedural logic. The problem here is to find knowledge representation that is declaratively well founded and procedurally effective. The fifth approach focuses on the depth and breadth of knowledge. A deep knowledge base facilitate the possibility of complex search problems. Broad knowledge in terms of general knowledge and a variety of specific concepts and situations helps to support generalization and analogy. Together with suitable procedures, the EDS should be able to address different subject areas, and may even understand English-like statements and possibly learn to enhance its capabilities.

6.4 Screening Process

According to Shuey, Spooner and Frieder [25], formalizing a research plan begins with identifying a list of areas of technical opportunity. For EDS, the technical areas are :-

6.4.1 Organizational needs

Architectural design with regards to loose and tight coupling of rules and data management might effect organizations where partitionability of applications and dynamic changes to data can affect performance of personnel, especially in the market driven environment where quick response and real-time data changes are reflected in decision making. Constraints-based systems will be important to the organization in terms of providing information about all current and future instances, and also contributes declarative and operational attributes of the data itself. In comparison, much of the traditional relational database systems contain only real-time information of a database relation. Intelligent databases would be useful in business entities where the constant cooperation of ideas, expertise is needed to make the best decision, given a finite set of resources. Knowledge acquisition would be important in order to make sense of diverse domain areas and to understand English-like text such that users do not have to resort to complex query building procedures.

6.4.2 Research and development areas

R&D efforts on constraints-based systems can be crucial since together with logic programming, the operational and representational attributes of databases can be enhanced. Development could be made on the inter-relationships and inter-object consistency enforcement, such as the automation of responses towards changing external factors. Intelligent databases should focus on efforts to identify methods to integrate disjoint technologies such that transparent user access is available. R&D towards knowledge acquisition will be crucial in the near future, involving the need to handle scaling problems in terms of the large number of rules and increasing data volume.

6.4.3 Available resources

The development of constraints-based systems will be feasible with the use of constraint logic systems (CLP), since these offer substantial advantage over programming languages. CLP systems offer ranges of input values, which can be tested with 'what-if' situations, unlike existing programming languages. This will be the initial resource requirement to the development of constraints-based systems in the organization. Intelligent database systems on the hand require technical resources such as improved operating systems, telecommunications and systems interfaces to solve the problems of interconnectivity; and planning and monitoring of tasks, object sharing and management resources to handle the issue of interoperability. Software engineering practices used for management of large conventional programs needs to be upgraded such that it will provide the capabilities and resources to offer concepts such as abstract data types, modularity and re-use for the management of the knowledge base in expert database systems.

6.5 Proposed Research Program

The integration of rules with the data manager offers advantages pertaining to the handling of data changes during inference sessions and application partionability. Research issues should delve on how this integration would translate to heterogeneous databases or even distributed networked databases.

For intelligent databases, where interconnectivity is an issue, research issues could address how operating systems, telecommunications and systems interfaces will handle the growing demand in the exchange of messages between intelligent agents. So, what are the strategic, organizational and technical connectivity issues that should be addressed ? Strategic issues can seek to find ways to achieve competitive advantage through inter-organizational systems. Organizational connectivity will invoke the need for effective cooperation of internal information systems for the evolution of the entire organization. Technical connectivity addresses the technology necessary to support the strategic and organizational requirements. Interoperability concerns the task of integration based on systems cooperation. However, a synthesis approach, for example in the creation of a super object model composed of knowledge representation, database data models and processing might not be feasible since the database models and systems continue to be developed in a heterogeneous fashion.

In the discussion of knowledge management approaches, several research issues can be posed:- is software engineering practices useful for the maintenance of the knowledge base and what are the limits; given a deep knowledge base, will a search or query respond quickly if it is stated declaratively, what about very large knowledge bases; can the rules be changed just as quickly as the data; and, should large programs be written as rule sets, rather than in conventional programming languages such that there is direct use of expert database technology ?

