Whenever possible, organizations strive to automate certain types of decisions. Automating decisions can be much more efficient, since you are essentially allowing a computer program to make a decision that was previously made by a human being. In addition, automating decisions can lead to more consistent and objective decisions being reached.
Earlier in this chapter, we discussed the issue of the degree of structure for decision situations. Basically, the more highly structured the decision situation, the easier it is to automate it. If it is possible to derive an algorithm that can be used to reach an effective decision, and the data that are needed as input to the algorithm can be obtained at a reasonable cost, then it typically makes sense to automate the decision.
For example, the chair assembly company discussed previously might find it possible to automate the decision as to when to request the transportation of a pallet of parts from the parts storage area to the assembly line. This could be done by scanning not only the parts that are used in the chair assembly, but also any defective parts (which could be tagged as defective and noted as such in the database through a touch-screen monitor, keyboard or mouse at a workstation on the assembly line). By monitoring all parts that are removed from the temporary storage on the assembly line, the information system could determine when a pre-determined re-order level has been reached, and issue a request to the next available fork-lift operator to deliver the needed parts to the assembly line.
Davenport and Harris (2005) offered a framework for categorizing applications that are being used for automating decisions. Most of the systems that they describe include some type of expert system, often combined with aspects of DSS, GDSS, and/or EIS. The categories they provided include:
Solution configuration– these systems are employed to help users (either internal staff or customers) work through complex sets of options and features to select a final product or service that most closely meets their needs. Examples might include configuring a large or medium-sized computer system or selecting among a wide variety of cellular telephone service plans. The underlying computer programs would involve a type of expert system, including a set of decision rules that have been obtained from experts from the decision context.
Yield optimization – describe systems which use variable-pricing models to help improve the financial performance of a company, or to try and modify the behavior of people in some way. One example would be an airline, where 10 different people on the same flight might pay 10 different amounts for their tickets, depending on when they purchased the ticket, how frequently they fly with that airline, how full the flight is when they book their ticket, and so on.
Routing or segmentation decisions– these systems perform a type of triage for handling incoming requests for information or services. Examples include systems that balance the loads on Internet Web servers by routing requests to different servers, or systems for insurance companies that handle routine insurance claim requests and only route exceptional (unusual) requests to human claims processors.
Corporate or regulatory compliance– these systems ensure that an organization is complying with all internal or external policies in a consistent manner. For example, mortgage companies that want to sell mortgages on the secondary market have to ensure that they comply with all of the rules of that market when they are preparing the original mortgage. Similarly, insurance companies have to comply with federal and state regulations when writing insurance policies.
Fraud detection – these systems provide a mechanism for monitoring transactions and noting possible fraudulent ones. The approach used might be very simple, such as checking for a credit card’s security code (in addition to the credit-card number, to prove the person physically has the card in their possession). Other approaches can be quite sophisticated, such as checking for purchases that seem to be out-of-character for the credit card holder (based on past purchasing history). By automatically identifying potentially fraudulent transactions, and then having a human operator contact the card holder to verify the transaction, the credit card company can reduce fraud losses and increase their customers’ satisfaction.
Dynamic forecasting– organizations all along a supply chain can decrease their costs of operations by reducing the amount of product (raw materials, work-in-process, finished goods) that they hold in inventory. Dynamic forecasting systems (that use historical sales data, etc.) help manufacturing companies align their customers’ forecasts with their own internal plans. This in turn helps them to reduce their inventory carrying costs and make more efficient use of their production resources (facilities and people).
Operational control– these systems monitor some aspect of the physical environment (such as wind speed or rainfall amount) or some type of physical infrastructure (such as an electrical power grid or a communications network). If an unusual event occurs (such as a sudden surge in electrical power at one point in the electrical grid), the system automatically performs some type of action (such as shutting down some nodes and re-directing power over others).
Many management decision situations are not highly structured, however, and hence can not (or should not) be completely automated. Next we describe systems that are designed to support decision making, rather than automate it.
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