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- Σύνοψη του βιβλίου "Advances in Enterprise Information Systems II"
- Special Issue "Selected Papers from ICEIS 2018: Advances in Enterprise Information Systems"
These seven systems represent a wide range of approaches in supporting decisions. The first one helps production foremen by simply providing rapid access to historical information such as who worked on what lot, and when the work was done. But the foremen must decide what should be done once they have the information. At the other extreme, the system supporting the underwriters virtually makes the decision in some cases. Between the two extremes, analysis systems and model-oriented systems help people organize information and also facilitate and formalize the evaluation of proposed decisions.
Although managers in most large companies have used budgeting or planning systems similar to the source-and-application-of-funds model I mentioned, the spectrum of possibilities for other kinds of decision support systems is surprisingly wide. Obviously, some of these systems are of no particular use in many settings. Still, their variety suggests that most companies should have a number of genuine opportunities for applying the concept of computerbased support for decision making.
What do decision support systems do that actually helps their users? What is their real impact? In my survey, answers to these questions proved elusive in many cases since the users valued the systems for reasons that were completely different from initial ideas of what the systems were to accomplish. In fact, a wide range of purposes exists for these systems. While many decision support systems share the goals of standard EDP systems, they go further and address other managerial concerns such as improving interpersonal communication, facilitating problem solving, fostering individual learning, and increasing organizational control.
Such systems can affect interpersonal communication in two ways: by providing individuals with tools for persuasion and by providing organizations with a vocabulary and a discipline which facilitates negotiations across subunit boundaries. Standard texts on systems analysis totally overlook the personal use of decision support systems as tools of persuasion.
At one point, it occurred to the plant manager that he could use this model to investigate whether marketing was setting goals that resulted in poor plant utilization and made him appear inefficient. Although the merger was not approved, management thought that the system helped it put up a good fight.
If you the budget cutter would like me to change these assumptions, I would be glad to generate a new budget.
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What level of shortfall do you suggest? A cynic might contend that the people in these situations were taking advantage of or abusing the systems.
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My point is that much of the benefit of many of the decision support systems in my sample was of this sort. Decision support systems also help managers negotiate across organizational units by standardizing the mechanics of the process and by providing a common conceptual basis for decision making. During my survey, managers frequently commented that consistent definitions and formats are important aids to communication, especially between people in different organizational units such as divisions or departments.
In a number of instances, the development of these definitions and formats was a lengthy and sometimes arduous task that was accomplished gradually over the course of several years, but which was also considered one of the main contributions of the systems. For example, one of the purposes of some of the model-oriented systems in my sample was to estimate beforehand the overall result of decisions various people were considering separately, by filtering these decisions through a single model. In these cases, the system became an implicit arbiter between differing goals of various departments.
As a result, issues were clarified and the negotiation process expedited. The production foremen I mentioned earlier noted the same kind of facilitation. Monetary savings are obviously a very important and worthwhile rationale for developing computer systems, but it should be clear at this point that the EDP-style assumption that systems should always be justified in these terms does not suffice in the area of decision support systems.
Equally obvious, there is a definite danger in developing a system simply because someone thinks it makes sense, especially if that someone is not the direct user of the system.
In fact, the systems I cited as my first, second, and fifth examples began this way and encountered resistance until they were repositioned as something that users would want in order to become more effective. This sort of overoptimism was present in the history of almost every unsuccessful system in the sample. The message is clear: try to take advantage of the creativity of technical experts, but be sure that it is channeled toward real problems. The challenge, of course, is how to accomplish both of these goals.
There are a number of ways, which I shall now discuss. Despite the common wisdom that the needs of users must be considered in developing systems and that users should participate actively in implementing them, the users did not initiate 31 of the 56 systems I studied and did not participate actively in the development of 38 of the The results, illustrated in Exhibit II, are not surprising.
Intended users neither initiated nor played an active role in implementing 11 of the 15 systems that suffered significant implementation problems. But it would be wrong to infer from these findings that systems should be avoided totally, if intended users neither initiate them nor play an active role in their implementation.
