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, for any demand event, a variable number of items may be required (for example 3 one time, 2 the next time, 5 the next time), then a Gaussian (or normal) model would be more appropriate. Without understanding both the level and variability in demand, you cannot select the most appropriate method of review.
Frequency of Demand
If the item in question has infrequent demand (sometimes referred to as slow moving), then there will most likely be insufficient data to use a Gaussian model. Again, a Poisson model will be most appropriate. Conversely, high levels of demand will lend themselves to a Gaussian model.
A word of warning: be sure to understand the demand pattern over as long a period as possible. As we saw previously, demand data in a short time frame can be misleading.
Probability and Impact of a Stockout
Strictly speaking the probability and impact of a stockout are two characteristics, but here they are treated as one decision variable because they actually give each other context and are often misused.
The probability/impact decision is often used by practitioners as a reason (or excuse) for overstocking their inventory. The argument that is most often used is that the impact of a stockout is so costly that it overrides any consideration of the cost of the items stocked. This is especially so in industries where the cost of operational downtime is high. However, stocking more than might be needed based on physical limits or probability is pointless and a waste of money. (See also the section in Chapter 5: When is Critical Really Critical?) In terms of calculating the ROP, the probability/impact decision affects the service factor component of the calculation. It is, in effect, a risk decision.
Using a Gaussian model, the service factor is a part of the safety stock calculation (see Figure 2-4) and the values can readily be looked up in widely published tables. Figure 2-5 shows a sample calculation of the ROP using a Gaussian model.
Using
