Figure 1-8 is a conceptual depiction of a bimodal distribution across a group of parts. The bimodal distribution shows a large number of parts that are in the too-little range, while still another large number of parts are in the too-much range. The Y axis represents the number of parts at any particular point on the loss function spectrum.
FIGURE 1-8 Bimodal inventory distribution
In Figure 1-8, not only is the smallest population in the optimal zone, but the time any individual part spends in the optimal zone tends to be short-lived. In fact, most parts tend to oscillate between the two extremes. The oscillation is depicted with the solid curved line connecting the two disparate distributions. That oscillation will occur every time MRP is run. At any one time, any planner or buyer can have many parts in both extremes simultaneously.
This bimodal distribution is rampant throughout industry. It can be very simply described as “too much of the wrong and too little of the right” at any point in time and “too much in total” over time. In a survey by the Demand Driven Institute between 2011 and 2014, 88% of companies reported that they experienced this bimodal inventory pattern. The sample set was over 500 organizations around the world.
There are three primary effects of the bimodal distribution evident in most companies:
• High inventories. The distribution can be disproportionate on the excess side, as many planners and buyers will tend to err on the side of too much. This results in slow-moving or obsolete inventory, additional space requirements, squandered capacity and materials, and even lower-margin performance, as discounts are frequently required to clear out the obsolete and slow-moving items.
• Chronic and frequent shortages. The lack of availability of just a few parts can be devastating to many manufacturing environments, especially those that have assembly operations and common material or components. The lack of any one part will block an assembly. The lack of common material or components will block the manufacture of all parent items calling for that common item. This means an accumulation of delays in manufacturing, late deliveries, and missed sales.
• High bimodal-related expenses. This effect tends to be undermeasured and underappreciated. It is the additional amount of money that an organization must spend in order to compensate for the bimodal distribution. When inventory is too high, third-party storage space may be required. When inventory is too low, premium and fast freight are frequently used to expedite material. Overtime is then used to push late orders through the plant. Partial shipments are made to get the customers some of what they ordered but with significantly increasing freight expenses.
These three effects are indicative of major flow problems in most organizations. Furthermore, these effects are directly tied to conventional planning activities and efforts in these organizations.
The Supply Chain Level—The Bullwhip Effect
There is a phenomenon that dominates most supply chains. This phenomenon is called the bullwhip effect. The fourteenth edition of the APICS Dictionary defines the bullwhip effect as:
An extreme change in the supply position upstream in a supply chain generated by a small change in demand downstream in the supply chain. Inventory can quickly move from being backordered to being excess. This is caused by the serial nature of communicating orders up the chain with the inherent transportation delays of moving product down the chain. The bullwhip can be eliminated by synchronizing the supply chain. (p. 19)
This definition clearly deals with important points discussed earlier in this chapter. “Inventory can quickly move from being backordered to being excess” is descriptive of the oscillation effect with the bimodal distribution. Additionally, this definition deals with both information and materials. “Communicating orders up the chain” is the information component, while “moving product down the chain” is the materials component.
The bullwhip is really the systematic and bidirectional breakdown of information and materials in a supply chain. Figure 1-9 is a graphical depiction of the bullwhip effect. The wavy arrow moving from right to left is the distortion to relevant information in the supply chain. The arrow wave grows in amplitude in order to depict that the farther up the chain you go, the more disconnected the information becomes from the origin of the signal as signal distortion is transferred and amplified at each connection point.
FIGURE 1-9 Illustrating the bidirectional bullwhip effect
A massive amount of research and literature has been devoted to the bullwhip effect. However, very little, if any, of that body of knowledge has been focused specifically on its bidirectional nature. Most of the research has been dedicated to understanding how and why demand signal distortion occurs and how to potentially fix it by synchronizing the supply chain. Remember that this is one of the basic objectives for planning.
Yet understanding the bidirectional nature of the bullwhip effect allows us to see how this single phenomenon connects directly to the other three previous perspectives. Could the bullwhip effect explain the existence of the other three perspectives?
• The user level. The user experiences conflicting and constantly changing messages and material shortages driven by updated requirements from customers. To sift out appropriate data, personnel attempt to clarify the picture by using ancillary nonintegrated tools.
• The organizational level. Materials and components quickly move from excess to back order or vice versa as updated requirements from customers appear. Company personnel often attempt to compensate by inflating inventories through an increase in safety stocks.
• The macroeconomic level. Overall supply chain performance erodes, requiring more resources and spend in order to compensate for the erosion and keep up with increasing demands from customers.
What can the bullwhip effect really teach us? What can explain conventional planning’s failure to live up to the potential that was envisioned and to protect and promote flow? In order to answer these questions, we need to expand the previous equation to include the biggest determinant in managing flow—managing variability.
In Figure 1-10 we see an expanded form of the equation previously introduced. Variability is defined as the summation of the differences between our plan and what happens. As variability rises in an environment, flow is directly impeded. Conversely, as variability decreases, flow improves. And as evidenced by the bullwhip effect, variability can be and often is bidirectional.
FIGURE 1-10 Connecting variability to the flow equation
The impact of variability must be better understood at the systemic rather than the discrete detailed process level. The war on variability that has been waged for decades has most often been focused at a discrete process level with little focus or identified impact on the total system. Variability at a local level in and of itself does not necessarily impede system flow. What impedes system flow is the accumulation and amplification of variability. Accumulation and amplification happens due to the nature of the system, the manner in which the discrete areas and environment interact (or fail to interact) with each other. “The more that variability is passed between discrete areas, steps or processes in a system, the less productive that system will be; the more areas, steps or processes and connections between them, the more erosive the