Demand Driven Material Requirements Planning (DDMRP), Version 2. Carol Ptak

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Demand Driven Material Requirements Planning (DDMRP), Version 2 - Carol Ptak

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on the new ERP system and downplay just how much ad hoc work is really occurring.

      Another piece of evidence to suggest the shortcomings of conventional MRP systems has to do with the inventory performance of the companies that use these systems. To understand this particular challenge, consider the simple graphical depiction in Figure 1-2. In this figure you see a solid horizontal line running in both directions. This line represents the quantity of inventory. As you move from left to right, the quantity of inventory increases; right to left the quantity decreases.

      A curved dotted line bisects the inventory quantity line at two points:

      

Point A, the point where a company has too little inventory. This point would be a quantity of zero, or “stocked out.” Shortages, expedites, and missed sales are experienced at this point. Point A is the point at which the part position and supply chain have become too brittle and are unable to supply required inventory. Planners or buyers that have part numbers past this point to the left typically have sales and operations screaming at them for additional supply.

      

Point B, the point where a company has too much inventory. There is excessive cash, capacity, and space tied up in working capital. Point B is the point at which inventory is deemed waste. Planners or buyers that have part numbers past this point to the right typically have finance screaming at them for misuse of financial resources.

      If we know that these two points exist, then we can also conclude that for each part number, as well as the aggregate inventory level, there is an optimal range somewhere between those two points. This optimal zone is labeled in the middle and colored green. When inventory moves out of the optimal zone in either direction, it is deemed increasingly problematic.

      This depiction is consistent with the graphical depiction of a loss function developed by the Japanese business statistician Genichi Taguchi to describe a phenomenon affecting the value of products produced by a company. This made clear the concept that quality does not suddenly plummet when, for instance, a machinist slightly exceeds a rigid blueprint tolerance. Instead “loss” in value progressively increases as variation increases from the intended nominal target.

      The same is true for inventory. Chapter 2 will discuss how the value of inventory should be related to the ability of inventory to help promote or protect flow. As the inventory quantity expands out of the optimal zone and moves toward point B, the return on working capital captured in the inventory becomes less and less as the flow of working capital slows down. The converse is also true: as inventory shrinks out of the optimal zone and approaches zero or less, then flow is impeded due to shortages.

      When the aggregate inventory position is considered in an environment using traditional MRP, there is frequently a bimodal distribution noted. With regard to inventory, a bimodal distribution can occur on two distinct levels:

      1. A bimodal distribution can occur at the single-part level over a period of time, as a part will oscillate back and forth between excess and shortage positions. In each position, flow is threatened or directly inhibited. The bimodal position can be weighted toward one side or the other, but what makes it bimodal is a clear separation between the two groups—the lack of any significant number of occurrences in the “optimal range.”

      2. The bimodal distribution also occurs across a group of parts at any point in time. At any one point, many parts will be in excess while other parts are in a shortage position. Shortages of any parts are particularly devastating in environments with assemblies and shared components because the lack of one part can block the delivery of many.

      Figure 1-3 is a conceptual depiction of a bimodal distribution across a group of parts. The bimodal distribution depicts 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.

      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 distributions 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 the same survey noted earlier, taken between 2011 and 2014 by the Demand Driven Institute, 88 percent of companies reported that they experienced this bimodal inventory pattern. The sample set included over 500 organizations around the world.

      Three primary effects of the bimodal distribution are evident in most companies:

      1. High inventories. The distribution can be disproportionate, 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.

      2. 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 any 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.

      3. 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.

      Why the bimodal distribution occurs is explained in Chapter 3. It is a combination of basic MRP traits, the type of demand signal that is typically used in conjunction with MRP, and the complex volatile supply chain environment within which companies now must operate.

      Experienced planning and purchasing personnel know that if they simply follow what MRP recommends, they will be in big trouble. Shortages will increase. Excess inventory will increase. Expedites will increase. Intuitively, planners

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