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National Academy of Engineering (US) and Institute of Medicine (US) Committee on Engineering and the Health Care System; Reid PP, Compton WD, Grossman JH, et al., editors. Building a Better Delivery System: A New Engineering/Health Care Partnership. Washington (DC): National Academies Press (US); 2005.

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Building a Better Delivery System: A New Engineering/Health Care Partnership.

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Supply-Chain Management and Health Care Delivery: Pursuing a System-Level Understanding

Reha Uzsoy

Purdue University

In recent years, effective supply-chain management has emerged as a significant competitive advantage for companies in very different industries (e.g., Chopra and Meindl, 2000). Several leading companies, such as WalMart and Dell Computer, are differentiated from their rivals more by the way they manage their supply chains than by the particular products or services they provide. A supply chain can be defined as the physical and informational resources required to deliver a good or service to the final consumer. In the broadest sense, a supply chain includes all activities related to manufacturing, the extraction of raw materials, processing, storing and warehousing, and transportation. Hence, for large multinational companies that manufacture complex products, such as automobiles, machines, or personal computers, supply chains are highly complex socioeconomic systems.

The ability of successful firms to make the effective management of supply chains a source of competitive advantage suggests that there may be useful knowledge that can provide a point of departure for the development of a similar level of understanding of certain aspects of health care delivery systems. Similar to the supply chains in manufacturing and other industries, the health care delivery system is so large and complex that it has become impossible for any individual, or even any single organization, to understand all of the details of its operations. Like industrial supply chains, the health care “supply chain” consists of multiple independent agents, such as insurance companies, hospitals, doctors, employers, and regulatory agencies, whose economic structures, and hence objectives, differ and in many cases conflict with each other. Both supply and demand for services are uncertain in different ways, making it very difficult to match supply to demand. This task is complicated because demand for services is determined by both available technology (i.e., available treatments) and financial considerations, such as whether or not certain treatments are covered by insurance. Decisions made by one party often affect the options available to other parties, as well as the costs of these options, in ways that are not well understood. However, almost all of these complicating factors are also present, to one degree or another, in industrial supply chains; the progress made in understanding these systems in the last several decades is a cause for hope that some insights and modeling tools developed in the industrial domain can be applied to at least some aspects of health care delivery systems.

In general, a centralized approach to controlling the entire system is clearly out of the question, although centralized decision models may be useful for coordinating the operations of segments of the larger system controlled by a single decision-making body. Designing decentralized models of operation that render the operation of the overall system as effective as possible is the main challenge for both health care delivery and industrial supply chains.

In the following section, I shall briefly discuss how the study of industrial systems has evolved from individual unit processes to considerations of complex interactions among many different components of an industrial supply chain. I shall then describe some examples of modeling approaches that have been applied to supply chains and close with some comments on how these tools might be adapted for the health care delivery environment.


If we examine how industrial operations, particularly manufacturing operations, have evolved since the beginning of the nineteenth century, we can see that many efforts were motivated by a desire to understand and optimize individual unit processes (see, for example, Chandler, 1980). These efforts led to many innovations, among them the development of improved machine tools and fixtures, a significantly better understanding of the chemistry of processes (e.g., steel-making), and through the work of the early industrial engineers, such as Frederick Taylor and Frank and Lillian Gilbreth, the optimization of interactions between workers and their environment.

As the understanding of unit processes developed, engineers began to consider larger and larger groupings of unit processes, trying to understand interactions between them and optimize the performance of entire systems, sometimes to the detriment of individual components. Hence, from considering individual unit processes, we progressed to considering departments of factories that perform similar operations, entire manufacturing processes from raw materials to finished products, and eventually, the operations of entire firms, as well as their suppliers and customers. It has often been observed that most significant new opportunities, both for cost reduction and the generation of new products and services, have been based on an understanding of interactions between different subsystems, or different agents, operating in the supply chain.

Among today's leading companies, examples abound. Many automotive companies, for instance, have developed joint ventures with transportation firms; the objective is to optimize the interface between the production and distribution functions and facilitate the just-in-time operation of automakers' final assembly plants. Software companies that provide supply-chain planning software for multilocation companies is another strong indicator of the advantages companies perceive will accrue to them by the effective management of the various elements of their supply chains. The strong trend in industry to outsource noncritical functions has increased the need for companies to effectively manage and clearly understand their relationships with other companies. As a final example, we can point to the collaborative forecasting, planning, and replenishment initiative in the retail sector; retailers work closely with major suppliers to develop demand forecasts for products through information-sharing and joint planning processes.

