BIS4430 Web-based Information Systems Management

Transcription

1 BIS4430 Web-based Information Systems Management UNIT 02 - Organisational Information Systems Context The purpose of the module is to help students understand the relationships between Information Systems and Organisations. This Unit describes the nature of modern organizations. These range in size from one person professionals working on their own or within a co-operating network to large multinational groups operating within the global economy. Whatever their size and wherever they are to be found today s organizations live on Information. Therefore, irrespective of the purpose for which they exist and their structural form every organisation today have one property in common, their need for Information and Information Systems.The basic concepts of information systems are introduced and the role of information in the organisation is discussed. The relationship between the nature of an organization and the Information Systems in use in that organization is critical and will contribute enormously to the success or otherwise of the organization. Learning Outcomes At the end of this unit you should be able to: Evaluate the role played by the major types of IS in a business and their relationship to each other Discuss the major types of Information Systems and relate them to managerial functions Identify the information requirements at various functional levels of the organisation Discuss various technologies that could be used for decision support Assess how systems that support decision making can provide value for the firm Identify the challenges posed by IS in the enterprise and management solutions Reading Material 1. Kenneth C Laudon and Jane P Laudon, (2006), 'Management Information Systems, Managing the Digital Firm, 9 th Edition, Pearson Education, ISBN Efraim Turban, Dorothy Leidner, Ephraim McLean, and James Wetherbe, (2006), 'Information Technology for Management, Transforming Organizations in the Digital Economy', 5 th Edition, John Wiley & Sons, ISBN Geetha Abeysinghe Page 1 02/07/08

2 Supplementary Texts 1. James A. O'Brien (2004), Management Information Systems, Managing Information Technology in the Business Enterprise, 6 th Edition, McGraw Hill-Irwin, ISBN , Chapter Ralph M Stair and George V. Reynolds (2001), Principles of Information Systems, 5 th Edition, Course Technology, Thomsons Learning. 3. Effy Oz (2005), Management Information Systems, 4 th Edition, Course Technology, Thomsons Learning, Chapter Efraim Turban and Jay E. Aronson, (2001), Decision Suppot Systems and Intelligent Systems, Prentice Hall. Table of Contents UNIT 02 - Organisational Information Systems... 1 Context... 1 Learning Outcomes... 1 Reading Material... 1 Supplementary Texts... 2 Table of Contents CLASSIFICATION AND EVOLUTION OF INFORMATION SYSTEMS... 3 Classification by Organisational Level... 3 Classification by the Type of Support Provided DECISION SUPPORT SYSTEMS... 7 Origins of DSS... 7 Nature of Managerial Work... 8 Components of a DSS... 9 Analytical Capabilities Of DSS Example Case Study GROUP DECISION SUPPORT SYSTEMS (GDSS) EXECUTIVE INFORMATION SYSTEMS (EIS) INTELLIGENT SUPPORT SYSTEMS What is Artificial Intelligence (AI)? EXPERT SYSTEMS (ES) The Components of an Expert System Hybrid AI Systems Activities Activity (ISs in an organisation) Activity (what are transaction processing systems) Activity 2.3 (customer profiling) Activity 2.4 (Case Studies in DSS) Activity 2.5 (What led to the growth of DSS) Activity 2.6 (GDSS) Activity 2.7 (Applications of ES) Activity 2.8 (Ethical Issues arising from DSS and ES) Geetha Abeysinghe Page 2 02/07/08

3 Content 2.1 CLASSIFICATION AND EVOLUTION OF INFORMATION SYSTEMS In Unit 1 we looked at organisations, their purpose, structure, and behaviour. We also looked at organisations as open systems. We had a brief look at what information systems are, their components and their role within organisations. Now read chapter 1 of Turban et al., Pages 22-25, Examples of Information Systems, in order to get a flavour of the wide variety of applications and benefits that information systems bring to organisations. As we saw in Unit 1, depending on the nature of the business they are dealing with, organisations vary in their size, their culture, and their structure. Within an organisation there are different interests (departments or functions), specialities, and different levels of authority. Information systems in an organisation can be classified based on the different roles they play within the organisation. Classification by Organisational Level Organizational structure refers to organizational subunits and the way they relate to the overall organization. Depending on the goals of the organization and its approach to management, a number of structures can be used. An organization s structure can affect how information systems are viewed and what kinds are used. Ralph M Stair and George W Reynolds, (2001), Principles of Information Systems. 5 th Edition, Thomson Learning, page 40) Now read chapter 3 of Laudon and Laudon, Pages 76-77, Different organizational Types, and look at Table 3-2 which gives one classification of organisations. The most common organisational structures we come across are: traditional, project, team and multidimensional. With the advancement of communication and network technologies some organisations today have adopted innovative structures which allow them to be agile and efficient. Vast majority of organisations still display some degree of hierarchy, where they have divisions, departments, work units, etc. Let us look at an organisation as a three dimensional pyramid structure where the height of the pyramid reflects the degree of hierarchy within that organisation. We can take an internal view of the organisation from a vertical view (where you can imagine the pyramid being cut into vertical sections) or a horizontal view (where the pyramid is cut horizontally). In the first view, each vertical section represents a functional area of the organisation, and thus a vertical view can be compared to a functional view of the organisation. In the second view each horizontal section represents a process, and thus the horizontal view is similar to a process view of the organisation. This can be represented graphically as shown in Figure 2.1 and Figure 2.2. Geetha Abeysinghe Page 3 02/07/08

