Smart Inventory Solutions. Phillip Slater
exists for that step; it is shown to indicate that at each of these steps the decision making is not a simple one dimensional activity. At each of these steps there are a number of internal processes and even individual behaviors and biases that can and will affect the outcome of that step. In addition there is the internal activity of Return to Store (RTS) that can short circuit the rest of the use–reorder–restock cycle. Although this figure is a simplified representation of the materials and inventory management cycle, it demonstrates that inventory management is anything but simplistic. This point is discussed in greater detail in Chapter 4: People and Processes.
Figure 2-1: The Materials and Inventory Management (MIM) Cycle
Comparison of Theoretical and Actual Inventory Management and Control
Inventory management and control refers to the actions associated with keeping the stock level of a particular SKU within predefined parameters. Figure 2-2 shows a classic ‘saw tooth’ diagram representing the theoretical movements of an SKU as it is used and reordered. In this diagram, the x-axis represents elapsed time and the y-axis represents the quantity on hand. This figure also shows how some of the definitions mentioned previously relate to the classic saw tooth representation.
The key simplifying attributes of the theoretical model are linear demand (that is, constant and equal demand over time) and instant and complete replenishment. In theory, when demand hits the reorder point (ROP), an order is placed for a predetermined quantity without need for further reference to the users of the item. There is then constant consumption over the lead time while the items are delivered. All items are delivered in one delivery so the item is completely restocked. The theoretical maximum is the Safety Stock level plus the ROQ. In the event that delivery takes longer than expected or there is greater demand than expected during the lead time period, then the quantity on hand dips into the safety stock (which is OK) and the item is completely restocked during subsequent cycles.
Figure 2-2: The Classic Theoretical Saw Tooth Diagram
The problem is, of course, that reality almost never looks like this. For engineering and spare parts, the chart in Figure 2-3 is far more representative. This graph has four characteristics that separate it from the theoretical profile. These are noted as A, B, C, and D on the chart and explained subsequently.
Figure 2-3: Actual Component Demand/Supply Chart
Point A: For this particular component the decision was made to set the initial parameters with an ROP of zero. That is, there is no safety stock. This level is more common for engineering materials and spare parts than many people realize and is not presented here to suggest that this ROP is either right or wrong. It is mentioned because this does not fit the common simplistic theoretical model that insists upon safety stock.
In this specific case, the ROQ was set to 10; hence, the theoretical maximum is 10 (ROQ + Safety Stock). Notice, however, that for the majority of the elapsed time in the chart the actual holdings are much higher than 10. Also, as the holdings rarely reach zero, there is nothing to suggest that setting the ROP at zero is inappropriate. Curiously, there are two instances where the holding increases without having reached zero — this is a clue to what is really going on, which will be discussed shortly. Thus, a traditional review of the ROP and ROQ would provide no improved understanding of how to manage this inventory item because the other elements of the MIM Cycle have far greater impact on the result than just the basic ROP and ROQ settings.
Points B and C: Notice that for this item there are long periods of no movement followed by short periods of multiple movements. Compare this to the theoretical model that assumes a constant and linear usage of items. The difference with the actual profile tells us that the average demand value that is so often used would vary enormously depending upon the point in the timeline at which the snapshot is taken; it is not constant or linear.
It is also interesting to note that the item is expected to be used in sets of 10 (hence the 10–0 setting). Yet of the nine issues of stock within the time line, only three are for the full set of 10. Clearly the management of this item requires insight beyond the obvious idea of setting a simplistic maximum and minimum.
Point D: Now notice the large spike in holdings on the right hand side (at the end of the timeline). This is the real issue with this particular component that was alluded to previously. This spike did not result from additional purchasing, but from a massive and sudden return to store (RTS) of items previously removed. Thus the apparent cycle of usage at point C was not usage at all (although the items were definitely removed from the storeroom). The purchases that were made to replace these items that were not actually necessary. (However, this was not known by those doing the purchasing; they were following their process.) The problem was that the maintenance people who removed the items did not advise anyone that they were not used (or that they may not be used). So, when they eventually had a cleanup and returned the items to the store they were now overstocked, compared to the theoretical maximum, by 21 items or 210%!
This example shows that the theoretical model and the actual situation can be sufficiently different so as to make the application of simplistic solutions not only pointless, but also even dangerous to company finances. A smart inventory solution is to ensure that the influence and complicating factors of all the elements of the MIM Cycle are considered for their impact.
Determining the Re-Order Point (ROP)
Now that some limitations of materials and spares inventory management theory are recognized, we must also acknowledge that someone must at some time determine when to order more stock. Deciding when to reorder requires calculation of the Reorder Point or ROP.
A number of different approaches are used to calculate the ROP, but once again a simplistic approach will not provide the best result. Calculation of the ROP requires consideration of a number of characteristics which help determine the approach that is best for that specific inventory item. Considering these characteristics is a reality that is missed by many software solutions that use just one approach. (Recall the previous discussion that the word inventory is used a collective noun to describe all the items held, although an inventory is actually made of many separate items that each have their own distinct characteristics).
In determining the ROP, the three main characteristics to consider are the level of demand, frequency of demand, and the probability and impact of a stockout.
Level of Demand
As we saw in the example above, demand is often represented as a perfectly linear equation. However, a linear outcome is more usually not the case. It is the variability in the level of demand that adds complexity to the calculation. This is why forecasting of many inventory types is such a widely-studied discipline. In order to calculate the ROP, you must understand the variability in the demand, not just know the average demand. Here’s why.
If the demand for an item is always for the same quantity on each demand event — for example, one electric motor or a set of four spark plugs — then a Poisson distribution is the most appropriate statistical model. (See Figures 2-4 and 2-6 for a summary of different statistical models). Note that at this stage we are considering the quantity, not the frequency, of demand.
If,