Why Procurement Should Automate Should-Cost Modeling

What would you like to bank upon when negotiating and budgeting? Static should-cost tools or more dynamic versions?

Yet, most procurement organizations continue to deploy inelastic models, never tapping into the full benefits of should-cost modeling.

A more effective approach would be a dynamic and fully automated should-cost model to replicate the cost structure needed to produce the final product or service.

This is because accuracy is crucial when estimating expected prices based on cost drivers such as raw materials, energy usage, transportation, packaging, storage, byproduct credits, fixed costs, overheads and profits.

There are many outcomes of following a dynamic approach: collaborative negotiations, enhanced price discovery, future budgeting and planning, risk mitigation, and, most importantly, savings.

It is also possible to apply this approach to develop models of different scale — from simple commoditized products to highly engineered systems with thousands of components.

Here are three main benefits of dynamic should-cost modeling:

1.  Edge in negotiations

Should-cost models are a strategic as well as a tactical tool for supplier negotiations. By understanding a supplier’s cost structure, negotiators can focus on the most important cost drivers.

An effective should-cost model helps deconstruct complex negotiations and lets the negotiator focus on one specific cost driver at a time. It also helps create a different approach to negotiations. It steers the conversation to a fact-based and data-rich discussion rather than towards an emotional outcome.

This, in turn, helps create a more collaborative approach to negotiations and enables a win-win situation.

With automated models, buyers and sellers can quickly align on data, assumptions, and outcomes, providing long-term benefits in supplier relationship management.

2.  Pulse on market conditions

Automated should-cost models enable stakeholders to understand the impact on their supply chains when the underlying costs change. This can include price changes of chemical feedstocks, supply shortages, newly imposed tariffs, or macroeconomic shocks like an oil price crash.

It is critical for procurement leaders to understand how changes in cost structures impact the final product(s) or service(s) in their supply chain model.

In many cases, small changes to supply or demand may yield large changes in price when the underlying cost structure is inelastic.

Market conditions can change quickly, and companies need to be prepared to understand the impact on business.

In addition, when there are significant cost pressures or market forces in play, a dynamic should-cost model can be useful in evaluating alternative production processes or production scenarios (for example, 100%, 75% or 50% operating rates).

For example, in the chemicals category, it would be difficult to determine if a chemical developed from an alternative process would result in savings or cost creep without the aid of a robust should-cost model.

3.  Risk minimization

Companies seldom produce one product or offer one service. More often, they provide an assortment, which means their supply chains are also increasingly complex.

Creating automated should-cost models is one way to help companies scale resources and manage risk effectively.

Thousands of different cost drivers might be needed to accurately model the cost structure of a portfolio. Having a single analyst or even a team of analysts to update models is ineffective and tedious.

Advanced technology solutions can help companies scale and monitor their risk exposure as a portfolio.

In fact, the same underlying cost driver can be used across a range of chemicals and one may discover that the overall risk exposure to a given company is higher than anticipated.

Automated should-cost modeling can help identify these dependencies and leaders can develop appropriate risk mitigation strategies.

In summary, dynamic and fully automated should-cost models can help companies at all stages of operations – from supplier negotiations to risk management.

By automating should-cost models, not only can companies gain competitive edge and deliver savings without significantly increasing overheads, they can also expand their technical capabilities and achieve better results.