A few years ago, we published two white papers about Should Be Costing, The Road to Should Be Costing Part 1 and 2. In the papers we provide an overview of different (and easy) methods buyers can use to develop should cost models.
Our Cost Management Certification provides the knowledge and tools to develop and use such models. However, we have moved away from using the Should Cost methodology. Instead, we have moved on to Predictive Pricing, using the models to predict supplier pricing.
Predictive Pricing is developed by applying statistical analysis to suppliers’ quotes vs. the cost model estimates. Predictive pricing analysis uses cost models as standard measuring sticks to evaluate supplier pricing.
The summary charts shown in the figure below enable the buyer to tell supplier B that they are losing new business opportunities because their quotes are consistently 9-12 % over the cost model. While their competitors are winning business at 3-5% over the model.
The buyer can use the summary charts as well to provide an immediate response to a request for pricing. If the cost estimate comes in at $3.00, the buyer can review the supplier quote to variance percentages represented on the chart and provide some direction. Normally I would choose the lowest possible number that more than one supplier can hit. Since both suppliers A and C have the potential to quote at 5% over the estimate, I would give a $3.15 to $3.20 range to sales.
Further statistical analysis enables us to calculate the probability a supplier will quote a price of $3.10 or $3.20 if our cost estimate comes in at $3.00.
In the next few weeks, we will be re-writing our Should Cost papers with to show how we can move beyond should cost to predictive pricing. If you want to help guide our re-write, look at the existing papers and let me know what you would like included in the revision.