THE EVOLUTION OF SUPPORT SYSTEMS

Transcription

1 ONLINE FILE W2.1 THE EVOLUTION OF SUPPORT SYSTEMS The first computers were designed to compute formulas for scientific and military applications during World War II. The first business applications began in the early 1950s, and the computers did repetitive, large-volume, transactionscomputing tasks. The computers crunched numbers, summarizing and organizing transactions and data in the accounting, finance, and human resources areas. The cost of these systems was easy to justify since they automated manual computations and saved labor. As the cost of computing decreased and computers capabilities increased, it became possible to justify IT for less-repetitive tasks. In the 1960s, a new breed of information system started to develop. These systems accessed, organized, summarized, and displayed information for supporting routine decision making in the functional areas. Such systems are called management information systems (MISs). Geared toward middle managers, MISs are characterized mainly by their ability to produce periodic reports such as a daily list of employees and the hours they work, or a monthly report of expenses as compared to a budget (see Section 2.3 and Chapter 7 for details). Initially, MISs had only an historical orientation: They described events after they occurred. Later, MISs were developed also to forecast trends, to support routine decisions, and to provide answers to queries. Other support systems began to emerge in the late 1960s and early 1970s when networked computing became prevalent. Airline reservation systems are perhaps the best example of this development. Electronic communication is only one aspect of what is now known as an office automation system (OAS). Another aspect, word processing systems, spread to many organizations in the 1970s and 1980s. At about the same time, computers were introduced in the manufacturing environment, with applications ranging from robotics to computeraided design and manufacturing (CAD/CAM) (see Chapter 7). By the early 1970s, the demand for all types of information technology had begun to accelerate. Increased capabilities and reduced costs justified computerized support for a growing number of nonroutine applications, and the idea of a decision support system (DSS) was born. The basic objective of a DSS is to provide computerized support for complex, nonroutine decisions (see Chapter 12 for details and also IT at Work W2.1.1, about Glaxo). At first, the high cost of building DSSs constrained their widespread use. However, the microcomputer revolution, which started around 1980, changed that. The availability of desktop computers, which were easily programmable, made it possible for a person who knows little about programming to build personal DSS applications. This was the beginning of the era of end-user computing, in which analysts, managers, and many other professionals are building their own systems. Decision support expanded in two directions. First, executive support systems (ESSs) were designed to support senior executives. These systems were then expanded to support other managers within the enterprise (see Chapter 12). Later these systems became enterprise (or enterprisewide) information systems, supporting all managers, knowledge workers and others in the enterprise. W22

2 W23 IT at Work W2.1.1 GLAXO WELLCOME SAVES LIVES WITH DSS POM Glaxo Wellcome of the United Kingdom, a subsidiary of GlaxoSmithKline (uk.gsk.com), is one of the largest pharmaceutical companies in the world. In 1996, the company found that a combination of two of its existing drugs, Epivir and Retrovir, were effective in treating some cases of AIDS. Almost overnight, doctors worldwide began writing prescriptions en masse. Such a tidal wave of demand depleted the inventories of the two drugs in the pharmacies. Glaxo needed to produce and ship Epivir and Retrovir quickly. But the increased demand, which is used to determine production, shipping schedules, and inventory levels, was too difficult to forecast. To solve the problem, Glaxo developed a special enterprisewide networked information system based on online analytical processing technology (see Chapter 12). Essentially, this system processes data as soon as transactions occur using special formulas. The system works with a vast amount of internal and external data stored in a data reservoir called data warehouse (Chapter 11). Using these data and DSS mathematical models, market analysts at Glaxo were able to track and size the sources of demand, generating summary reports and projections in minutes. The projected demand was inputted into other DSS models that figure appropriate production plans, delivery schedules, and inventory levels along the supply chain. As a result, Glaxo streamlined its distribution process so wholesalers and retailers around the world seldom ran out of the drugs. An added benefit was that operational costs were reduced. Also, the system provided Glaxo s employees with a tool that allows them to quickly and easily access information from different sources that is now stored in one place. In addition, the network allows for efficient internal and external collaboration and communication. Finally, the IT solution enabled the company to maximize the business opportunity and to save lives in the process. The system was used in several other situations to support production, inventory and related decisions of several other drugs. In 2000 the Glaxo DSS was transformed to Web-based, and it is used extensively throughout the corporation today. Sources: Compiled from Friar (1996), from uk.gsk.com, and from microstrategy (accessed April 2001). For Further Exploration: Why was a DSS needed in this case, and why is quick data consolidation so important? The second generation of EISs supports people working in groups. Group support systems (GSSs) initially supported people working in a special decisionmaking room. They also expanded, due to network computing, to support people working together but in different locations (virtual groups, see Chapter 12). By the mid-1980s, the commercialization of managerial applications of artificial intelligence began. Essentially, artificial intelligence (AI) is concerned with programming computers to perform intelligent problem solving. The commercial applications of AI are known as intelligent support systems (ISSs). One application of AI that is of special interest to management is expert systems (ESs), which provide the stored knowledge of experts to nonexperts, so the latter can solve difficult or time-consuming problems. These advisory systems differ from TPS, which centered on data, and from MIS and DSS, which concentrated on processing information. With DSS, users make their decisions according to the information generated from the systems. With ES, the system makes recommended decisions for the users based on the built-in expertise and knowledge, as shown in Online A Closer Look