Clearly, the basic process of improving a system by a detailed understanding of the most fundamental unit processes, in other words the “atomic” elements of the system, and steadily extending that knowledge to interactions among larger and larger groupings of these elements is directly applicable to health care delivery systems. The individual unit processes in this case include the processing of a patient in an emergency room, the process by which a medical insurance claim is approved, and the scheduling of hospital operating rooms to optimize their performance. The need for a better understanding of how the operations of individual elements affect each other is apparent; these interactions can be quite complex because of long time lags between cause and effect. For example, the decision by a regulatory agency to disallow a certain kind of preventive procedure for infants may result in the emergence of an unexpectedly large number of children with special needs in the elementary school system several years later. The same kinds of problems are present to some degree in industrial supply chains, and a significant body of knowledge has been developed over the years to address them.

Based on the history of industrial enterprises, we know that the development of today's enterprises required substantial organizational innovations, such as capital budgeting to allocate scarce capital between competing activities, cost accounting to develop an understanding of factors contributing to product costs, and the development of multidivisional corporations with complex structures of management incentives and coordination mechanisms. An important development in recent years has been the recognition of the need for a cross-functional view of supply-chain operations. All aspects of a firm's operation, from the design of a product to the specific timing of marketing promotions, have a direct effect on the operation of the supply chain. Therefore, different functional specialties must actively collaborate to develop solutions to optimize the performance of the overall system. Similarly, in health care delivery a number of different constituencies, such as doctors, government agencies, insurance providers, and patient groups, are all involved in the operation of the health care delivery supply chain.


In the domain of industrial supply chains, it is probably safe to say that we have developed a fairly good understanding of the operation and economics of individual unit processes, including functions such as transportation, distribution, warehousing, and information processing. In particular, we have developed a substantial understanding of the often complex dynamics of capacity-constrained systems subject to variability in both demand and process (Hopp and Spearman, 2000). However, in general we are only beginning to learn how to integrate the solutions to these individual elements to reach a reasonable understanding of the operation of the overall supply chain.

Integrated planning models based on linear and integer programming have been applied to the segments of the supply chain controlled by a single company for at least four decades (e.g., Johnson and Montgomery, 1974). Although these models have been successful in many instances, they have not been effective in addressing the needs of a supply chain that involves many different companies with potentially conflicting objectives. In recent years, considerable efforts have been made to use some of the tools of economics, such as contracts, as a mechanism for coordinating the operation of complex supply chains (Tayur et al., 1998). However, these models are generally subject to long-run, steady-state assumptions that can be carefully evaluated relative to market conditions.

Conventional Monte Carlo simulation techniques (Law and Kelton, 1991) have proven extremely effective for systems in which the operational dynamics can be described at a high level of detail, such as segments of manufacturing processes or hospital operations. The difficulty with these models is that for large-scale systems the level of detail required to unequivocally model system behavior accurately becomes prohibitive in terms of both data collection and computation time. Systems dynamics models used to model large systems work by establishing input-output relationships for their components and simulating their operation through time using techniques based on the techniques used for the numerical solution of differential equations (Sterman, 2000). Although these techniques are capable of modeling large, complex systems, they usually do so by specifying aggregate input-output relationships for large subsystems, which must be validated and whose parameters must be estimated carefully. Nevertheless, these models can capture many critical aspects of supply-chain behavior, such as the “bullwhip effect,” in which variability in orders is amplified as it passes down the supply chain from the consumer towards the producers of raw materials (Forrester, 1962).


At the risk of overgeneralizing, it appears that most of the tools required for analysis of the individual unit processes in health care delivery, such as efficiency of hospital facilities, have been developed in the engineering literature and have, in fact, been applied intermittently to a variety of systems over the last several decades (e.g., Pierskalla and Brailer, 1994). However, if our experience with industrial supply chains is any guide, only limited improvements in health care delivery can be obtained by these means. Repeated experience has shown that far greater improvements can be obtained by a thorough understanding of the interactions between different elements of the system and restructuring them in a way that leaves all parties better off. This brings the modeling issues squarely into the region where current supply-chain research is weakest (the effective coordination of socioeconomic systems consisting of multiple, independent agents); but this is also the area that is developing most rapidly. The development of novel models at the intersection of conventional engineering and economics promises to provide a wide range of challenging research problems for many years to come.

To support this agenda, the most pressing research need is for techniques that can be used to model systems at the aggregate level, where one can accept some level of approximation to obtain computationally tractable models that achieve the correct qualitative behavior and provide useful insights into interactions between systems. This means that the aggregate models must capture the often nonlinear relationships between critical variables correctly, which has not always been the case in supply-chain modeling. The literature on systems dynamics may be a good starting point for this initiative, but it must be complemented by a variety of other techniques, such as economic models of competition and collaboration and agent-based techniques for modeling complex systems.

It is important to bear in mind that the purpose of these models is far more likely to be descriptive than prescriptive, that is, models are far more likely to be used, and arguably far more useful, to inform debate between the various parties involved in health care delivery than to deliver decisions to be executed. Hence, the development of large-scale computational simulations of different scenarios with different actors and interaction protocols between the actors appears to offer interesting research challenges. These tools would be extremely beneficial to decision makers in health care delivery.


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Copyright © 2005, National Academy of Sciences.
Bookshelf ID: NBK22867


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