The batching policies that dictate the MRP equation include order minimum—the amount that must always be ordered; order maximum—the largest quantity that can be assigned to an order; and order multiple—a rule that governs ordering between the minimum and the maximum. The order minimum and maximum should be evenly divisible by the order multiple.
An example: an intermediate component can have an order minimum of 100, a multiple of 50, and a maximum of 500. This means that if the intermediate component has a parent demand of 102 pieces, a minimum of 150 (the minimum plus the next multiple) of the component must be ordered to cover that demand. At some point later if the parent requirement changes to 99, the intermediate component requirement drops to 100. The parent changed by 3; the component changed by 50. The effect of this complication is devastating in any environment where ordering policies, particularly minimums and multiples, are dramatically different at each level of the bill of material.
Figure 3-11 is an example of the demand amplification in a more complex environment. An end item (FPA) has three components. All three components have minimums and multiples assigned. A demand of 115 for FPA will yield demands of 150 for Intermediate Component A (ICA), 250 for Intermediate Component B and 200 for Intermediate Component C.
Batching practices can dramatically affect the way that material moves in a supply chain, contributing to or amplifying the accumulation of delays. For example, delay accumulation could occur while an order waits on a truck for other orders to fill up the truck. The transportation batching policy dictates that only full trucks are allowed.
The logic and policies behind batching policies can be very problematic. Most batches are heavily influenced by an emphasis on protecting unit cost and have no consideration for flow. That emphasis on unit cost actually further distorts the flow of relevant information throughout most companies. The assumption that driving to unit cost performance equates to the best return on investment performance is unequivocally and mathematically proven false. Yet industry ignores this fact every day. This subject, however, is technically outside the scope of this text. For an in-depth look at this issue, refer to Demand Driven Performance: Using Smart Metrics by Debra Smith and Chad Smith.
FIGURE 3-11 Batching complications to MRP supply order calculations.
Are the challenges described in this chapter and Appendix A unknown to seasoned planning professionals? Absolutely not. These challenges are well known and common. They explain the existence of the poor asset performance, the work-arounds, the bimodal inventory distribution, and the bullwhip effect. Additionally, they leave most planning organizations in a huge dilemma: utilize MRP or ignore it. The answer to this dilemma is almost always the same; do both. Most organizations are simultaneously ignoring and utilizing MRP. Just how much ignoring and utilizing is something that tends to be specific to organizational functions and the individual users. There has to be a better way.
MRP enabled organizations to quickly calculate and synchronize total requirements given a set of demand inputs. This was of particular importance when the company had a deep bill of material or many shared components. The whole purpose of MRP was to synchronize connections and dependencies. In the New Normal there are undoubtedly more connections and dependencies than ever. Thus MRP should be more relevant today than ever. Yet MRP is failing in the New Normal.
MRP’s role in the modern supply chain is significant. Even in the New Normal, the heart of every supply chain is manufacturing, and at the heart of manufacturing is MRP—it is the conductor of the supply order symphony in every supply chain. Each node in the supply chain has an MRP system supporting a different manufacturing operation. Therefore, a primary limitation of any supply chain will be how well MRP systems perform not just individually at each node but also collectively throughout the web.
If industry wants more agile manufacturing and supply chains that protect and promote the flow of relevant information and materials, then industry will need a more agile form of MRP. As evidenced in this chapter, companies cannot simply expect to implement conventional MRP better to get the necessary protection and promotion of flow. The first building block of a more agile form of MRP will be explained in the next chapter. This building block will mitigate if not largely eliminate the bullwhip effect by simultaneously addressing both demand signal distortion and material supply distortion by dealing with the core problem driving the bullwhip effect. This building block is called “decoupling.”
Unlocking a Solution— The Power of Decoupling
This chapter establishes a primary solution element to eliminate the bullwhip effect and create a framework for a proven and practical method of planning and execution for the conditions of the New Normal.
Chapter 3 described how the conventional planning approach featuring Material Requirements Planning (MRP) directly leads to the distortions of relevant information and materials that comprise the bull-whip effect. Figure 4-1 summarizes the connection between MRP’s core trait of making everything dependent (our previously alluded to core problem) and the distortions to relevant materials and information. The boxes at the tips of the arrows are effects of the boxes at the tail of the arrow.
At the bottom of Figure 4-1 there is a rounded box with the words “MRP treats everything as dependent.” There are two primary paths that lead from this box. The first path has to do with distortions to relevant information. That path is noted with dashed rounded boxes with no fill. This path shows that since MRP treats everything as dependent then manufacturing and procurement cycles are simply too long to respond to actual demand. This forces the use of forecasted demand which means the initial signal is in error by definition and that the demand signals will change as the incorporation of actual demand or changes to forecast occur. This triggers nervousness which creates constantly changing signals or leads to distortive behaviors to compensate for the nervousness (weekly buckets and/or BOM flattening).
Figure 4-1 culminates with an effect of distortions to relevant materials. Of course, it will be very difficult to have the “the right material at the needed time” if relevant information is distorted. But even if relevant information was not distorted, if demand was known and accurate and did not change, the effect that “the right material is not ready at needed time” would still exist. This is the second path depicted by the shaded boxes to the right. Since MRP treats everything as dependent, then all of the timing and quantity requirements in its plans are subject to those dependencies. Chapter 3 shows how dependent networks suffer performance erosion. An MRP plan, even with perfect demand information, will only be realistic if everything goes exactly according to plan with no variation.
This core problem of MRP has remained in place in large part because calculation dependency was developed as the real power of the MRP tool. If this dependency calculation was removed, then the true value of the MRP tool has also been removed. Yet as described in Chapter 3