3 W24 A CLOSER LOOK W2.1.1 EMBEDDED LOGIC Since the early 1990s, expert systems have become so integrated that they have turned into parts of processes. Vendors sell them embedded in various products but seldom mention their presence. Programs are sold based on their functionality for a given application, not on whether they are expert. The new robot mind becomes just part of the plumbing. This self-effacement may have delivered expert systems to their true role, that of Web-based interfaces to corporate data resources. Expert systems can personalize inquiries to the nth degree, says Tod Loofborrow, president and CEO of Authoira, (authoria.com), a software development company specializing in human resources and health-care information, whose systems let employees explore their benefits policies on the Web. Akeel Al-Attar, of Attar Software (attar.com) thinks expert systems are a natural support system for e-commerce, since consumer interviews are rule bound and require much back-and-forth communication with corporate databases. He gives as an example a Japanese pump manufacturer, Ebara Manufacturing, which produces several thousand kinds of pumps for many industries. Traditionally, when customers would just ask for a pump, sales personnel had to figure out what kind was needed. As products got more numerous and sophisticated, this manual system started to break down. Ebara fixed this problem with an online expert system. The system leads customers through a series of questions that connect their needs to specific products, often in less than a minute. Al-Attar points out a subtle edge enjoyed by this new generation of outward-facing expert systems (systems that connect an organization to external entities). E- commerce applications connect to the outside world and bring in revenue. While it might be true that a saved dollar contains the same number of pennies as an earned dollar, technologies that make money tend to get more front-office attention. In the end, this may prove to be an even better marketing story than capturing the wisdom of real experts. Sources: Extracted from Hapgood (May 2000); updated March 2003 at attar.com.

4 W25 Knowledge work systems (KWSs) are designed specifically to aid professional and managerial activities by supporting the creating, gathering, organizing, integrating, and disseminating of an organization s knowledge (see Chapter 10). The software that supports these systems connects (1) people to documents and information and (2) people to people. It captures information and converts it into new knowledge that propels the enterprise forward toward its goals. Examples of knowledge work software are: Document management systems (DMSs) that automate the flow of electronic documents through their entire life cycle. DMSs retain an image of the documents, create an index of key words, may translate the image to computer readable format, and control the distribution (see Chapter 11). Workflow software, authoring tools, and scanners support document management. Word processing systems are the familiar software, such as Microsoft Word, and supporting hardware that help in creating, editing, combining, formatting, savings, distributing, and printing documents. Desktop publishing systems produce professional-quality documents, combining text, photos, and graphics. A major trend now is doing Web publishing, that is, publishing Web pages, e-zines (electronic magazines), and blogs (Web logs); for more, see Chapter 4. All of the above systems are very beneficial, but their support is fairly passive. Even expert systems are unable to learn from experience. By the beginning of the 1990s, a new breed of intelligent systems with machine learning capabilities emerged. These systems, such as artificial neural networks (ANNs), case-based reasoning, and genetic algorithms, can learn from historical cases (see Chapter 12). This capability enables machines to process vague or incomplete information, as shown in IT at Work W2.1.2.

5 W26 IT at Work W2.1.2 DETECTING BOMBS IN AIRLINE PASSENGERS LUGGAGE The Federal Aviation Administration (FAA) in the United States is making continuous efforts to improve safety and prevent terrorists from sneaking bombs aboard airplanes. Since it is practically impossible to open and search every piece of luggage, the FAA uses computer technologies in an attempt to find different types of explosives or other weapons. One approach is to bombard each piece of luggage with gamma rays that are collected by a sensor and then interpreted. The FAA is using statistical analysis and expert systems to conduct the interpretation. However, these technologies cannot detect all types of explosives. Since 1993, artificial neural networks have been added to improve detection effectiveness. The artificial neural network (ANN) is exposed to a training set; that is, it is shown pictures of historical cases, obtained by gamma rays. It is also told whether each specific piece of luggage contained an explosive or not. Once trained, the system is used to predict the existence of explosives in new cases. It can detect an explosive even if the explosive device is somewhat different from those used for training. The objective is not only to detect explosives successfully, but also to minimize false alarms caused by the fact that GOV many things (including clothing) contain nitrogen, a major component of bombs. Since the terrorist attacks of 9/11, airport security has been beefed up with the help of intelligent systems. In addition to large-baggage scanning, there are systems for scanning carry-on baggage. Even cargo containers and pallets are scanned by intelligent systems. Finally, walkthrough and hand-held metal detectors use multiple sensor-based intelligent systems to increase our safety and security. Sources: Informal communication from Scan-Tech Security, Northvale, NJ (a developer of one of these systems), 1998; from bomb detection.com (accessed February 25, 2003); from spysite.com/bombs_ weapons_detection.html; and from security_sa.com. For Further Exploration: It is said that two heads are better than one. Can the addition of ANN be considered an extra head? Why?