While this reduces the amount of changes to intermediates (since there are none) and this reduces the total number of action flags, does it produce more relevant information or actually distort the picture further? The key to more relevant information is not to simply ignore dependencies. When we ignore critical dependencies, we risk oversimplification. Oversimplification means to simplify to the point of error, distortion, or misrepresentation.
The bill of material files used in a planning system should reflect how the product is actually made. Dramatically flattening bills like the example in Figure 3-7 effectively ends any capability to provide visibility to and plan for synchronization between the finished and purchased part levels. The price for this is paid by the manufacturing floor as scheduling and schedule execution become an order of magnitude more difficult.
FIGURE 3-6 Daily versus weekly MRP runs
FIGURE 3-7 The flattening of a bill of material
All of these factors combine to mean that MRP is producing plans:
This means that the very nature of MRP combined with the way that it is typically used inevitably leads to distortions to relevant information. Furthermore, all of these distortions to relevant information have been related to one single attribute of MRP. Have you spotted it yet?
Distortions to Relevant Materials
The next consideration is the supply portion of the bullwhip—the distortion of relevant materials. As mentioned previously, MRP creates a synchronized and precise plan at all levels of the bill of material based on its required inputs and assumptions. This plan will happen only if everything in the entire dependent network goes precisely according to plan. In almost every modern environment, this is an impossibility for two reasons.
First, there is a basic and inherent level of variability in any environment, even one deemed to be in control. Deming called the normal or random variation that occurs in processes “common cause variation.” Normal or random operational variability results in a process that may be statistically within calculated control limits but still varying between those limits. Reducing the gap between the limits is a worthy goal. The elimination of the gap is an impossibility—it would require every process to be perfect.
We know that any process cannot be perfect. The collective effect of this imperfection must be examined. Figure 3-8 appeared in the first and third editions of Orlicky’s Material Requirements Planning. The figure has three columns. The first column is the number of components required to make a parent item. The second two columns are different levels of average component availability. The left column assumes all components have 90 percent availability, whereas the right column assumes 95 percent availability. For example, a parent item with 4 components that average 90 percent availability has a 65.6 percent (.9 × .9 × .9 × .9) chance that all components will be available simultaneously when required. A parent item that has 10 components that have an average of 95 percent availability will have a 59.9 percent chance that all components will be simultaneously available when needed.
Figure 3-9 shows how less than perfect material availability results in an erosion of the probability that all materials will be present when needed. Remember, MRP assumes full allocation—no order should be started unless all the components are available. In fact, even if many components have extremely high variability or arrive early, the parent order release is still at the mercy of any one missing component.
Figure 3-9 illustrates an environment in which four of the materials have high availability while one component has low availability on average. Components 1, 3, 4, and 5 have extremely high average availability (95 percent, 98 percent, 97 percent, and 99 percent, respectively. Component 2, however, has a relatively low average availability level (72 percent). The impact that component 2 has on the overall probability that all components will be available when required is significant; that probability drops to 64.4 percent. This translates to delays in the planned release.
FIGURE 3-8 Probabilities of simultaneous availability
FIGURE 3-9 One problematic material
Thus a simple rule emerges with regard to dependent structures that contain integration points requiring simultaneous inputs to advance to the next stage of the structure or plan. This is a valid description of the plans that MRP generates. This simple rule is “delays accumulate, while gains do not."
Figure 3-10 conceptually illustrates this effect. A dependent structure is visible at the bottom of the graphic. In this case that dependent structure is a synchronized plan based upon product structure. There are concurrent paths and integration points culminating in a finished item (FPA). Above the structure there is a graphical depiction of delay accumulation. The arrow steadily rises as activity progresses through the planned build. The arrow’s position at any one place depicts both how far along the planned activity path the build is (X axis corresponding to the structure) and the amount of accumulated delay (Y axis).
This effect is only partially impacted by signal accuracy. In other words, the demand signal could be perfect, but delay accumulation will still affect the environment if normal and random variation exist in the resources required to execute those signals. This delay accumulation results in an effect that is frequently referred to as “supply continuity variability.” This forces two profound realizations:
1. From an execution perspective MRP will never create a realistic plan in environments of even moderate complexity.
2. Any true solution to the bullwhip effect must address both demand signal distortion and the material supply distortion (supply continuity variability).
FIGURE 3-10 Illustrating delay accumulation
Amplifying the Distortions to Relevant Information and Materials—Batching Policies
The distortion to relevant information and