This content does not express the views or opinions of Spend Matters.Apple has made famous the use of ‘should-cost’ modeling and benchmarking as a cornerstone of effective procurement organizations for the past few decades, which has led many companies, big and small, to adopt the practice. The momentum around this ‘best practice’ has been further accelerated by the marketing efforts of market intelligence companies selling data and even a few purpose-built software platforms to make these efforts more efficient and accurate.While these models offer valuable insights into cost structures that are useful for driving an understanding of market dynamics and setting category strategy, especially when used in conjunction with other tools like SWOT and Porter’s Five Forces analyses, they also come with significant limitations when companies attempt to use them to guide negotiations and evaluate negotiation outcomes.This article will outline those limitations and propose that invoking competitive negotiations with machine learning solutions such as Arkestro is the optimal way to evaluate the market competitiveness of price quotes.These models don’t scale to all categories or all items within a categoryTo get to the level of sophistication for a should-cost to be effective as a true measure of a competitive market price requires both a high amount of category expertise, as well as time to create the model and back-test it against past results. This, given the productivity constraints of modern strategic sourcing teams, means that they often must choose between refining the assumptions and calculations in their model and doing other things, like studying the broader market, building supplier relationships or solving tactical challenges like shortages.Even where benchmarking data sets do exist and little modeling is necessary, it still requires bandwidth and a skilled eye to vet the models and data sets across every item within a category for every category of spend. This is a cost to the organization on top of the ‘hard’ cost required to evaluate, purchase and operationalize an accurate, vetted dataset for the many categories a typical company might be buying.Then, if a company has developed models and/or benchmarking data for a set of their categories, when used as the target, or measure of a ‘good’ quote from a supplier, it requires additional human attention if the cost that is quoted from a supplier is different from the modeled cost. Whether it is higher or lower than the ‘should’ cost, the question is always, is the model correct or is the quote competitive? Finding the answer to this question across tens, hundreds, or thousands of parts can create a wasteful cycle of time-consuming analysis.The core assumption of should-cost analysis is flawedShould-cost models assume cost-plus pricing, but most companies, especially in competitive industries, use value-based, dynamic, or competitive pricing strategies to optimize profits by focusing on customer value rather than production costs.IP-dependent products like microprocessors and pharmaceuticals are priced far above production costs to recover RD investments and reflect consumer value. Similarly, Apple commands higher margins than competitors like Dell due to its brand and the loyalty of its customers, even for functionally similar products

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