References

  1. Gorry,M.A and Scott.M.S (1971) "A framework for management information systems" Sloan Management Review, vol.13, p 55-70. Pioneering article on the concept of structure in decision making. The article discuses the interaction between the level of management and the amount of structure in the decision making done at each level.
  2. Sprague, R.H. (1980) "A framework for the development of decision support systems', Management Information Systems Quarterly, vol.4, p 1-26. A formalisation of the concept of decision support systems. This article provided the basis of DSS research.
  3. Turban, E (1990) Decision Support and Expert Systems : Management Support Systems, Second Edition, Macmillian Pub. Co., N.Y. A text on decision support systems and expert systems. The role of artificial intelligence in the design and development expert systems.
  4. Teng, J.T.C and Galetta, D.F. (1990) "MIS research directions: A survey of researcher's views", Data Base (21), p 1-10. A research survey on the research done in the MIS field. Published articles in major MIS journals were analyzed to derive the relevant sub-areas in this field.
  5. Eom, S.B., Lee, S.M. and Kim, J.K. (1993) "The intellectual structure of decision support systems (1971-1989)" Decision Support Systems, vol. 10, p 19-35. Citation analysis of published articles in the relevant information systems journals.
  6. Alter, S.L (1980) "Decision Support Systems : Current Practices and Continuing Challenges, Addison-Wesley, Reading, MA. Article highlighting the differences between decision support systems and electronic data processing on five dimensions.
  7. Moore J.H. and Chang M.G. (1980) Design of decision support systems, Data Base, vol. 12: 1, Fall. Emphasized that the structure of decision making as proposed by Gorry and Scott is not rigid since this structure would depend on the decision maker's perception.
  8. Bonczek, R.H., Holsapple, C.W. and Whinston, A.B. (1980) "The evolving roles of models in decision support systems", Decision Sciences, (11:2). Introduced the concept of knowledge and the AI-like terms in describing the framework for decision support models.
  9. Keen, P.G.W (1980) "Adaptive design for decision support systems", Data Base (12), Fall. Concept of adaptive process of learning and evolution in the structure of decision support systems as being important for the development of DSS.
  10. Sprague,R.H. Jr and Watson, H.J. (1996) Decision Support for Management, Prentice Hall, N.J. An updated book on decision support systems, expert systems, executive information systems. Discusses concepts, ideas and updated examples of each of these managerial support systems.
  11. Sprague, R.H. Jr and Carlson, E.D. (1982) Building Effective Decision Support Systems, Prentice-Hall, Engelwood, NJ. A primer on the development, design and implementation of decision support systems.
  12. Davis, M.W. (1988) Applied Decision Support, Prentice-Hall, Engelwood, NJ. A text on the general understanding of what a DSS is (and is not), how it is used to support decisions, and what factors contributes to a successful application.
  13. Turban (1990), p 131-141.
  14. DeSanctis, G. and Gallupe, B. (1987) "Group decision support systems: A new frontier", Management Science, May. Introducing a framework on group decision support systems.
  15. Jordan, M.L. (1988) "Executive information systems - make life easy for the lucky few", Computerworld, (Feb. 29)
  16. Neethi, S and Krisnamoorthy (1988) "Decision support systems : A critical review", In Managerial Decision Support Systems, Singh, M.G. and Salaasa, D. (eds), Elsevier Science Pub., North Holland, 77-80. This paper focuses on the importance of man-machine interaction in DSS. A paradigm based on man-machine interaction is suggested. There is a discussion on the use of AI and decision theory in developing DSS.
  17. Kerschberg, L (1989) Second international conference on expert database system at Vienna, VA, Benjamin Cummings Pub. Co. Inc., Redwood City, CA. Article proceedings on the Second international conference on EDS.
  18. Stonebraker, M (1992) "The integration of rule systems and database systems", IEEE Transactions on Knowledge and Data Engineering, (4:5), p 415-423. The integration of rules systems into DBMS. A discussion of a survey of research in this area and some of the implementation issues.
  19. Stonebraker, M and Hearst, M (1989) "Future trends in expert data base systems", In Second international conference on expert database system at Vienna, VA, Kerschberg, L, (ed) Benjamin Cummings Pub. Co. Inc., Redwood City, CA. p 3-20. This paper discusses two architectures for EDS - loose and tight coupling, some research trends in integrating rule systems into DBMS.
  20. Volonino, L., Watson, H.J and Robinson, S (1995) "Using EIS to respond to dynamic business conditions", Decision Support Systems, (14:2) p. 105-116. An investigation into how EIS technology can be used to respond to major business problems. What are the current problems and the changes in organizational structure and managerial activities that should be taken to mitigate these problems.
  21. Chi, R.T. and Turban, E (1995) " Distributed intelligent executive information systems", Decision Support Systems, {14:2) p 117-130. This article proposes that a single AI agent is insufficient when information is broad in scope and complicated nature, as is evident in most EIS. This paper discusses how a framework called the distributed intelligent EIS, which combines multiple resources for information processing.
  22. Mark, F.N (1994) " Management support systems and their evolution from executive information system", Information Strategy : The Executive's Journal, (10:3), p 31-38. This paper proposes using the strategic business objectives (SBO) method to develop EIS, such that these systems will be able to meet executive needs. Advantages and disadvantages of this methodology are discussed.
  23. Kirkpatrick, D (1992) " Here comes the payoff from PCs", Fortune, (125:6), p 93-102. This article discusses the productivity gains made through the use of groupware products - electronic meeting software.
  24. Grabowski, G. and Wallace, W.A. (1993) "An expert system for maritime pilots: Its design and assessment using gaming." Management Science, (39:12), p 1506-1530. Design and methodology of the piloting expert system to be used by maritime pilots - a means to automate and also to complement navigational skills.
  25. Shuey, R.L., Spooner, D.L. and Frieder, O (1996) "The Architecture of Distributed Computer Systems: A Data Engineering Perspective", Addison-Wesley (forthcoming), Chapter 14. Develops a screening methodology to address the issue of formalizing a research direction or a means to identify a research problem.
  26. Morgenstern, M., Borgida, A., Lassez, C., Maier, D and Wiederhold, G. (1988) "Constraint-based systems : Knowledge about data", In Second international conference on expert database system at Vienna, VA, Kerschberg, L, (ed.) Benjamin Cummings Pub. Co. Inc., Redwood City, CA. p 23-44. An article addressing the applications, opportunities and techniques for constraints-based systems which ranges over databases, knowledge-based systems and logic programming.
  27. Brodie, M.L., Bobrow, D., Lesser, V., Madnick, S., Tsichritzis, D., and Hewitt, C (1988) "Future artificial intelligence requirements for intelligent database systems", In Second international conference on expert database system at Vienna, VA, Kerschberg, L, (ed.) Benjamin Cummings Pub. Co. Inc., Redwood City, CA. p 45-62. This paper identifies the potential roles and nature of the emerging notion of intelligent database systems.
  28. Walker, A., Kowalski, B., Lenat, D., Soloway, E., and Stonebraker, M (1988) "Knowledge Management", In Second international conference on expert database system at Vienna, VA, Kerschberg, L, (ed.) Benjamin Cummings Pub. Co. Inc., Redwood City, CA. p 63-69. Discusses some the approaches to knowledge management, a necessity in the light of increasing program size conversely resulting in problem of management and maintenance.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5