Solving Margin-Optimising Problems in Inventories
Changes in product availability can be expected to produce sales and margin gains when increased, or sales and margin penalties when reduced. Although the logic in these observations is generally not questioned, the size of these gains or penalties is hard to quantify since they are non-linear. A second determinant of sales and margin is, of course, the relative price of the product. Again, we have the problem of estimating sales and margin changes following price changes. FDC ICS has developed an aid to the solution to these two sets of problems using two new product-specific KPI’s.
For the cost / benefit analyses of availability targets it is important to quantify the gains and penalties associated with availability changes. The FDC ICS solution to the problem is to calculate a Service Loss Multiplier (SLM), which is a measure of this gain or penalty potential. An SLM of 1.0 suggests that 100% of the demand for the stock-out items will be lost, with nothing going on customer backorder. An SLM of 0.4 suggests that only 40% of the demand for the stock-out item will be lost, with the remaining 60% going on backorder. The same logic applies equally to potential sales gains from increases in availability.
Furthermore, a high SLM (0.8 and above) for a product suggests a very elastic demand curve, where total revenue will increase following price reductions. Similarly, a very low SLM (0.2 or less) for the product suggests a very inelastic demand curve, where total revenue will increase following price increases. The SLM is an indication of the likely price-elasticity of demand that applies to the product and becomes yet more meaningful when combined with the Turn & Earn index for the product.
The Turn & Earn index is an indicator of the behaviour of total net margin, after stock holding costs. It is computed by multiplying the margin of the product by its stock-turns, as corrected for actual availability achieved. For a given stock level, the higher this index, the greater the proportionate net margin produced by the product over a period of time. Products with a high index are proportionately spinning off a great deal of net margin, and vice versa. (The definition of “high” and “low” in this context is specific to an industry and, possibly, to an individual company within the industry.)
Assuming an appropriate stock level, a low index signals a problem that could be caused by inappropriate pricing. If the product has an inelastic demand curve, it may be possible to raise the index by raising the price. Conversely, if the product has an elastic demand curve, it may be possible to raise the index by lowering the price. This suggests that we need to determine the price-elasticity of products if we are to optimise pricing and, therefore, to optimise total profit margin per product. (The Turn & Earn index is a measure that may be used to test for optimised margin.)
Armed with the knowledge of each product’s SLM as well as its Turn & Earn index we are in a position to look for combinations of these KPI’s that would identify products with possibly inappropriate pricing. Any product having a low SLM as well as a low Turn & Earn index should raise questions. A low SLM suggests a well differentiated product with few ready substitutes. In other words, it probably has an inelastic demand curve where total margin (and Turn & Earn index) will rise as the price is increased. In this case the low Turn & Earn index suggests that the product may be under-priced. An over-priced product, too, may show a low Turn & Earn index but, in this instance, it will also show a high SLM. Other meaningful combinations of these useful KPI’s are also possible.
Access to each product’s SLM allows us to estimate the margin-optimising availability setting. Visibility of each product’s Turn & Earn index allows us to identify the products that are candidates for product rationalisation programmes or for pricing reviews. The combinations of these two KPI’s allow us to divide and rank the products in the “Probably Over-Priced” and the “Probably Under-Priced” baskets. The result of these important decision inputs is better-targeted product availability, effective price reviews and better-optimised total margin from stocked products.