Theory of Constraints Handbook - James Cox Iii [114]
Corbett and Csillag (2001) report on seven DBR implementations in Brazil. Five of the companies were multi-nationals, while two were Brazilian. Six used MRP and one used Kanban before using DBR scheduling. Average time to implement DBR was 3.6 months with the longest being 7 months. All seven companies started showing beneficial results during the implementation period. Six of the seven reported that they were satisfied with DBR. Even the one reporting dissatisfaction experienced a 50 percent drop in WIP and lead time and an increase in revenue per employee from US$56,000 to $64,000.
Lindsay (2005) reported on the implementation of DBR in Intel distribution centers (DCs) in an attempt to reduce order cycle time and reduce Inventory. Five DCs located in five countries have implemented DBR with an average cycle time reduction of more than 60 percent and a standard deviation reduction of more than 70 percent.
Vermaak and Ventner (undated) report the use of TOC in conjunction with computer simulation of a conveyor system in a coalmine, which resulted in an 8 percent increase in output.
Mabin and Balderstone (2003) report on an analysis of over 80 successful TOC implementations taken from a search of available literature. A portion of one of their tables reporting percentage improvements in various measures is shown below.
Huff (2001) reports that Bal Seal Engineering used DBR to increase Throughput, reduce Inventories, improve due date performance, reduce Operating Expense, and double net profit. Boeing and Rockland Manufacturing also achieved dramatic improvements relative to Throughput, Inventory, and profit.
Special Cases
The TOC literature contains a number of articles describing research that does not fall neatly into the previous categories but that are significant in their contributions to the body of knowledge. I have classified this research into the topics given in the next sections.
Free Goods
Free goods are defined as goods that do not require any resource constraint involvement in their production—they require solely non-constraints. Free goods represent an opportunity for immediate increase of Throughput with little to no increase in Operating Expense (recall that Throughput accounts for raw material expense, items that are truly variable costs). However, Chakravorty and Atwater (2005) found that DBR is very sensitive to levels of free goods. Therefore, schedules using DBR need to be aware of how orders of free goods are accepted. Specifically, they found that the number of tardy orders increased as the level of free goods released to the shop increased. They attribute this phenomenon to the loss of protective capacity at certain non-constraint resources. Atwater, Stephens, and Chakravorty (2004) discuss the impact of free goods on system Throughput. They found three basic insights for the system they modeled. First, operating the resource constraint at a level above 98 percent resulted in erratic Throughput performance. Second, increasing protective capacity above 7 percent did not significantly improve on-time performance. That is, once a nonconstraint’s capacity reached 107 percent of the constraint’s capacity, further increases in capacity did not improve on-time performance. Of course, this value would be very sensitive to the number and duration of statistical fluctuations included in the model. Third, when demand for constraint goods is high, managers can improve on-time performance by limiting the orders they accept for free goods (refusing such an order would reduce future utilization of non-constraint resources).
What If the Market Is the Constraint?
What if all goods are free goods? That is, what if the market is the constraint? Pass and Ronen (2003) define a market constraint as a situation in which the production capacity of every resource exceeds demand for it; they address this issue for a high-tech firm. They note that it is usually easier to control an internal