Case Study
Making Elevators Go Faster!
This story has been reported in numerous places and has almost become a classic example to explain the need for problem identification. Ackoff (as cited in Larson, 1987) described the problem of managing complaints about slow elevators in a tall hotel tower. After trying many solutions for reducing the complaint—staggering elevators to go to different floors, adding operators, and so on—the management determined that the real problem was not about the actual waiting time but rather the perceived waiting time. So the solution was to install full-length mirrors on elevator doors on each floor. As Hesse and Woolsey (1975) put it, “The women would look at themselves in the mirrors and make adjustments, while the men would look at the women, and before they knew it, the elevator was there.” By reducing the perceived waiting time, the problem went away. Baker and Cameron (1996)give several other examples of distractions, including lighting and displays, that organizations use to reduce perceived waiting time. If the real problem is identified as perceived waiting time, it can make a big difference in the proposed solutions and their costs. For example, full-length mirrors probably cost a whole lot less than adding an elevator!
Sources: Based on J. Baker and M. Cameron. (1996, September).
“The Effects of the Service Environment on Affect and Consumer Perception of Waiting Time: An Integrative Review and Research Propositions,” Journal of the Academy of Marketing Science, 24, pp. 338–349; R. Hesse and G. Woolsey (1975). Applied Management Science: A Quick and Dirty Approach. Chicago, IL: SRA Inc; R. C. Larson. (1987, November/December). “Perspectives on Queues: Social Justice and the Psychology of Queuing.” Operations Research, 35(6), pp. 895–905.
Questions for Case 1.1
1. Why this is an example relevant to decision making?
2. Relate this situation to the intelligence phase of
DECISION-MAKING PROCESSES AND COMPUTERIZED DECISION SUPPORT FRAMEWORK
In this section, we focus on some classical decision-making fundamentals and in more detail on the decision-making process. These two concepts will help us ground much of what we will learn in terms of analytics, data science, and artificial intelligence. Decision making is a process of choosing among two or more alternative courses of action for the purpose of attaining one or more goals. According to Simon (1977), managerial decision making is synonymous with the entire management process. Consider the important managerial function of planning. Planning involves a series of decisions: What should be done? When? Where? Why? How? By whom? Managers set goals, or plan; hence, planning implies decision making. Other managerial functions, such as organizing and controlling, also involve decision making. Simon’s Process: Intelligence, Design, and Choice It is advisable to follow a systematic decision-making process. Simon (1977) said that this involves three major phases: intelligence, design, and choice. He later added a fourth phase: implementation. Monitoring can be considered a fifth phase—a form of feedback. However, we view monitoring as the intelligence phase applied to the implementation phase. Simon’s model is the most concise and yet complete characterization of rational decision making. A conceptual picture of the decision-making process is shown in Figure 1.1. It is also illustrated as a decision support approach using modeling. There is a continuous flow of activity from intelligence to design to choice (see the solid lines in Figure 1.1), but at any phase, there may be a return to a previous phase (feedback). Modeling is an essential part of this process. The seemingly chaotic nature of following a haphazard path from problem discovery to solution via decision making can be explained by these feedback loops.
The decision-making process starts with the intelligence phase; in this phase, the decision maker examines reality and identifies and defines the problem. Problem ownership is established as well. In the design phase, a model that represents the system is constructed. This is done by making assumptions that simplify reality and by writing down the relationships among all the variables. The model is then validated, and criteria are determined in a principle of choice for evaluation of the alternative courses of action that are identified. Often, the process of model development identifies alternative solutions and vice versa. The choice phase includes the selection of a proposed solution to the model (not necessarily to the problem it represents). This solution is tested to determine its viability. When the proposed solution seems reasonable, we are ready for the last phase: implementation of the decision (not necessarily of a system). Successful implementation results in solving the real problem. Failure leads to a return to an earlier phase of the process. In fact, we can return to an earlier phase during any of the latter three phases. The decisionmaking situations described in the opening vignette follow Simon’s four-phase model, as do almost all other decision-making situations. The Intelligence Phase: Problem (or Opportunity) Identification The intelligence phase begins with the identification of organizational goals and objectives related to an issue of concern (e.g., inventory management, job selection, lack of or incorrect Web presence) and determination of whether they are being met. Problems occur because of dissatisfaction with the status quo. Dissatisfaction is the result of a difference between what people desire (or expect) and what is occurring. In this first phase, a decision maker attempts to determine whether a problem exists, identify its symptoms, determine its magnitude, and explicitly define it. Often, what is described as a problem (e.g., excessive costs) may be only a symptom (i.e., measure) of a problem (e.g., improper inventory levels). Because real-world problems are usually complicated by many interrelated factors, it is sometimes difficult to distinguish between the symptoms and the real problem. New opportunities and problems certainly may be uncovered while investigating the causes of symptoms. The existence of a problem can be determined by monitoring and analyzing the organization’s productivity level. The measurement of productivity and the construction of a model are based on real data. The collection of data and the estimation of future data are among the most difficult steps in the analysis. ISSUES IN DATA COLLECTION The following are some issues that may arise during data collection and estimation and thus plague decision makers:
• Data are not available. As a result, the model is made with and relies on potentially
inaccurate estimates.
• Obtaining data may be expensive.
• Data may not be accurate or precise enough.
• Data estimation is often subjective.
• Data may be insecure.
• Important data that influence the results may be qualitative (soft).
• There may be too many data (i.e., information overload).
• Outcomes (or results) may occur over an extended period. As a result, revenues, expenses, and profits will be recorded at different points in time. To overcome this difficulty, a present-value approach can be used if the results are quantifiable.
• It is assumed that future data will be similar to historical data. If this is not the case, the nature of the change has to be predicted and included in the analysis. When the preliminary investigation is completed, it is possible to determine whether a problem really exists, where it is located, and how significant it is. A key issue is whether an information system is reporting a problem or only the symptoms of a problem. For example, if reports indicate that sales are down, there is a problem, but the situation, no doubt, is symptomatic of the problem. It is critical to know the real problem. Sometimes it may be a problem of perception, incentive mismatch, or organizational processes rather than a poor decision model.
Nice information..