4 Information systems can be designed to support the functional areas or traditional departments such as, accounting, finance, marketing, human resources, and manufacturing, of Geetha Abeysinghe Page 4 02/07/08

5 an organisation. Such systems are classified as functional information systems. Functional information systems typically follow the organisational structure. Two other systems which reflect the way organisations are structured are: enterprise information systems and inter-organisational systems. Functional information systems are typically focused on increasing the efficiency of a particular department or a functional area. Organisations have realised that in order to be agile and efficient they need to focus on organisational processes. As can be seen from Figure 2.2, a process may involve more than one functional area. One disadvantage of functional systems is that although they may support a particular functional area effectively, they may be incompatible to each other. Such systems, rather than aiding organisational performance will act as inhibitors to an organisation s development and change. Further to this the adoption of e-commerce necessitates not only the interaction between internal systems (which demands compatibility) but sometimes also the systems of partners and suppliers. Now carry out Activity 2.1 (ISs in an organisation) (Learning Outcome: Evaluate the role played by the major types of IS in a business and their relationship to each other) Now read chapter 4, Pages , Brief history and Scope of EC, of Turban et al. Note what applications and technologies are used for in e-commerce. Systems that support several departments or the entire enterprise are called enterprise information systems. Enterprise systems will be discussed in detail in Unit 6 of this module. As you now know organisations which adopt e-commerce require to interact with systems outside the organisation such as those of their partners or suppliers. Such systems which connect two or more organisations are called inter-organisational systems. Classification by the Type of Support Provided In Activity 1, you read about some examples of Information Systems used for different purposes and providing different benefits. At the end of each example the authors listed the activities that were supported by the IS you read about. This provides another basis for classifying Information Systems, the type of support they provide irrespective of the functional area. James O Brien (2004) groups the support provided by IS into three major categories: supporting business processes, supporting decision making, and supporting competitive advantage. You will study about competitive advantage in detail later. In this unit we will mainly look at systems that will help managers make decisions more effectively. Before doing so, let us briefly look at how the role of IS within organisations has changed with time. In the early days, computers were mainly used for data processing activities. Mainly large volume, repetitive labour intensive activities such as, record keeping, accounting, and Geetha Abeysinghe Page 5 02/07/08

6 transaction processing were automated. The main objective of these early systems was to reduce cost. In most organisations which started using computers at this early stage, the first application where IT was used was transaction processing. Now carry out Activity 2.2 (what are transaction processing systems) (Learning Outcome: Discuss the major types of Information Systems and relate them to managerial functions) Then managers realised the large volumes of data being collected can be used for their decision making purposes. This added another role to IS, giving rise to the concept of Management Information Systems (MIS). The focus of this new role was to provide managers predefined management reports which provided information for routine decision making in functional areas. Now read Chapter 2 of Laudon and Laudon, Pages 44-45, Management Information Systems. Soon it was apparent that these predefined reports did not satisfy adequately the information needs of managers. This gave rise to the concept of Decision Support Systems (DSS). DSS provided managers with ad hoc interactive support for their decision making needs. These systems could be tailored to individual decision making needs of end users, whether they are single users, groups, or organisations as a whole. Decision Support provided Information form and frequency Management Information Systems (MIS) Provide information about the performance of the organisation Periodic, exception, demand, and push reports and responses Decision Support Systems (DSS) Provide information and decision support techniques to analyse specific problems or opportunities Interactive inquireries and responses Information format Prespecified, fixed format Ad hoc, flexible, and adaptable format Information processing methodology Information produced by extraction and manipulation of business data Information produced by analytical modelling of business data Table 2.1: Comparison of MIS and DSS (James O Brien, Management Information Systems, Managing Information technology in the Business Enterprise, McGraw Hill-Irwin, 2004, page267) In the 1980s there were several developments in the field of information systems. This was due to a number of reasons: the rapid development of microcomputers, wide usage and acceptance of application software packages, and telecommunication networks are some. These developments gave rise to the concept of end-user computing. Other new roles which appeared in the 1980s are: providing decision support for executives (Executive Information Systems) and people working in groups (Group Decision Support Systems); use of artificial intelligence techniques giving rise to expert systems and other intelligent systems; Geetha Abeysinghe Page 6 02/07/08

