Theory of Constraints Handbook - James Cox Iii [132]
Every production operation is characterized by the following elements.
There Is a High Degree of Dependency
Dependency in this context means that certain operations or activities in the plant cannot take place until certain other operations or activities are completed. Some examples of dependency in a manufacturing operation are as follows:
The routing sequence of required operations to manufacture a product is a simple example of manufacturing dependencies. In the typical case, the production process cannot begin until the required materials have been procured; individual operations cannot be performed until the prior operation specified in the routing has been performed; and the assembly operation cannot begin until all required components have been fabricated or purchased.
Another obvious example of dependency is the same resource being required to process more than one operation. These operations can be different steps in the routing of the same product (rough milling and finish milling, for example) or steps on different products (rough milling of Product A and rough milling of Product B). The possibility for creating blockage for one product when the resource is occupied with another product is obvious.
Other examples of dependency include:
Resources cannot be set up until the setup person is finished with another job.
Work cannot begin until the setup or changeover is complete.
The first piece of a lot cannot be inspected and approved until the inspection gauges are calibrated.
The number of dependencies in even a small production operation is staggering.
Production Operations Are Subject to a High Degree of Variability
Variability exists in manufacturing operations in the form of both random events and statistical fluctuations. Random events are those activities that take place at irregular intervals, have no discernable pattern, and by nature are unpredictable. Examples of random events include:
A significant customer order is suddenly cancelled.
A key vendor’s plant is crippled by a strike and the critical materials are not readily available.
Tools, fixtures, gauges, etc., are suddenly unavailable due to unexpected breakage.
Statistical fluctuation or common cause variations in manufacturing environments refer to the fact that all processes have some degree of inherent variability. Examples of statistical fluctuations include:
Receipt of materials from vendors can vary in quantity, quality, or timing from purchase order to purchase order.
Time to set up a resource varies each time the resource is set up.
Actual customer orders are different from the forecast.
Process yields may change from one lot to another.
We will use the term variability to describe both random events and statistical fluctuations.
The existence of these two phenomena—dependency and variability—combine to make the task of controlling the performance of manufacturing operations very difficult. In fact, the day-to-day role of a shop floor manager is nothing more than attempting to cope with the almost endless stream of disruptions and their impact on a wide range of activities.
At a single step in any process, it is not safe to assume that the effect of statistical fluctuations will average out and the performance of the process will be the average rated performance for that step. One of the dramatic effects of having both dependencies and fluctuations is that this averaging out does not occur. As discussed in detail in several other works (Goldratt and Cox, 1984; Srikanth and Umble, 1997; Schragenheim and Dettmer, 2001). “Disruptions/fluctuations will not average out for the total system and most individual resources will be forced to perform below their capability” (Srikanth and Umble, 1997, Vol. 1, Chapter 4).
Resource Capacities Are Unbalanced to Each Other and to the Market Demand
The ideal goal that every operation strives to achieve is that of a balanced capacity plant—every resource has just enough capacity to meet market demand. A major effort of