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Riezebos, Korte, and Land (2003) report on a problem with maintaining lead times in a DBR implementation, which they corrected using Workload Control to better manage the release of material into the shop, thereby maintaining an appropriate buffer size.
Simons et al. (1996) discuss the difficulty of scheduling a DBR managed system with multiple CCRs where they correctly stipulate that a CCR need not be a bottleneck. They follow the process for applying DBR as outlined by Goldratt (1990, 241-3) in The Haystack Syndrome. They created a “diverse set of benchmark problems” on which to test the efficacy of the general DBR algorithm. They used a branch-and-bound approach to obtain optimal schedules and found that in the presence of multiple CCRs, the DBR solution averaged within 3 percent of optimal.
Floating or Multiple Bottlenecks
A situation that is frequently proposed to give DBR problems is the existence of multiple or floating bottlenecks; that is, bottlenecks that change over time because of seasonal or long-run changes in the product mix. Lawrence and Buss (1994) state that balanced utilization rates increase the shifting bottleneck problem. They further state that increasing capacity at non-bottleneck work centers is the “best hope” for improving shop performance.
Simons and Simpson (1997) defend DBR’s ability to contend with multiple constraints. The Goal System utilizes an iterative procedure to schedule multiple constraints to “accommodate interaction.”
Guan et al. (2007) report simulating an electronics manufacturing system with multiple bottlenecks. Lenort and Samolejova (2007) report on identifying floating bottlenecks in metallurgical production and using such identification to maximize output.
Summary and Conclusions
This literature amply demonstrates that DBR is an effective and efficient system for planning and control of both manufacturing and service organizations. It has been applied successfully in a wide range of organizations. Reported problems are few and seem to occur primarily where implementers develop inadequate protective capacity. Two remaining issues for research are the ideal levels of protective capacity and the correct initial buffer sizes. Buffer sizing is a short-term problem because buffer sizes can be adjusted quickly using information developed through Buffer Management. Protective capacity usually cannot be established precisely as a manager might hope, as equipment is available only in certain sizes so a piece of equipment yielding the desired amount of protective capacity may not exist. The most recent application of DBR is to supply chains. Early papers on this issue have argued that application of the 5FS and DBR within supply chains is both possible and beneficial.
For future research on cases, it would be helpful if the investigators would be specific about whether the plant was a V, A, or T configuration; whether the market or internal resource was the constraint; how buffers were sized initially; and how Buffer Management was achieved. This information would enable the reader to understand the implementation more thoroughly.
Future simulation research on more complex plants regarding protective capacity and protective inventory is needed. Most of such simulation research to date has involved only a few stations in an I formation.
References
Amen, M. 2000. “Heuristic methods for cost-oriented assembly line balancing: A survey,” International Journal of Production Economics 68:1–14.
Amen, M. 2001. “Heuristic methods for cost-oriented assembly line balancing: A comparison on solution quality and computing time,” International Journal of Production Economics 69:255–264.
Andrews, C. and Becker, S. W. 1992. “Alkco Lighting and its journey to Goldratt’s goal,” Total Quality Management 3:71–95.
Atwater, J. B. 1991. The impact of protective capacity on the output of a typical unblocked flow shop. Doctoral diss., University of Georgia.
Atwater, J. B. and Chakravorty,