7 information technology became an integral part of business processes, products, and services helping organisations build a competitive advantage in the global market place (Strategic Information Systems). The major focus of this module is this latter category, the strategic role of information systems. In the 1990s we saw the rapid growth of the Internet, intranets and extranets. Globalisation changed the way organisations conducted business. The capabilities of Information Systems changed dramatically. Today, IS/IT is integrated into business processes to such an extent that one cannot be isolated from the other. Now read Chapter 2, Pages 54-57, The evolution of Support Systems, of Turban et al. Make a note of the different kinds of systems mentioned, and different technologies mentioned in the learning journal. In the next section we will look in detail at some of the decision support systems more widely used in organisations today. 2.2 DECISION SUPPORT SYSTEMS Decision Support Systems are computer-based information systems that provide interactive information support to managers and business professional during the decision-making process. Decision support systems use (1) analytical models, (2) specialized databases, (3) a decision maker s own insight and judgements, and (4) an interactive, computer-based modelling process to support the making of semi structured and unstructured business decisions. (James O Brien, Management Information Systems, Managing Information technology in the Business Enterprise, McGraw Hill-Irwin, 2004, page267) Origins of DSS DSS grew out of managerial dissatisfaction with systems developed for the efficient processing of large amounts of routine data in a structured manner being used to tackle unstructured problems. It was clear that there was a mismatch between system functionality and managerial requirements. Unfortunately whilst the former was easily defined and could be modified albeit still resulting in a capability for handling structured problems, the latter was less easy to pin down. The classical approaches to requirements capture relied heavily on users being able to articulate their needs to computer professionals with limited knowledge of the organisational domain. This is a comparatively straightforward task with highly structured problems but very difficult with less structured ones. Now read Chapter 11, Pages , The Manager s Job, of Turban et al. Geetha Abeysinghe Page 7 02/07/08

8 Nature of Managerial Work The Manager s role in the workplace has long enjoyed the attention of academics. Clearly the responsibilities of managers vary enormously. Among the various roles managers play, decision making is one of the most challenging. Now read Chapter 3, Section 3.3 of Laudon and Laudon, The Impact of IT on Management Decision Making. The type of problems managers and business professionals face can be described as: Structured Problems which are routine and repetitive. A standard solution method or a set of decision rules will exist. Therefore, the method of finding the best solution is clear. Examples of such problems are: finding an appropriate inventory level, choosing an optimal investment strategy, or approving credit. As you can see they are mostly about minimising cost or maximising profit. Unstructured Problems which are novel, non-routine, and complex. The decision making is mostly based on human intuition, experience and knowledge. Therefore, there are no decision rules or the decision method is fuzzy, and it is almost impossible to find the best solution. Rather a satisfactory solution will be found. Examples of such problems are: selecting a cover for a magazine, planning new services, hiring an executive, or personnel selection. Some decisions are semi-structured; in such cases only part of the problem has a clear-cut answer provided by a routine procedure. Decision Support Systems are more likely to be used by middle and senior management whilst computer-based support for structured problems will be made available to relatively junior and less experienced staff. Read Chapter 11, Pages , A Framework for Computerized Decision Analysis, of Turban et al. Make notes on your thoughts on the two topics in the learning journal. The kind of problems we described above is not a novelty to managers. They have dealt with these problems as long as organisations existed. So, what has changed? Why do managers need the support of IS/IT today? Read Chapter 11, Pages , Why Managers need the Support of Information Technology, and Can the Manager s Job be Fully Automated of Turban et al. Make notes on your thoughts on the two topics in the learning journal. Geetha Abeysinghe Page 8 02/07/08

