This blog entry summarizes and provides comments on the book The Goal, by Eliyahu Goldratt. The Goal tells the story of Alex, a fictitious manufacturing plant president. Alex is on the verge of losing his job for having an unproductive plant. Alex is constantly reacting to emergencies, he has a large backlog of customer orders, long lead times and significant amounts of work-in-process inventory. Alex's old friend, Jonah - a former physics teacher turned process consultant - asks Alex a series of probing questions that lead him to an epiphany which helps turn the plant around.
Alex wants to improve his plant's productivity. Jonah responds by posing a question to Alex: What is his manufacturing plant's goal? After determining that productivity in the abstract is not the goal and sifting through a series of cost accounting metrics, Alex finally realizes that the goal of his plant and the overall company is simple: to make money. Jonah posits that each and every activity that occurs in the plant should be measured by one metric: does that activity advance the company toward its goal. If an activity does advance the company toward its goal, then it is productive (even if it does not appear to be productive in the abstract). For example, having an employee sit idle by a machine could be productive if that machine is a bottle-neck operation for the company and redeployment to another task at a non-bottleck operation would result in a small loss of set-up time for the bottleneck machine when the current process is complete.
The Goal describes a "theory of constraints," which focuses on identifying bottleneck operations in a manufacturing process, increasing throughput and identifying statistical fluctuations and dependent events in the process. Statistical fluctuations and dependent events can have a subtle impact on the productivity of a manufacturing process.
For example, assume that Step A is a dependent event with respect to Step B (i.e. Step A must be completed before Step B can begin). Assume the average time to complete Step A is 10 minutes per batch. If there is statistical fluctuation with respect to Step A, then the actual time to complete Step A would vary by batch. For example, the actual time to complete Step A may vary from 7 minutes to 13 minutes. The combination of these two factors will create a constraint on Step B. Assume that Step B always takes a uniform time of 10 minutes to complete due to process automation. If Step A also had a uniform 10 minute process time without fluctuation, the total process time (ignoring setup) would be a constant 20 minutes. In practice, Step B will be idle after runs that fluctuate above 10 minutes during Step A and the total process time will be longer than 20 minutes about one half of the time.
One of the primary points of the book is to point out how important it is for a decision-maker not to lose sight of the true goal of the business by focusing on metrics that do not accurately advance the goal. In the Goal, many of the metrics that are identified as unproductive are cost accounting metrics that do not take a system-wide approach in measuring productivity.
The Goal also emphasizes the importance of "continuous process improvement." If one step in a process is improved to better advance the company's goal, other steps may be impacted and an iterative analytical and improvement process must be followed to achieve additional improvements to productivity. This is a good reminder that change and a constant focus on improvement are critical to success. In Alex's case, once Alex stopped focusing on specific cost accounting metrics, he focused on system-wide productivity and his throughput increased dramatically. Each time his team made a change to a process, one or more other opportunities for improvement emerged.
The Goal discusses another broad concept. Many of the ideas to improve Alex's manufacturing process originated in the laws of physics. The Goal discusses how considering the nature of a problem in one realm can reveal a general principle that holds true across other applications. It is important not to become too focused on one problem in a vacuum. Sometimes, an idea that originates in one setting may provide the answer to a seemingly unrelated problem.
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