Theory of Constraints Handbook - James Cox Iii [111]
Huang and Sha (1998) use a hybrid DBR/Kanban system to model a wafer fabrication facility through a simulation model. Kanbans, which pull material forward station-by-station, somewhat override the purely informal DBR approach to non-constraint dispatching. Huang and Sha also attempt to determine the optimal size of Kanbans in such a system.
Hurley and Whybark (1999) studying a simulated V-plant correctly point out that variance reduction can reduce the need for protective capacity and protective inventory.
Chakravorty (2000) presents a second case study of DBR at Robert Bowden, Inc., emphasizing the fact that it is a V-plant. V-plants running DBR have not received a great deal of attention in the literature. The plant used two buffers—constraint and shipping. Between 1996 and 1999, annual sales in units increased from approximately 58,000 to over 80,000 while the number of workers only increased from 12 to 16. During the same period, the stock of finished goods was reduced from 3800 to 1325, while late orders decreased from 19 to 7 percent.
Frazier and Reyes (2000) present a detailed description of how DBR was applied to the Dallas, Texas, plant of a company manufacturing cable and telecommunication equipment in a V-plant. After three months, WIP decreased to one-third of its previous level, raw materials inventory value decreased by approximately 30 percent, and percent on-time completion of jobs increased by more than 30 percent.
Schaefers et al. (2004) report the implementation of DBR in a facility that buys large rolls of metallic sheets and cuts them into smaller coils with less width and length. This appears to be a V-plant. The firm is a make-to-order (MTO) operation with no internal constraint so it used the shipping schedule as the drum (S-DBR). Before implementation, lead time varied from 21 to 182 days. After implementation, it was a stable 10 days. Customer service level increased from 34 to 87 percent. The exact change in profitability was not reported, but the authors did say that the facility changed from losing money to making money.
Belvedere and Grando (2005) report on a DBR implementation at an Italian chemical company producing dyes and pigments. Because the main raw materials are natural products, it was difficult to obtain the desired color precisely. The solution would be diluted and color-tested repeatedly, causing a dilution and the sample-testing department to be the constraint. In two years, the DBR led to a decrease in raw materials and finished goods inventory and to an increase in the number of stock turns, which almost doubled between 1999 and 2001.
Umble, Umble, and Murakami (2006), noting the lack of case studies from Asian implementations, report a case involving Hitachi Tool Engineering, a Japanese tool-engineering firm employing approximately 1100 people. They describe the plant they studied as a V plant. In addition to implementing DBR, the firm implemented some TOC thinking processes. A simple DBR system was set up using three shelves at the bottleneck with each shelf containing a day’s work for the bottleneck. The authors report that this was adequate to buffer the bottleneck and to subordinate other resources to the bottleneck’s schedule.
A-Plant Research
Simulation of Hypothetical A-Plants In this section, I discuss simulations of hypothetical lines that appear to me to be A-plants. Unless specifically mentioned, the authors did not specify the plant type using the VATI breakdown.
Taylor (1999) simulated a traditional push (MRP) system versus a pull (JIT) and “hybrid” (DBR) system regarding their impact on financial measures. His simulation model appears to be an A-plant. It contained 29 stations. Independent variables included buffer size and location. He found that the DBR system had higher profit, return on investment (ROI), and cash flow while using considerably less inventory. The pull system placed second in financial results with the push system placing last. Taylor (2000) studied this same plant for impact