9 Components of a DSS In order to understand how a DSS, first we need to look at the components of a DSS. What constitute a DSS depends on the functionality required by the uses. A DSS should at least contain a user interface, a model management subsystem and a data management subsystem. A feature of the working practice of middle and senior managers is a high degree of parallelism i.e. they are engaged on several different tasks at any moment. This requires them to be able to switch between tasks, for example, either at the request of a colleague or because of external interactions. Computer support for such a working environment means systems should have rapid response, highly intuitive user interfaces and require little or no assistance from professional programmers during daily operations. The widespread use today of iconic interfaces will normally satisfy the HCI requirement. At the same time users need access to sophisticated data analysis and modelling tools. The various models relevant to the application area such as financial, statistical, management science, or other quantitative models that provide the system s analytical capabilities are held on a Modelbase. Examples of models include the Operational Research models used in Transport Scheduling which may help a distribution manager determine the best way to satisfy demand for a product produced in several locations and consumed in many other locations. Another example is analysis of Queuing. Queuing Theory helps managers resolve one of life s most irritating phenomena, waiting in line to be served. Numerous options exist, including what is the current favourite, one line of waiting customers and several service points each capable of handing all possible service requests. The Model Management Subsystem consists of the modelbase, the appropriate software which managers the modelbase, and modelling languages for building custom models. Read Chapter 11, Pages , Modelling and Models, of Turban et al. Data Analysis facilities are provided through a Data Management Subsystem. Data Management Subsystem includes the database which contains relevant data for the situation and is managed by the database management system (DBMS). The database includes both internal and external data. Internal data are either transactional data or data collected internally from other subsystems in the organisation. The DBMS creates, modifies, and maintains the database as required by the user. The database component enables the DSS to perform any type of data analysis. Optionally a DSS may contain a Knowledge-based Management Subsystem. This is usually interconnected with the organisation s knowledge repository, which is called the organisation s knowledge base. This can also be part of a knowledge management system enhancing the decision making capability with intelligence, or it can be one or more expert systems providing expertise about the application domain, or a combination of both. Knowledge-based systems and expert systems will be discussed further in Section 2.6. Read Chapter 11, Pages , Structure and Components of DSS, of Turban et al. Geetha Abeysinghe Page 9 02/07/08

10 Analytical Capabilities of DSS DSS have many analytical capabilities. Let s take the example of a company which has recognised that there is a positive correlation between the number of sales staff and revenue generated. Through setting up a simple model in a spreadsheet based on this relationship, it is possible to perform the following types of analysis: What-if analysis: In a typical what-if analysis the manager can make changes to variables, or relationships and observe the resulting changes in the values of other variables. For example, in the above company, 'what if we increase our sales force by 10 percent? The corresponding forecast increase in sales can be readily calculated by the spreadsheet. Or in a spreadsheet model one can change the formula for the tax and observe the change in the net profit. Mathematical models also make it easy to ask what-if questions that explore the effect of alternative assumptions about key variables. For example, a bank's planning model may contain the assumption that it will be able to make loans next year at 9% interest. Bank managers might want to see whether the bank will still be profitable if the interest rate drops to 8% or if it takes extra months to roll out a new service. A manager can do what-if analysis to study what effect changes have on a problem. For example, product pricing is a complex decision. Decision makers must take into consideration many internal items, such as, material, production and labour costs, and external items such as competitors' prices and product demand. A DSS can help answer questions like, 'what if the price of raw materials increases by 3.6% in a year? What if demand for the product increases by 10%? What if competitors reduce price for a similar product by 20%? Sensitivity analysis: This is a structured what-if analysis where we change the value of a single variable repeatedly to see the effects on other variables. In our previous example, instead of increasing the sales force by 10%, we can increase the sales force in increments of 1%, and see its effect on labour costs, sales, and other overheads. This could be repeatedly done until an optimal value that matches the company objectives is reached. Typically this type of analysis is carried out when the decision makers are uncertain about the estimates or assumptions they make about a key variable. Goal-seeking analysis: This is the reverse of what-if. This helps managers determine what they should do to achieve a certain goal. Here we first decide the value we want to achieve in a particular variable. Then we change the other variables governing the targeted value until the target is achieved, e.g. by what percentage do we need to increase the sales force to achieve a growth in sales of 100,000? Suppose the company's goal is to increase sales of its top-selling product by 10%. A DSS can show different ways to achieve that goal. A student at Middlesex University can use a DSS to determine what grades he or she must get in her final year in order to achieve a 1 st class. Optimisation analysis and simulation: This is a complex version of the goal seeking analysis. The aim of this analysis is to find the optimal value for one or more variables under a specific set of constraints. The values of the non-target variables are changed under the given Geetha Abeysinghe Page 10 02/07/08

