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 371 sive analysis of a single orlimited numberof observations. Eisenhardt (1989), Voss et al. (2002), Voss (2006), and Yin (1994) contain extensive discussions of why and how to conduct effective case research. Voss (2006) in particular directly considers the question of whether research based on a single observation makes sense. The next foursections will describe some examples of each of the four types of research. I want to emphasize that what follows is not intended to be a comprehensive survey of empirical research in operations management, but a few illustrative examples. Clearly, a great deal of other excellent work is omitted here because of space constraints. The reader will detect, and I hope forgive, a Wharton bias in this survey; it is not that this research is necessarily more meritorious, but that it was more familiar to me. 2. Engineering Engineers in industry and academia alike design and build things, then test theircreations, first in the lab and eventually in the field. In most engineering disciplines what gets built is a piece of hardware, but in our field the analog to hardware is usually the software implementation of an algorithm. Since the earliest days of operations research and management science, a core paradigm has been to model a real-world problem, devise an algorithm to analyze the model, create a software implementation of that algorithm, and then observe whether the algorithm improves the performance of the function that was modeled. Using company data to estimate the parameters of a model is a form of empiricism. Moreover, while it might seem that deploying an algorithm is the end, rather than the beginning, of research, I have found that so much is learned during the implementation process that this itself constitutes a type of empirical research. During implementation you are forced to verify and refine the details of your model, so you evolve a very precise definition of how a particular operations function works. You also discover properties of real data that influence algorithm design. Forexample, the application of the simplex method to real linear programming problems revealed that real problems have very sparse constraint matrices, a property that could be exploited in developing a more efficient algorithm. Finally, putting an algorithm into the hands of users teaches you how they think about problem solving. For example, most users want to see more than the final solution for a given data set. They want to see the logic path that leads from the input data to the final solution, and they want a sensitivity analysis that tells them whetherit is worthwhile forthem to take an action that might relax one of the constraints of the problem. Vehicle routing is one context where much has been learned from the deployment of algorithms. Vehicle routing focuses on the efficient use of a fleet of vehicles that must make a numberof stops to pick up and/ordeliverpassengers orproducts. The problem requires one to specify which deliveries or pickups should be accomplished by each vehicle and in what order so as to minimize total cost subject to a variety of constraints such as vehicle capacity and delivery time restrictions. Fisher (1995) surveys research in vehicle routing and describes a number of successful applications of vehicle-routing algorithms. Bartholdi et al. (1983) is one of my favorite vehicle-routing applications because of the novel algorithm used and the social contribution of its application. The authors describe the application of a routing system based on space-filling curves that they developed for the Atlanta, Georgia, branch of Meals-on-Wheels, which delivers hundreds of meals daily to those who cannot shop forthemselves. A space-filling curve is a mapping between a lower and a higherdimensional space. A space-filling curve from R 2 to R 1 induces a sequencing of all points in a plane and hence provides a heuristic for the planer traveling salesman problem. The authors use a particular space-filling curve to sequence all required deliveries for a given day. They then assign deliveries in orderto a vehicle until furtherassignment would exceed vehicle capacity. This process is repeated with successive vehicles until all deliveries have been assigned to a vehicles. The resulting algorithm is so simple that it was implemented on two Rolodex card files and shown to have significant positive benefit. As the result of decades of similar research in creating and applying vehicle-routing algorithms to particular real problems, we now have a rich understanding
Fisher: Strengthening the Empirical Base of Operations Management 372 Manufacturing & Service Operations Management 9(4), pp. 368–382, © 2007 INFORMS of the many nuances of vehicle routing that arise in practice, a taxonomy of the various versions of real vehicle routing problems, and a knowledge of vehiclerouting data structures. For example, Fisher (1994) observes that the delivery points in real vehiclerouting problems are highly clustered and discusses how to exploit this in an optimization algorithm. If the essence of empirical research is gathering and understanding information about the world, all of this clearly constitutes significant empirical research. Bowman (1963) presents an excellent example of learning through implementation. The paper was already a classic by the late 1960s when I was a graduate student, and it profoundly influenced me and many otherstudents at that time. Holt et al. (1955) describe decision rules to determine optimal monthly production, inventory, and workforce levels as linearfunctions of priorvalues of these variables and a demand forecast. Bowman (1983) applied these rules to ice cream, chocolate, and candy plants in three separate studies. He first used accounting data to derive accurate estimates of the coefficients required in the decision rules and obtained results that improved on existing practice. But, surprisingly, he got still better results by choosing these coefficients so the output of the decision rules most closely matched, on average, managers’ past decisions. Amazingly, rules fitted to managers’ prior decisions did better than the managers themselves! Bowman’s findings illustrate that one often makes interesting discoveries in the process of implementing an algorithm. Bowman’s discovery was a principle that he summarized as follows. Managers make good decisions on average, but they are hurt by variation in theirdecision making. Thus, “A decision rule with mean coefficients estimated from management’s behaviorshould be betterthan actual performance [of the managers]. It may also be better than a rule with coefficients supplied by traditional analysis” (p. 321). 3. Operations Management Econometrics Many fields apply regression and other statistical analysis tools to data sets in an attempt to discover evidence to support various hypotheses. In fact, this is the type of research that most people in our field associate with the term empirical research. Without question, the International Motor Vehicle Program study of 70 automobile assembly plants worldwide is the “mother” of all operations management econometrics research efforts. This ambitious study, which began at MIT. in the late 1980s and continues to this day, seeks to discoverand validate management practices associated with high levels of quality and productivity in an automobile assembly plant. The average numberof defects pervehicle as measured by the J. D. Powers survey that tests most car models is used as the measure of quality. Productivity is equated to the numberof hours to assemble a vehicle, a metric tracked by most automobile plants. To compare two different plants, this metric is adjusted to normalize for differences in vehicle complexity. The study 2 found large differences in quality and productivity between plants. Moreover, those plants with the highest productivity also tended to have the highest quality, contradicting the conventional wisdom that productivity and quality trade off against each other. High performance was correlated with certain practices that have come to be called “lean production” and include just-in-time inventory management and a high degree of worker involvement. Although these practices are typically associated with Japanese manufacturers, it was shown that this is not merely a Japanese effect; there were a significant number of poorly performing plants within Japan that did not follow lean production principles and an equally significant numberof highly performing plants outside of Japan that did follow lean production principles. The automobile industry has proven to be a fertile context forthe application of econometric methods. Anotherhighly successful example is Clark and Fujimoto (1991), which reports results of an extensive study of product development in the auto industry. The authors examined a large number of new model development projects at 20 automobile manufacturers worldwide to understand the management practices that influenced design quality, product development time, and product development productivity as measured by the engineering hours required by a project. 2 The results of this ongoing study have been published in many papers. Early work can be found in Krafcik (1988) and MacDuffie (1991), with more recent results in MacDuffie et al. (1996).
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