Theory of Constraints Handbook - James Cox Iii [300]
Hypothesis #1: The system’s starting conditions (its capacity, capability, etc.) are simply insufficient to meet the demand and the only solution is to “elevate” the system constraint(s) (constraining starting condition) by investing in more resources or better resources. This hypothesis of the underlying cause for a gap is quite a common claim. “If you want my department to do more . . . I need more resources, better systems, etc.”
Hypothesis #2: The system’s starting conditions (its capacity, capability, etc.) are sufficient to achieve significantly higher levels of Throughput within significantly shorter lead times than currently but capacity, time, and costs are wasted due to the current mode of operations. The solution in this case will be to “better exploit” (not waste) the potential of the system constraint (i.e. always try better exploiting before elevating the system constraint).
TABLE 15-4 Success Criteria Recommended by Dr. Eli Goldratt (2008c)
How can we validate whether Hypothesis #1 (no significant inherent potential) or Hypothesis #2 (significant inherent potential) is most valid for a specific organization?
Let’s start with the general facts (governing principles) about any system and see what we can deduce from these.
Fact 1: The system constraint (bottleneck) governs the Throughput (flow rate) of goal units for the whole system.
Implication: The system (on average) can never produce more goal units than what the constraint is capable of. However, if constraint capacity is wasted through starvation, blockage, breakdowns, or rework, then the system will achieve a lower Throughput than what the system (based on its constraint) is capable of. The level of constraint capacity wasted on starvation, blockage, breakdown, rework, etc., can be used as a reliable way to estimate whether inherent potential exists (i.e., the opportunity to do more without investing in more resources). The capacity lost is normally between 25 and 50 percent of the available capacity.
Fact 2: The critical chain (the longest path of dependent events considering both process and resource dependency) governs the lead time (flow time) of all goal units through the system.
Implication: The parts going through the system can never go faster (on average) than the time to cover the critical chain. However, this flow time will be longer than the sum of processing and movement times on the critical chain when goal units traveling through the system have to wait for a resource or a decision. The level of time wasted on the critical chain due to resource or information unavailability (delays) can be used as a reliable way to estimate whether inherent potential exists (i.e., the opportunity to do the same or more within less time without investing in more resources). The time lost is normally 25 to 50 percent of the critical chain time.
Fact 3: Every system’s performance (Throughput of goal units, lead time, costs, and investments) varies over time. Sometimes there is a significant variation between the best, the average, and the worst.
Implication: The “best ever” performance shows what is possible with the current starting conditions. Normally the “best ever” is achieved under ideal or crisis circumstances. The “ideal” circumstances should be turned into standard best-practice. It is in crisis situations that we become very open to “do whatever it takes,” including changing the current rules (normal mode of operation) and ignoring efficiency measurements. For example, if there is a scarcity in the market, we naturally move to a “wait for the pull” rather than “push as much as you have.” Why not use pull all the time?“Necessity is the mother of invention,” but frequently these “inventions” that got us out of the crisis don’t “stick” since we go back to the “way we’ve always done it before.”
Therefore, if when we observe a significant gap and variation between the actual performance