Theory of Constraints Handbook - James Cox Iii [113]
T-Plant Research
I was unable to find any simulations or studies that dealt with T-plants.
Research That Could Not Be Classified as V, A, or T
Sometimes research cannot be classified as V, A, or T. For one thing, the research may include more than one plant with the types of plant being different. Even if a single plant is described, in many instances, there may not be enough information provided to make a reasonable conclusion on whether the plant is V, A, T, or I.
Simulations of DBR Systems
A number of individuals have simulated DBR systems, sometimes to estimate DBR’s parameters, such as the time buffer, and other times to compare DBR’s effectiveness with systems such as Lean or CONWIP. Guide (1995) experimented with different buffer sizes and Buffer Management techniques at a naval air station. Kosturiak and Gregor (1998) simulated a flexible manufacturing system (FMS) using MRP, Load-Oriented Control (LOC), DBR, and Kanban and found that LOC and DBR had the best performance, while DBR was easier to implement. Hasgal and Kartal (2007) combined DBR with the Wagner-Whitin lot-sizing algorithm and reduced cycle times and WIP in a simulation model compared to DBR by itself.
Kayton et al. (1997) simulated a wafer factory running DBR to better understand the impact of preventive maintenance in such a facility. They found that downtime at non-constraints can become problematic in facilities using DBR even when significant protective capacity exists.
Lea and Min (2003) simulated a seven-station, three-product line using both JIT and DBR and found that JIT had slightly higher profits and service levels. They also found that activity-based costing systems slightly outperformed traditional costing and Throughput Accounting systems.
Case Studies
Several articles present case studies of successful implementation of DBR and constraint management. These case studies could not be classified as V, A, or T. Often the reports include multiple plants.
Gupta (1997) discusses DBR benefits to a supply chain. In 1998, Gupta discussed the need for software to implement DBR, describing some situations that are too complex for manual implementation. Koziol (1988), a manager at the Valmont plant in Brenham, Texas, discusses the successful implementation of DBR at that facility.
Spencer and Cox (1995) report a study of nine repetitive-manufacturing companies, three of which were pure JIT, three added MRP to JIT, and three added TOC (OPT® or DBR) to JIT. No specific improvement numbers were reported; however, they found that the existence of repetitive manufacturing does not preclude the application of any of the three production planning and control systems.
As mentioned earlier, Wolffarth (1998) presents practical lessons learned from an implementation of DBR within an ERP system. Umble and Umble (2006) describe how Buffer Management was used in two accident and emergency facilities in Oxfordshire, UK.
Guide and Ghiselli (1995) report on the implementation of DBR at Alameda Naval Air Depot. This disassembly / repair facility implemented preventive maintenance, added small transfer batches, eliminated local efficiency measures, and took other DBR-related steps. Results achieved included increasing Throughput while reducing WIP, reducing airplane turnaround times, and increasing the turns ratio. Further refinements of DBR at the facility were reported to have been planned.
Umble et al. (2001) report a case study of DBR used within an ERP system. The case is Oregon Freeze Dry (OFD), which processes products by removing water at low temperatures and pressures. A branch of OFD implemented DBR in 1997, identifying a resource constraint that was designated as the drum. ERP was implemented at about the same time. The authors report that an ERP system makes DBR more effective. Once the drum schedule is determined, the ERP