11 constraints until the best values for the target variables are found. e.g. which is the best mix of increasing advertising and sales force to achieve a set increase in sales? Now read Chapter 11, How a DSS works, in Turban et al., Pages Example Case Study One typical example of a DSS application is for the approval of mortgage applications. Traditionally, when a customer approaches a bank for a mortgage, a staff member asks the applicant questions about their assets and liabilities. The answers are entered into paper based forms, some may be entered into an internal database system. An underwriter will review a combination of paper-based applications and online credit bureau files in order to establish the applicant s credit worthiness. External agencies may be contacted via telephone, or by other means, the credit history of the customer with the bank may be checked if there exists one, or the applicant s own bank may be contacted, etc. This process usually takes several days. Although banks have cut down the time taken for collection of information, processing the information, and risk evaluation before taking a decision, is done by humans which sometimes causes delays. Today, the financial market, as any other, is faced with fierce competition. With the introduction of online services there is a need to cut down the waiting time and reduce the time to an almost instantaneous decision. This could only be achieved by integrating IS/IT to the process. One solution is to combine decision support software with data warehousing. The bank s new system will automate many decisions that were made by human beings in the past. Much of the work which was previously done by human underwriters now can be done by the system. The mortgage decision process is the same whether a customer calls, goes into a bank branch, or apply on line. The applicant is asked questions about his/her assets and liabilities. The answers are entered into a computer system where the data are stored in a customer warehouse. Then the DSS takes over. It first collects data online about the applicant from credit bureau in order to determine the applicant s credit worthiness. The system then combines that information with any data on the applicant previously stored in the data warehouse and the information newly gathered from the applicant. All this data is submitted to a series of predefined lending criteria already stored in the decision support system s knowledge base. The system will then produce answers to any questions posed by the loan officer.. The system lets users perform what-if analyses with various terms and loan amounts to see what best suits a customer s needs. When customers calls the bank for these streamlined mortgage loans, bank staff input the applicant s data on a series of graphically oriented screens. These screens can be navigated in any order. During the application interview, staff can easily shift from screen to screen to jump between assets and liabilities for a husband and wife, conversing with the clients while inputting the information. Geetha Abeysinghe Page 11 02/07/08

12 Now read Chapter 11, the opening case, New Balance Makes Sure That Shoes Fit, in Turban et al., Pages Now carry out Activity 2.3 (customer profiling) (Learning Outcome: Discuss various technologies that could be used for decision support ) Now carry out Activity 2.4 (Case Studies in DSS) (Learning Outcome: Assess how systems that support decision making can provide value for the firm) Today most decisions within organisations are not taken by one single person but by a group of people. In the new business models which have arisen due to the use of advanced communication technologies, not only there exist group decision making but members of the decision making team can be dispersed in different geographical locations. Systems that support group decision making are known as Groups Decision support Systems (GDSS). 2.3 GROUP DECISION SUPPORT SYSTEMS (GDSS) Now carry out Activity 2.5 (What led to the growth of DSS) (Learning Outcome: Discuss the major types of Information Systems and relate them to managerial functions) GDSS provides facilities which are typical in group decision making, such as, brainstorming, idea organisers, questionnaires, and voting tools, and provides anonymity when required. GDSS software attempt to eliminate the negative factors associated with group decision making, such as: a few people dominating the discussions, time consuming, possibility of deviating from the problem concerned, and reluctance of members of expressing their ideas in the fear of being victimised by top management or internal politics. Based on the frequency of decision making and the geographical location of the participants, there are four commonly used alternatives as shown in Figure 2.6. Decision Room: This alternative is chosen when the decision makers are located in the same building or geographic area and they are occasional users of the GDSS approach. All the decision makers meet together in the same room. Each team member is provided with a computer terminal, usually arranged in a horse-shoe fashion. Facilitator is located in front of the room with a white board. In multinational corporations, interpreters are provided when necessary. Software is provided so that each person see the output in his/her own language. Geetha Abeysinghe Page 12 02/07/08

13 Local Area Network: Group members are located in the same building or geographic area, and the frequency of decision making is very high. In this case the group members participate from the privacy of their offices still using a moderator. Teleconferencing: Location of group members is distant and the decision frequency is low. At each location the local group meet in a decision room. The decision rooms at different locations are tied together via telecommunication liks. Wide Area Decision Network: Location of group members is geographically remote and the decision frequency is high. This alternative is used by virtual workgroups, and groups of workers located around the world working on common problems via a GDSS. Now read Chapter 13, Section 13.3, Group Decision-Support Systems of Laudon and Laudon, Pages Make notes on the different types of tools used in GDSS in learning journal. Now carry out Activity 2.6 (GDSS) (Learning Outcome: Assess how systems that support decision making can provide value for the firm) Geetha Abeysinghe Page 13 02/07/08

