Theory of Constraints Handbook - James Cox Iii [112]
Atwater and Chakravorty (2002) found that mean flow time through a simulated system that has a jumbled flow and appears to be an A-plant decreased as protective capacity increased but at a diminishing rate as protective capacity reached 7 percent. Mean tardiness decreased in the same fashion. In their study, they varied constraint utilization from 94 to 98.5 percent. They compared releasing jobs immediately upon arrival in the system to releasing jobs according to the DBR schedule and found that while DBR had a smaller mean flow time through the system, the immediate release approach resulted in fewer tardy jobs.
Simulations of Real A-Plants Wu, Morris, and Gordon (1994) show how DBR improves makespan when compared via simulation to a traditional production control system. Make-span is the time from the start of processing until the final unit clears the system. The Wu et al. simulation is an A-plant based on a furniture manufacturer. They demonstrated that a Taiwanese furniture manufacturer would benefit significantly from makespan by implementing DBR. In their simulation, makespan decreased approximately 50 percent when DBR was added to the environment.
Guide (1995) presents a simulation model used to estimate ideal buffer sizes in a DBR implementation at a naval repair depot. A naval or air force repair depot completely disassembles a plane (while this may resemble a V-plant, in contrast to a V-plant parts flow down each path instead of one or the other paths), repairs or replaces components as needed (probably A-plant), and reassembles the plane (A-plant). This process is known as remanufacturing.
Steele et al. (2005) simulate a shop using both DBR and MRP. They found that DBR has much better performance and suggested the use of DBR within MRP systems. They based their simulation on a bearing manufacturer. This involved an assembly that sat atop two V-lines.
Case Studies of A-Plants Andrews and Becker (1992) present a case study of Alkco Lighting, noting “Buffer Management” as a keyword. This A-plant involves several assembly operations. Alkco changed its primary measurement from efficiency to Throughput. As a result, WIP inventory improved significantly and there was an accompanying improvement in cash flow. Prior to implementation, the company was promising delivery in 60 to 90 days, had an on-time rate of only 65 percent with 16 percent of deliveries being more than one week late. Thirty-two percent of Inventory was in finished goods. The DBR system as managed by Alkco freed up 40 percent of its total floor space. Five years into the implementation, lead time was reduced to one week, while on-time delivery increased to 98 percent, sales volume increased 20 percent, and before-tax profit increased 42 percent.
Spencer (1994) reports on improvement from Trane Co. of Macon, Georgia, where output changed from an average of three units per day to six units per day with the same work-force when DBR was implemented. At this location, Trane assembles large air conditioners designed to cool commercial facilities.
Guide (1996; 1997) and Guide and Ghiselli (1995) present three discussions of the application of DBR in remanufacturing applications such as a military repair depot. As in Guide’s (1995) simulation discussed above, this facility appears to be an A-plant (reassembly) sitting atop a disassembly operation. Disassembly is somewhat akin to a V-plant in that a single plane diverges into many components to be evaluated and repaired, replaced, or reused. However, the consensus is that a disassembly operation is different from a V-plant where a part flows to one product or another.
Luck (2004) presents an Ashridge Business School (UK) study of a supply chain centered on a manufacturing company called Remploy, which makes military garments. Remploy had two plants, a V-plant that cut material and an A-plant where sewing was accomplished. Five months into a standard DBR implementation, Throughput had increased 19 percent, output per employee