Theory of Constraints Handbook - James Cox Iii [110]
Kadipasaoglu et al. (2000) simulating an I-facility found that (1) when protective capacity increased from 0 to 12.5 percent, flow time decreased by about 40 percent; (2) there is a benefit to WIP level for having the constraint be the first station;8 and (3) non-constraint downtime and protective capacity tend to have opposite effects on flow—increasing non-constraint downtime decreases flow, which can be offset to an extent by increasing protective capacity. Betterton and Cox (2009) later studied this simulation and found that the methodology employed was not a correct implementation of DBR. First, Kadispasaoglu et al. (2000) had random arrivals released into the plant rather than using a rope to release material at the drum’s pace. Second, using station 1 as the constraint, Kadispasaoglu et al. used infinite buffers at all downstream stations. Blocking can never occur with infinite buffers, so the constraint would never undergo blocking. Simulating the environment as a true DBR environment, Betterton and Cox (2009) found that some of Kadipasaoglu et al.’s findings were not correct.
Blackstone and Cox (2002, 419), using a simulated I-facility, define “protective capacity” as “the capacity needed at non-constraint workstations to restore WIP inventory to the location adjacent to and upstream of the constraint workstation to (create a time buffer to) support full utilization of the constraint workstation.” It should be noted that the ability of downstream stations to empty the space buffer when it contains work is also protective capacity—protecting against blocking. Blackstone and Cox also show that the size of the time buffer required to adequately protect the drum is inversely related to protective capacity, a point that had been made previously by Atwater (1991).
Kim et al. (2003a) simulated a variety of flow control mechanisms within an I-line and found that, compared to output flow control and dynamic flow control, bottleneck flow control achieved greater output with less WIP while maintaining smaller lateness and tardiness of orders.
Real I-Lines I found no simulation studies or case studies of real I-lines. I think this is because even when the flow is straight line, real facilities tend to have multiple products that diverge into various configurations as they travel down the line. That is, they are V-plants, not I-plants.
V-Plant Research
Simulations of Real V-Plants Vaidyanathan et al. (1998) describe the simulation of a coffee production facility having moving CCRs. The simulation model was used to develop a schedule for this V-plant. The simulation showed that output could be increased by approximately 40 percent by using the simulation model to develop the schedule.
Hasgul and Kartal (2007) used the Wagner-Whitin algorithm, a very sophisticated technique used to attempt an optimal sequence of jobs over a lengthy planning horizon, to schedule a simulated refrigerator plant. The portion of the company they were simulating corresponded to a V-plant. They reported achieving an average cycle time decrease from 12 days to 7 days when DBR was applied.
Case Studies of V-Plants Chakravorty (1996) reports a case study at Robert Bowden, Inc., a $40 million sales supplier of residential and light commercial building products whose manufacturing facility is a V-plant. After implementation of DBR (which is described in the article), the average number of orders processed increased by 20 percent with no increase in staff, and expediting of orders was significantly reduced.
Rerick (1997) presents a study of semiconductor wafer manufacture at Harris Corporation, which reduced cycle time by approximately 50 percent while almost doubling output. Wafers were made for automotive, telecommunications, and computer markets.