The Science Behind Cost Savings

Posted: 09/15/2020 - 21:36
Cost savings is always measured against a set of expectations, and these expectations are only made possible by quantitative analysis.

Quantitative and scientific approaches to cost estimation have existed for decades and are increasingly accessible to procurement teams via technologies that automate data analysis. 

In many cases, these new capabilities simply accelerate the manual work that procurement professionals were doing using pivot tables and advanced Excel functions (e.g., show only rows with less than five days lead time) that effectively filter large numbers of line items in seconds. 

However, in addition to automating existing types of analysis, automation is making genuinely new types of analysis possible – analysis tasks that were simply not feasible when the negotiation tabulations were confined to a spreadsheet. 

This article overviews the top approaches to estimating costs and the applicability of these use cases. 

Cost Modeling

Description

Cost modeling is a type of estimation based on the lifecycle analysis of a product or service. Often referred to as “clean sheet analysis,” the approach takes the question of cost as separated from the overall market and simply asks: given the materials, labor, transport and packaging involved in delivering the material or good, what did it cost my supplier to produce this and how much can I reasonably expect them to discount their margin? 

The goal of clean sheet analysis is to understand the supplier’s marginand then to negotiate a reasonable margin reduction relative to the quantity being procured. Put simply, this methodology estimates volume-based discounts based on the lifecycle costs involved in each individual unit. 

Small retail quantities might assume a 15% to 20% margin, whereas much larger industrial quantities might offer economies of scale that can yield a significantly lower margin. This is the same reason that Costco can offer bulk discounts and Walmart can offer everyday low prices. Clean sheet analysis is the quantitative methodology behind what is popularly known among supply chain and procurement practitioners as “fact-based negotiation.”

Use Cases

Cost modeling is often used for procurements of highly complex, highly customizable and highly variable commodities, materials and finished goods. Its biggest proponents are in the manufacturing sector, where supplier relationships often entail the shipping of specific molds and tooling associated with production to a supplier’s physical location. This implies a significant inter-embeddedness between the buyer and supplier entity, a long-term relationship.

Category Benchmarking

Description

Category benchmarking is an approach that begins with analyzing prices of similar goods and services belonging to the same overall category. For example, if a company has $1.3M in HVAC spend, a category benchmarking approach would look at the average spend per supplier and the average savings achievable through strategic sourcing.

Inparticularly granular cases, benchmarking would even be able to determine whether capital expenditures, operating expenses and services sub-categories are disproportionately larger than average for the category of HVAC.

Use Cases

Category benchmarking is common in organizations that have a mature approach to category management, or that consume a third-party category data solution. As a strategic sourcing manager, I found this last analytics category helpful when looking at spend associated with the “Services” line items of a capital expenditure facilities category.

For example, where it was supposed to be under 10% of the total category, services had ballooned to over 30% since the previous year, showing that individual site locations were receiving maintenance that they did not need. 

Upon closer investigation, the category benchmark discovered the individual site location facility managers had unknowingly signed up for a routine preventative maintenance plan that was greatly excessive given their intended use of the equipment.

Category benchmarking is a high-level approach that can help managers identify outliers and zero in on opportunities once a project is in-flight. It can also be useful to determine which categories are low-hanging fruit for future savings.

Behavioral Price Analysis

Description

Behavioral price analysis is a brand new approach to cost estimation that forms the basis behind Bid Ops signature feature: intelligent first offers. 

The idea is simple: line item discounts are fundamentally driven by competition for an expected reward. Salespeople negotiate similarly based on the desire to win and fear of losing, and these behavioral drivers exist regardless of which spend category you’re sourcing.

That’s why the missing ingredient in clean sheet analysis and category benchmarking is front and center in behavioral price analysis: quantity and competition. 

The fundamental question that this type of analysis answers is: what is the impact of competition on the pricing of each line item’s quantity for my bidding process? 

Behavioral price analysis recognizes that price and cost are distinct entities. Quoted price offers are often dictated by the context and deal dynamics of a specific negotiation, rather than the spend category or the product lifecycle.

Use Cases

Behavioral price analysis is useful in forecasting the outcome of any competitive bidding process when you have baseline pricing data, either the last price paid or the average price. 

“Competitive” simply means that the buyer has multiple award options – not that there is an intent to change suppliers – and we have seen this approach help build win-win relationships in several categories (even in sole source negotiations). 

If you believe that competition or quantity impacts your negotiations, behavioral price analysis can be a powerful tool in getting the best price from the right suppliers.

The Final Word

Cost savings is always measured against a set of expectations, and these expectations are only made possible by quantitative analysis. Regardless of which industry or category you are focused on, a robust quantitative methodology is at the heart of successful bid management. 

By approaching cost savings projects with a hypothesis-testing framework, you can discover which analytics approach drives the most significant impact to your business and results in creating value for your supplier relationships.

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About The Author

Edmund Zagorin's picture

Edmund Zagorin is founder and CEO of Bid Ops Inc., the first company to apply artificial intelligence to vendor negotiations. As a procurement practitioner, Edmund experienced the tedium of pivoting spreadsheets to evaluate vendor bids based on a set of ever-changing qualitative and quantitative criteria, leading him to form the team that created the Bid Ops platform. Edmund frequently meets with groups of F1000 executives to discuss the evolving role of artificial intelligence in spend analysis, vendor audit, bid scoring and across the enterprise procurement stack.