Imagine for a moment that you have been hired as the new assembly line foreman at Gizmos & Doodads Incorporated, a company that manufactures highly desirable widgets. Your new boss tells you about how slow production has been; orders have been taking twice as long to fulfill and the line has been unable to keep up with what is otherwise a successful increase in business. Your job is to make sure that the line workers can meet demand.
You outline a plan to not only meet demand but also have the factory running at peak efficiency. The first step of your plan is to determine the current rate of production and set goals to measure improvement. The second step is to measure and fine-tune the efficiency of each phase of the operation. Step three, of course, is profit.
In order to find out the production speeds, you implement a widget-counter system that measures how quickly each unit is made. After a week of aggregating information, you determine that the end-to-end time for manufacturing is half as fast as you need it to be to meet the quota. You’ve confirmed that there is indeed a problem in the performance process, but you still don’t know why.
To understand what’s wrong, you move to the second step and analyze what each part of the assembly line is doing. You inspect every station for inefficiencies and time how long it takes until the task is completed. Contrary to the continuous collection of data in the first step, this one is more like a snapshot of ...