Σύνοψη του βιβλίου "Advances in Enterprise Information Systems II"
For one thing, 14 of the 25 systems I studied in which this was the pattern were ultimately successful. More important, many of the genuinely innovative systems in my sample, including 5 of the 7 that I described earlier, exhibited this pattern. On the other hand, many of the systems initiated by users do little more than mechanize existing practices. One way to do this is to devise an implementation strategy to encourage user involvement and participation throughout the development of the systems regardless of who originated the concept. Examples of successful strategies follow.
Impose gracefully: Marketing and production managers in a decentralized company did not relish the extra work format changes and data submission requirements needed for a yearly budgeting system, which top management was installing. Initially, they were especially unenthusiastic because they thought the system would not really help them.
So at every stage the designers made a point of developing subsystems to provide these middle managers with sales and materials usage information that had never been available.
This quid pro quo worked well; instead of seeing the system as a total imposition, the manager saw it as an opportunity for them to take part in something which would be beneficial to them. Run a dog and pony show: Central planning personnel in two companies designed systems for budgeting and financial analysis. In one company, the system never caught on despite lengthy training demonstrations for divisional staff and other potential users. In contrast, the training program for the system in the other company fostered immediate and active involvement.
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In order to attend the workshops, people were required to bring their own financial analysis problems. They learned to use the system by working on these problems.
Special Issue "Selected Papers from ICEIS 2018: Advances in Enterprise Information Systems"
When the workshops ended, many users were enthusiastic: not only did they know how to use the system, but they had also proved to themselves that it could help them. Use a prototype: Two ever-present dangers in developing a system are creating a large, expensive one that solves the wrong problem or creating one that some people in the organization cannot live with. Either can happen, not only when the system is designed without consulting the user and affected parties, but also when there is no one having enough experience with the particular kind of system under consideration to clearly visualize its strengths and weaknesses before it is built.
Implementers of a number of systems in my sample avoided these traps by building small prototypes, which gave the users something specific to react to. As a result, the large-scale version could be developed with a realistic notion of both what was needed and what would fly in the organization. A similar approach, also successful, was simply to build systems in small pieces that could be used, changed, or discarded easily. Hook the user with the responsibility: Each new module or application developed as an outgrowth of one of the three sales information systems I mentioned earlier goes through three stages.
The first stage consists of general, uncommitted discussions of any current problem areas with which user groups are concerned. Following research by the management science staff, the second stage is a brief formal problem statement written in conjunction with the user group. In addition to describing the problem, this statement goes over the methodology and resources that will be required to respond to it. The third stage is a formal request for authorization of out-of-pocket expenses.
Sell the system: In one of the companies I studied, a marketing analysis group used a direct selling procedure to convince people of the merits of a sales forecasting system. The system was adopted. In another company, management had a real-time system installed for monitoring the largely automatic production of an inexpensive consumer item in order to minimize material loss due to creeping maladjustments in machine settings.
During the initial installation, the implementation team discovered suspected, but previously unsubstantiated, cheating by piecework employees; more pieces were leaving many machines than were entering them. The employees were sold on the new system: they knew very well that it worked. Despite extensive experience with EDP, many organizations have used no more than one or two of the seven types of decision support systems I have illustrated here.
One reason for this is that justifying such systems can be difficult: quantifying the impact of replacing ten clerks with one computer is one thing, while quantifying the impact of improved individual effectiveness of line personnel is quite a different thing. Another reason is that implementation can be tricky: many of the ideas come from people other than the users. Nevertheless, developing a decision support system makes sense when it becomes clear that a fundamental change may be needed in the way decisions are reached and implemented.
Often, the process of defining the system is every bit as valuable as the system produced. My final point is that the concept of decision support systems itself can help managers in understanding the role of computers in their organizations. Artificial Intelligence and Decision Support Systems 3. Information Systems Analysis and Specification 4. Software Agents and Internet Computing 5. Human—Computer Interactions 6.