Since Eli Goldratt first appeared on the scene in the late 1970s, his ideas concerning production management have generated a huge amount of interest, controversy, and misunderstanding. These ideas have been proliferated under several names such as optimized production technology (OPT), drum‐buffer‐rope (DBR), synchronized manufacturing (SM), and theory of constraints (TOC). Although there seems to be general agreement on the importance of how capacity‐constrained resources are scheduled, research aimed at advancing the state of the art for the specific problem addressed by DBR continues to be limited by prior misunderstandings and the lack of a rigorous examination by the academic community. This paper seeks “to advance the state of research on constraint scheduling in several ways. First, it presents a concise history of the evolution of DBR. It then explains the use of rods in constraint scheduling. Next, it presents in detail the solution algorithm incorporated by the Goldratt Institute in their production software and, finally, relates that algorithm to alternative methods. In the process of these activities, several lingering misconceptions are resolved.
Job batching is used extensively in manufacturing and the relevant theoretical considerations have been well‐researched. However, while batching is also employed in mass services, it is not clear to what extent the manufacturing theory may be transferred. A single case study of a court scheduling service system with imbedded instances of batching was studied to address this question. The findings and analysis of the case indicate that while the factors that affect batching in manufacturing still apply, so do additional factors. The net effect is a broader set of considerations which influence the determination of when batching is desired in mass services and how big batches should be. Definitions of these factors, their relationships with batch size, and testable hypotheses are offered.
Prior research substantiates the value of compensating customers for poor quality. However, little guidance has been provided for the recovery of errors while service delivery is in progress. The authors provide analytical models inspired by reliability theory to guide managers in allocating their investments in service recovery. Models are constructed to achieve four different objectives and converted to Lagrangian formulations for solution. Example solutions illustrate the contrast in investment allocations.
To succeed, service businesses must offer their customers high-quality, reliable service. However, many of the characteristics that make services unique also make it difficult to ensure consistently correct performance. To promptly identify and correct errors when they occur, service managers have been advised to include recovery steps in their service processes. However, while service recovery has anecdotal support, the literature has so far not offered management tools for analytically evaluating a system's needs for recovery measures or assessing their potential benefit. To provide such a tool, this paper transfers the logic of reliability theory, which is widely used in the design of electrical and mechanical systems. The application of this approach yields several useful insights for managers, to include the effects of various process structure characteristics.
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