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number of stations is minimized. Thus, a common field of study regarding assembly lines has long been assembly line balancing. Amen (2000) developed a list of heuristics for assembly line balancing. He later (2001) performed a study of the comparative performance of these methods. Becker and Scholl (2006) extend the discussion to include U-shaped lines, as are common in JIT facilities, and mixed model lines. U-shaped lines are used primarily to produce components for JIT operations, with material entering at one end of the U and exiting at the other. Workers usually perform multiple tasks with tasks often on each side of the U. The number of workers in the line varies by season to maintain a daily output consistent with daily sales.

Just-in-Time

The Toyota Production System (TPS) and Kanban System (Sugimori et al., 1977) were “developed by the Vice-President of Toyota Motor Company, Mr. Taiichi Ohno and it was under his guidance that these unique production systems have become deeply rooted in Toyota Motor Company....” Just-in-time is the successor of the TPS. The purpose of using JIT is to eliminate waste from processes (Hall, 1997). The name JIT is misleading because it suggests that the concept primarily involves materials arriving just in time for use. The major benefit of JIT techniques is the simplification of the processes themselves.

JIT implements a pull system of control often using cards or Kanbans to implement the pull system in which materials are replenished at approximately the same rate they are used.

The objective of JIT is to streamline a process—to change and improve the process itself, not to install a control pull system on a process undeveloped for it. Improvement is multidimensional: delivery (lead time and due date performance), cost, quality, customer satisfaction, and so on.

OPT®—The Precursor to DBR

DBR gradually evolved out of Goldratt’s experience with a shop floor scheduling software called OPT®. In his article “Computerized Shop Floor Scheduling,” Goldratt (1988) explains in detail how OPT® evolved. The first version of the software was basically automated Kanban. Goldratt states that early versions of OPT® were such that straightforward usage was restricted to repetitive environments.

Goldratt came to realize that not all machines need to be utilized 100 percent of the time—only constraints need this. OPT® was reformulated to limit non-constraints to only the work necessary to keep constraints properly fed. This led to difficulty convincing supervisors of non-constraint resources to follow the schedules when these schedules called for less than 100 percent utilization. Goldratt realized that only the bottlenecks should be scheduled—other stations have excess capacity and can keep pace—and thus data accuracy was really needed only at the constraint.

The Nine OPT® Rules We will now list the nine OPT® rules (Goldratt and Fox, 1986, 179)4 and discuss them as special cases of mathematical programming and other methods:

1. Balance flow not capacity.

2. The level of utilization of a non-bottleneck is not determined by its own potential but by some other constraint in the system.

3. Utilization and activation of a resource are not synonomous [sic].

4. An hour lost at a bottleneck is an hour lost for the total system.

5. An hour saved at a non-bottleneck is just a mirage.

6. Bottlenecks govern both throughput and inventories.

7. The transfer batch may not and many times should not be equal to the process batch.

8. Process batches should be variable not fixed.

9. Schedules should be established by looking at all of the constraints simultaneously. Lead times are the result of a schedule and cannot be predetermined.

It is often counter-productive to attempt to balance capacity in order to get a flow-balanced plant. Because constraints determine system performance, constraints should have a buffer of material (represented as a time buffer) upstream of them to protect them from out-ages occurring upstream. This buffer will disappear as it is used to protect from outages.