14 2.4 EXECUTIVE INFORMATION SYSTEMS (EIS) The primary objective of Executive information systems is to provide top executives with quick and easy access to strategic information. They combine many of the features of management information systems and decision support systems. They are characterised by their easy-to-use graphical interfaces and graphical displays that can be customised to decision makers preferences. Other features they are well known for are exception reporting, trend analysis, and drill down capabilities. According to Effy Oz (2002) an effective EIS should display the following features: - An easy-to-use and easy-to-learn graphical interface - On-request drill-down capability that allows executives to look at a situation in increasing detail - On-demand financial and other critical success factor ratios (e.g. sales per employee, divisional total actual expense against budgeted funds) - Easy-to-use but state-of-the-art navigational tools which allows the executives to navigate the databases and the data ware houses, - Statistical analysis tools - Sensitivity analysis - Ability to answer to ad hoc queries - Access to external data stores. Today many systems added with additional features such as web browsing, electronic mail, groupware tools, DSS and expert system capabilities, and in some cases even with other intelligent techniques. Due to the widespread capabilities and user friendliness of the systems, today they are more widely used by managerial end users at all levels in the organisation. These systems are also known as, Enterprise Information Systems (EIS), and Executive Support Systems (ESS). These names also reflect the wide range of features that are added to theses systems today. Turban et al. Distinguishes between executive information systems and executive support systems, saying that the latter goes beyond EIS, providing more functionalities and intelligent support. However, in this module we will be using the two terms interchangeably. The lines between DSS and EIS are hazy today. Many full blown DSS are sold by vendors under the label EIS. When you are purchasing a system it is the features you need to look at, not the label it comes under. Now read chapter 13, Section 13.4, Executive Support in the Enterprise of Laudon and Laudon, Pages Make notes on the different types of tools used in GDSS in learning journal. Vendors more popularly refer to theses systems today as business intelligence systems. This is because over the years these systems have been evolved into business activity monitoring systems and business performance management systems. This does not indicate that EIS is not used today as originally intended. Only that if needed, there are much more advanced tools that can be incorporated giving the systems capability to support the executives in more unstructured decisions. Geetha Abeysinghe Page 14 02/07/08

15 Now read Chapter 11 of Turban et al, Pages , Business Performance Management. 2.5 INTELLIGENT SUPPORT SYSTEMS As we have seen so far organisations are complex objects. Decision making within this complex environments in most cases is not a structured process. Globalisation and rapidly changing market situations have increased the complexity of business decision making and the uncertainty in the decision environment. Executives of the modern enterprise are faced with: complex international markets; challenging, dynamic, and global competitive environments; intricate government regulations (having to deal with more than one government); and increasingly demanding workforces and customers. As the complexity of the decision making environment increases, so does the uncertainty of the decision situation. Decision making under these conditions is not a simple task. Managers do not base their decisions purely on the information provided, they also need to learn from past experience and apply their knowledge, know-how, heuristics, and sometimes gut instinct. In other words managers need to use their intelligence. How do we develop information systems that could display intelligence? We achieve this by applying Artificial Intelligence (AI) techniques. What is Artificial Intelligence (AI)? AI is a dynamic growing field of science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, and engineering. From the early stages the research focus of AI has been developing computer systems and machines that demonstrate characteristics of intelligence, or in other words show intelligent behaviour. Oz (2002) defines intelligence as, The ability to acquire and apply knowledge, to think, and to reason. O Brien (2004:283) lists some of the attributes of intelligent behaviour as: - Think and reason, - Use reason to solve problems, - Learn and understand from experience, - Acquire and apply knowledge, - Exhibit creativity and imagination, - Deal with complex or perplexing situations, - Respond quickly and successfully to new situations, - Recognise the relative importance of elements in a situation, - Handle ambiguous, incomplete, or erroneous information. British mathematician Alan Turing, devised in the 1940 s what became to be known as the Turing Test which aimed at detecting whether a machine can exhibit intelligent behaviour that cannot be distinguished from that of a human agent. The Japanese Fifth Generation Computer Project was started in 1981with the aim of developing technologies that would ultimately lead to intelligent computers. In the 1980s there were rather fierce, debates about the possibility of making a machine truly intelligent. Opinions were coming from the Geetha Abeysinghe Page 15 02/07/08

