Strengthening the Empirical Base of Operations Management

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Fisher: Strengthening the Empirical Base of Operations Management Manufacturing & Service Operations Management 9(4), pp. 368–382, © 2007 INFORMS 379 context of a mythical beersupply chain. Many other studies have placed students in supply chain-related decision contexts to understand the human side of supply chain decision making. Schweitzerand Cachon (2000) provide a good example. Students were asked to make various types of newsvendor decisions to understand how and why their decisions differed from those recommended by the standard newsvendorformula. It is harder to convince a company to use itself as a laboratory, and it is therefore noteworthy that some researchers have succeeded in doing this. The application of a model and algorithm within a company is a kind of experiment, and I would regard the algorithm application examples in Bartholdi et al. (1983) and Bowman (1963) as examples of experiments within organizations. Burchill and Fine (1997) persuaded a numberof companies to test a process the authors had developed forconcept engineering and to compare results using the new process versus their standard process. Lapre and Van Wassenhove (2001) worked with N. V. Bekaert, S.A. to conduct experiments using one of its production lines to learn the impact on productivity of various production parameters. The results of these experiments are credited with enabling huge production improvements. 8. Approaches to Conducting Empirical Research: Navigating the Matrix I have found two strategies to be particularly effective in conducting empirical research. Both involve conducting various types of empirical research corresponding to different cells of the matrix (Figure 1). I have always found it useful when contemplating research on a topic to start in the lower-right cell with discussions with one ormore companies. These discussions enable a deeperunderstanding of an issue so that subsequent research can be guided by better questions. It may make sense to write a case on one of the companies with which one is interacting, which is a useful way to further deepen one’s understanding of issues. Sometimes, one will find a single company that has a well-defined problem that can be modeled and analyzed, so we start in the lower-right unstructured descriptive cell and move to the upper-left structured Figure 4 Navigating Matrix Cells—Approach 1 Interaction with the world Highly structured: Data and algorithms Less structured: Interviews and observations Goal of the research Prescriptive Descriptive Engineering Software implementation of algorithm deployed in a company and run daily Principles Ohno sees U.S. supermarket and invents Toyota Production System Operations management econometrics Statistical analysis of large data sets to discover drivers of success in operations Start here Case studies Interview and observe managers Research cases prescriptive cell, following the path shown in Figure 4. Many traditional projects that apply operations research/management science to a particular problem follow this path. Another approach is illustrated in Figure 5. We start by interacting with a group of companies to form some hypotheses, then assemble a data set from an expanded set of companies to test the hypotheses, and finally use the validated hypotheses to design an agenda forimprovement. The research reported in Fisher et al. (1999) and Ramdas et al. (2003) followed this path forthe issue of the best way to share parts across various finished products. We started in the lower-right cell by engaging in discussions with executives at several auto companies to understand how they thought about this issue. We learned that they viewed designing cars to maximize shared components as an important tool Figure 5 Navigating Matrix Cells—Approach 2 Interaction with the world Highly structured: Data and algorithms Less structured: Interviews and observations Prescriptive Goal of the research Descriptive Engineering Operations management econometrics Software implementation Statistical analysis of of algorithm deployed in large data sets to a company and run daily discover drivers of success in operations Optimization Validation Hypotheses Start here Principles Case studies Ohno sees U.S. Interview and observe supermarket and managers invents Toyota Production System Research cases
Fisher: Strengthening the Empirical Base of Operations Management 380 Manufacturing & Service Operations Management 9(4), pp. 368–382, © 2007 INFORMS for providing maximum variety to customers while retaining less variety in plants. We also learned that many functional components could be rank ordered on a single quality metric. Brakes are an example, where the metric is stopping power. One could support an entire product line with a single brake type if it had the powerto stop the heaviest carin the line within a requisite distance, but the per unit production cost of this brake would be inordinately high for smaller cars. Conversely, you could minimize per unit production costs by having a unique brake for each car, but this would result in high plant complexity costs. We then sought a principle that would guide our subsequent research and showed, for a stylized version of the problem, that the number of brakes that minimizes perunit production costs and the cost of plant complexity from a greater number of brakes could be found via a model that resembled the economic order quantity model. From this we hypothesized that the numberof brakes an auto company would create to support a given product line would depend on factors such as the number of cars in the line, the variance in weight of those cars, and their production volumes. We then moved to the upperright cell to verify these hypotheses by analyzing a database assembled fora large numberof auto companies using public data on auto specifications and production volumes and data provided as a service to salvage yards that showed commonality of brakes across cars. Finally, we moved to the upper-left cell by formulating and analyzing a model for determining the optimal number and type of brakes to support a defined product line. The approaches to research outlined here might be contrasted with a more common one of reading a paperin a journal and identifying a variant of it to be analyzed. While much good research falls in this category—and it’s fine to have this as part of a portfolio, if that’s all we do—we would be at risk, in von Neuman’s words, to “separate into a multitude of insignificant branches.” 9. Conclusions and Some Suggested Action Steps I have suggested that the field of operations management can benefit from strengthening its empirical dimension. As evidence, I have offered the examples of physics, medicine, and finance, all of which have a strong empirical tradition and are prospering. Moreover, these fields provide role models for how empirical research should be conducted, which I have attempted to summarize in this paper. Some advantages of a strong empirical component to our research include the following: 1. Identifying and verifying important phenomena 2. Identifying and characterizing important questions on which we can do useful research 3. Validating models and assumptions that we have made 4. Establishing the relevance of our research by demonstrating how the research outputs apply to practice. If you agree with these assertions, then a natural question is what action steps should be taken. We can separate actions into those to be taken by individuals, academic departments, and professional societies. As an individual, you could, I hope, considereither adding an empirical component to your own research portfolio orcontinuing yourempirical research if you already do it. Academic departments can considerdevoting some of theirhiring slots to faculty doing empirical research, introducing courses on empirical research into their PhD programs (the PhD course Empirical Research in Operations Management designed and taught by Christian Terwiesch in the Operations and Information Management Department at the Wharton School is one example) and giving PhD students a clinical experience via working on research projects within companies. A more ambitious goal would be to create more institutionalized opportunities for a clinical experience that might constitute a “teaching hospital” foroperations management. Some interesting initiatives in this direction are the internships provided within the MIT Leaders for Manufacturing program and the University of Michigan Manufacturing Applications Project program, in which faculty work with students on field projects with companies. There is also a huge leadership opportunity for professional societies such as the Institute forOperations Research and the Management Sciences (INFORMS) and the Production and Operations Management Society (POMS). Clearly, publication of empirical research
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