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 375 rework, and scrap. We could continue to add inspectors, steadily reducing the defect rate while increasing cost and thus moving along a cost-quality tradeoff curve. On the otherhand, if we identify one ormore root causes of defects and modify the process to remove these root causes, then the process will produce fewerdefective units so we will both lowerthe defect rate and reduce cost because of reduced rework and scrap. Total cost will be reduced as long as the cost of diagnosing and fixing root causes of defects is less than the savings in rework and scrap expenses. Total Quality Management (TQM) wisdom suggests sorting root causes on the number of product defects they cause and fixing first those root causes responsible for the most product defects. If this approach is followed, we will likely improve quality and reduce cost as we attack the most serious process defects initially; eventually we will be fixing process defects that cause so few product defects that the savings in rework and scrap will be less than the cost of process improvement—then improved quality comes at the expense of increased cost. Several authors have suggested principles that align with these observations. Clark (1996), Hayes and Pisano (1996), and Porter (1996) observe that most companies operate off the efficient frontier between quality and cost and are therefore initially able to improve on both dimensions. But eventually they reach the frontier and face a tradeoff between cost and quality. Ferdows and DeMeyer (1990) suggest that companies should and do give priority to quality over cost. If they are off the efficient frontier, then they improve both quality and cost but give higher priority to quality improvement. If they face a tradeoff between quality and cost, then the improved quality is at the expense of increased cost. Once they reach a position of high quality, they may then focus on cost reduction and eventually move to a new efficient frontier in which both quality and cost are improved. Lapre and Scudder (2004) examine these hypotheses within the context of the airline industry using public data collected and reported by the U.S. Department of Transportation (DOT). Their quality metric is the numberof customercomplaints made to the DOT per100,000 passengers, theircost measure is cost per seat-mile flown, and theirasset utilization measure is fleet utilization, the average percentage of the time in a 24-hourday that a plane was available forservice that the plane was in active use; a period of active use is defined from when the plane first moves under its own power from the boarding ramp at the departure airport until it comes to rest at the ramp for the destination airport. Using DOT data, these three variables were tabulated for each of the 11 years from 1988–1998 forthe 10 majorairlines operating during this time: Alaska, America West, American, Continental, Delta, Northwest, Southwest, TWA, United, and U.S. Airways. The study qualitatively examined the cost-quality path followed by each airline over the 11 years and concluded that when an airline was forced to make a tradeoff between quality and cost, it generally elected first to improve quality at the expense of cost, and, in some instances, subsequently also improved cost to arrive at an overall superior position. Their empirical research thus provides support for the hypotheses of Clark (1996), Ferdows and DeMeyer(1990), Hayes and Pisano (1996), and Porter (1996). As another example: of integration of theory and empirics, Cachon and Lariviere (2001) and Terwiesch et al. (2005) both considera demand planning problem between a manufacturer and supplier, the first from a theoretical and the second from an empirical perspective. A manufacturer is launching a new product and will be purchasing a specialized key component from a supplier. In advance of the product launch, the supplierneeds to build capacity, which can only be used forthe specialized key component. To help the supplierdecide what level of capacity to build, the manufacturer provides the supplier with its forecast of demand in the form of a probability density function of demand. Note that the manufacturer has an incentive to inflate its forecast to encourage the supplierto build ample capacity and thus minimize the risk that it might loose business because of inadequate supply. The supplier, on the other hand, knows the manufacturer has this bias and is therefore inclined to discount the forecast. This is a very real problem. As one personal example, I worked once with the manufacturing division of a large company that had tracked the forecasts prepared by the sales and marketing division and found they exceeded actual demand by 30%. Consequently, they began to divide the sales and marketing forecast
Fisher: Strengthening the Empirical Base of Operations Management 376 Manufacturing & Service Operations Management 9(4), pp. 368–382, © 2007 INFORMS by 1.3 for planning purposes. Sales and marking got wind of this, and you can guess what they did; they began to inflate theirforecasts by 60%! Cachon and Lariviere (2001) consider this complex planning context and ask how a manufacturer that is telling the truth about its forecast can convince the supplierof this. They design contracts the manufacturercan offerthe supplierthat would not be attractive to the manufacturer if the true forecast were less than they were representing. Building in part on this research, Terwiesch et al. (2005) take an empirical approach to this problem. They worked with a manufacturer and 78 of their suppliers to collect data on 3,000 instances over two years when the manufacturer shared a forecast with a supplier. They note that the problem is often a repeated game, so when a supplier receives a forecast from a manufacturer, its faith in that forecast will depend on the accuracy of all of the forecasts received from that manufacturer in the past. Those suppliers that have received forecasts that were relatively poor(biased high and/orchanged frequently in the past) provided significantly worse service, delivering less than was ordered and delivering it late. Conversely, the manufacturer tended to inflate its forecast to the extent that it had been short shipped by the supplierin the past, thus creating the conditions of what would appearto be a downwardly spiraling relationship. Experimentation is a common form of empirical research in the physical sciences and, as described in Croson and Donohue (2002), is emerging as a useful technique in operations management. Probably the most famous laboratory experiment in operations management is the beergame described in Sterman (1989). Participants play the role of managers of firms in a beersupply chain, comprised of a manufacturer, a wholesaler, a distributor, and a retailer. They make supply decisions based on recent downstream demand or orders, but with no knowledge of future demand ororders. The beergame has been used almost universally in courses on supply chain management and has been the source of an important conjecture about supply chains called the bullwhip effect. It is usually observed in the beergame that ordervariability increases as one moves upstream in the supply chain, just as the movement of a bullwhip increases from the handle to the tip. For example, the variation in manufacturer orders is usually much greater than retail demand. Formany years it was believed, and anecdotally observed, that real supply chains exhibited this same phenomenon. Then Lee et al. (1997) developed analytic results for various supply chain planning contexts that would explain why the bullwhip effect could be expected to occur. Guided by this framework, Cachon et al. (2007) used data from the U.S. Census Bureau and the Bureau of Economic Analysis on sales, inventory, and prices to search for instances of the bullwhip effect. They found the bullwhip effect in some situations but not in others, which led them to develop a more refined framework of the factors that increase demand variability and those that attenuate demand variability as one moves upstream in a supply chain. The research reviewed in this section that was conducted using both theorizing and empirical research, with each stimulating the other, has many positive features. However, some might argue that our field lacks a cohesive and general theory of operations, and therefore any discussion of integration of theory and empirics must be postponed until we have such a theory. I would argue that, as suggested by von Neuman (1956), the best theories are the result of efforts to understand real phenomenon: thus, theorizing based on empirics increases the chances of improving the theoretical base of operations management. Others have suggested that relations between theoreticians and empiricists are often contentious in otherfields, including medicine, physics, and finance. I would counterthat vigorous debate about issues is the mark of a healthy field. Doing research on important issues that people care about will always invite controversy, but that’s a good thing, not a bad thing. 7. Data Sources Data are the raw materials of empirical research, so a crucial question for an empirical researcher is where to get data. I examined a numberof papers on empirical research in operations management to identify data sources. The results are compiled in Table 2. Many of the papers cited as examples of various types
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