16 computing, mathematics, philosophy and psychology communities. The quest for a way to make the machine fully intelligent as a human being is still going on. Now read Chapter 11, Section 11.5 of Turban et al., Pages , Intelligent Support Systems: The Basics. Look at Table 11.4, The Commercial AI Techniques, which lists most commonly used AI techniques and their applications. Follow the further readings provided, and look at any example case studies given. One of the areas where AI has had a positive impact is business. A number of AI technologies have been developed to demonstrate specific aspects of intelligent behaviour, such as learning from examples and to solve specific problems. Of these, Expert Systems, Neural Networks, Natural language processing, Fuzzy Logic, Genetic Algorithms, and Intelligent Agents are the most well-known and successfully applied in business. O Brien (2004) groups the applications of AI into three major categories as sown in Figure 2.7. Combining information systems and database management systems with programs that use principles of AI can provide excellent support for high-level decision making in business. Basic appreciation of these technologies including differences between them will help you to select an appropriate technology for a specific problem. You also need to appreciate the limitations and costs of these technologies. These include the expertise and time required in setting up and maintaining the solution. Information systems which use AI to mimic human behaviour are known as intelligent support systems. One of the most widely used intelligent systems are Expert Systems (ES) Geetha Abeysinghe Page 16 02/07/08

17 and other knowledge-based information systems (KBIS). In this unit we discuss expert systems in detail. 2.6 EXPERT SYSTEMS (ES) In the attempts to make machines as intelligent as human beings, one effort which has been more fruitful than others was to code into a computer program a limited amount of knowledge about a particular domain or task, so that the computer executing the program can answer a limited number of questions about that task or domain. As you read in Turban et al. (reflect back to what you read on, page 477 under Knowledge and AI ) the organised collection of knowledge specific to a domain stored, so that it can be used by an intelligent system is known as a knowledge base. Information systems that used such knowledge bases are usually referred to as knowledge based systems. Knowledge based systems are computer-based systems that embody aspects of human knowledge in domains such as gameplaying (e.g. chess), theorem-proving and so on. The domains of application of knowledge based systems have been extended to many areas in business today. As we all know when the problem domain is complex, or the problem itself is critical to the organisation we seek the advice of experts who have specific knowledge about that domain. As the unstructured nature of the problem increases, so will be the cost of the expert advice. An information system which tries to emulate the knowledge of the expert in a specific domain is called an Expert System (ES). An expert system is a knowledge-based system that captures aspects of an expert s competent performance in a specific, complex, but relatively narrow domain of expertise and makes it available to those with less expertise. The system can provide answers to user queries by making human like inferences about the knowledge stored in the knowledge base. The system is also capable of explaining the reasoning process on which the answer was based on. The Components of an Expert System The components of an expert system includes: a knowledge base, an inference engine (software modules that perform inferences on the stored knowledge and user interface programs (software that communicates with the end user). The user interface also includes an explanation program, which explains the advice/conclusions given to user if asked. The top part of Figure 2.8 shows the components of an expert system. Now read Chapter 11, The Components of Expert Systems, pages of Turban et al. Now read Chapter 12, How Expert Systems Work, pages in Laudon and Laudon. One important difference between applications of AI and conventional computing is that they are not-algorithmic in general. This means that AI applications do not guarantee a correct answer, and that the logic governing their operations can rarely be fully expressed using conventional procedural style programming. Geetha Abeysinghe Page 17 02/07/08

18 In the early years, expert systems were developed using specialised AI languages such as LISP and Prolog. Today there are a number of programming environments called AI shells available in the market. These provide a very user friendly development environments which facilitates developing customised user-friendly interfaces, capturing and building the knowledge base, building the rules for the inference engine and strategies for managing and searching the rule base. Developing expert systems are usually done using the prototyping process. A professional, known as a knowledge engineer, works with the domain experts, eliciting and capturing their Geetha Abeysinghe Page 18 02/07/08

19 knowledge, and building the knowledge base. A knowledge engineer performs a role much similar to a systems analyst in conventional information systems development. The process of building the knowledge base is shown in the lower part of Figure 2.8. Application Categories of Expert Systems Decision management Systems that appraise situations or consider alternatives and make recommendations based on criteria supplied during the discovery process. Examples are: - Loan portfolio analysis - Employee performance evaluation - Insurance underwriting - Demographic forecasts - Diagnostic/troubleshooting Systems that infer underlying causes from reported symptoms and history. Examples are: - Equipment calibration - Help desk operations - Software debugging - Medical diagnosis Design/configuration Systems that help configure equipment components, given existing constraints. Examples are: - Computer option installation - Manufacturability studies - Communication networks - Optimum assembly plan Selection/classification Systems that help users choose products or processes, often from among large or complex sets of alternatives. Examples are: - Material selection - Delinquent account identification - Information classification - Suspect identification Process monitoring/control Systems that monitor and control procedures or processes. Examples are: - Machine control (including robotics) - Inventory control - Production monitoring - Chemical testing Table 2.2: Most widely used application categories and examples of expert systems (O Brien, 2004:295). Geetha Abeysinghe Page 19 02/07/08

20 Now read Chapter 11, Applications of Expert Systems, pages of Turban et al. See Table 11.6, Generic Categories of Expert Systems. What types of problems are most suitable for expert system solutions? O Brien (2004) summarises the generic tasks that can be accomplished using expert systems as shown in Table 2.2. We can also find out the answer to this question by looking at various examples where expert systems have been used successfully in organisations. Now read Chapter 12, Examples of Successful Expert Systems of Laudon and Laudon, Page Now carry out Activity 2.7 (Applications of ES) (Learning Outcome: Assess how systems that support decision making can provide value for the firm ) There are a huge number of benefits an ES can bring to an organisation. Now read Chapter 11, The Benefits and Limitations of Expert Systems, pages of Turban et al. See Table 11.5, Benefits of Expert Systems. Despite the many advantages certain limitations in ES has slowed down the commercial adoption of ES. Turban and Aronson (2001:423) lists some of the problems which caused this slow adoption: Knowledge is not always readily available It can be difficult to extract expertise from humans The approach of each expert to a situation assessment may be different yet correct. It is hard, even for a highly skilled expert, to abstract good situational assessment when s/he is under pressure. ES work well only within a narrow domain of knowledge. Most experts have no independent means of checking whether their conclusions are reasonable. The vocabulary, or jargon, that experts use to express facts and relations is often limited and not understood by others. Help is often required from knowledge engineers who are rare and expensive, a fact that could make ES construction costly. Lack of trust on the part of end users may be a barrier to ES use. Knowledge transfer is subject to a host of perceptual and judgemental biases. ES may not be able to arrive at a conclusion, or even may produce incorrect recommendations. The development of web-based ES overcame some of these drawbacks. Geetha Abeysinghe Page 20 02/07/08

21 Now read Chapter 11, IT at Work 11.4: Even an Intelligent System can Fail, pages 482 of Turban et al. Think about the questions given at the end of the case study Some of the limitations such as the inability of ES to learn from experience have been overcome by combining ES with AI technologies such as neural networks and fuzzy logic. Developing and maintaining ES has been made easier by the sophisticated development tools available today. Hybrid AI Systems In order to reap the benefits of different AI technologies and also to overcome certain drawbacks of ES, both can be integrated into one application. Such systems are known as Hybrid AI systems. First we need to understand the different capabilities that could be provided by different AI technologies. Now read Chapter 11, Section 11.7, Other Intelligent Systems, pages of Turban et al. Make notes on your learning journal Now carry out Activity 2.8 (Ethical Issues arising from DSS and ES) (Learning Outcome: Identify the challenges posed by IS in the enterprise and management solutions) Geetha Abeysinghe Page 21 02/07/08

22 Activities Activity (ISs in an organisation) Read the opening case study. Building an e-business at FedEx Corporation, of Chapter 2 (pages 49-51) of Turban et al. and make a list of the information systems that you can recognise in the description and the purpose they serve in the organisation Back Activity (what are transaction processing systems) Find out the difference between e-commerce and e-business. What types of interactions are required between organisations which are engaged in e-commerce? Back Activity 2.3 (customer profiling) Read the Windows on Organizations on page 468 of Laudon and Laudon and answer the questions Back Activity 2.4 (Case Studies in DSS) Visit the website of MicroStrategy at who gives a number of case studies where their DSS software has brought success to their customers Information Advantage ( is another DSS provider. Their DecisionSuite has been used in retail. You can see a number of case studies. Back Activity 2.5 (What led to the growth of DSS) Using your knowledge about the nature of business today, advances in technology and telecommincations, changing busness models, changing organisational behaviour, etc. make a list of reasons you think led to the growth of GDSS. Back Geetha Abeysinghe Page 22 02/07/08

23 Activity 2.6 (GDSS) What factors affect the success of a GDSS session? Back Activity 2.7 (Applications of ES) Find out an example of an application of expert systems in the following areas: 1. telephone network maintenance 2. credit evaluation 3. medical diagnosis or Find any three applications of expert systems and identify the problem domain. For example, credit card fraud, user training, help desk, etc. Back Activity 2.8 (Ethical Issues arising from DSS and ES) Organisations that develop and use DSS and ES need to be aware of a number of ethical issues that may need to be addressed. Discuss some of the ethical issues that you need to be aware as a manager and a developer of DSS and ES. Back Geetha Abeysinghe Page 23 02/07/08