Guest Blog

Part IV : For Deep Insight Into Cost, Combine Analytics with (Should-Cost) Modelling

In this 6-part series, guest blog writer Michael Lamoureux, a freelance procurement expert, explores and takes a deep dive into spend forecasting. With years of deep domain craft in sourcing and supply chain, he shares the secrets of what it takes to keep up in today's fast moving economy. Read Part 1, Part 2, Part 3, Part 5, Part 6.

Great sourcing is about minimizing the total cost of ownership which goes beyond just the unit price or the landed cost, but also considers other relevant cost factors such as taxes, import/export duties, and interim storage costs.  But just minimizing total landed cost across a set of bids doesn't mean you are making the best buy.  It's only the best buy if the product and logistics costs are optimized.  In fact, even trying to model and optimize TCO (total cost of ownership) by also taking into account waste factors, inventory overhead costs, future disposal costs, etc. does not mean the organization is making the best buy.

Furthermore, while it might seem easy to just let the market optimize the total landed cost by going out to bid and inviting enough suppliers and logistics providers to provide the requisite competition, it's not always that easy.  Particularly when the organization is sourcing a manufactured product and there are only a few suppliers in the mix, who are not incentivized to get lean and push prices down (because they have a stranglehold on a significant percentage of the market) or, even worse, are in a position to collude to keep prices high.

But how do you know when the competition in the market is sufficient to drive an optimized cost or when it's just not enough? This is where should-cost modelling comes in.  One might think that basic trend analysis using market pricing or past pricing is enough to help one determine if the market bids are on track or not, but there are a few things one needs to remember here.  One, there are generally only market prices for commodities (via commodity indices) or consumer goods (tied to government contracts or public catalogs).  Two, the organization might not have enough price/bid history to accurately project what current pricing should be.  Three, the true cost of manufactured goods depend on a number of variables including, but not limited to, raw material costs, local labour (market) costs, (local) energy costs, and other manufacturing overheads (which are often dependent on the production technique used).   The only way to combine this data and arrive at a likely true cost is with a detailed should cost model.

But should cost models alone are not enough to help you understand where the best opportunities are, or how to go about realizing them.  That's where analytics comes in.  When should cost modelling is integrated with analytics, the solution can automatically calculate the current expected cost of a (manufactured) product, compare it to the current cost the organization is paying, multiply it by the expected demand over a given time period, and estimate the savings potential of (re)sourcing the category.

The organization can quickly see where the biggest opportunities are using current commodity, labour market, and energy market costs based upon should-cost models that take into account current production methods.  But this isn't all analytics can do.

If the supplier exposes its raw material costs, or if the organization has enough historical data for both the product and the component raw materials to determine the impact commodity market changes have on supplier pricing, it can also determine when a supplier is likely paying more for a raw material than it should be.  Then, if the organization is buying a number of (custom) manufactured products from the same supplier that use the same raw material, it can determine if it has an opportunity to lower costs further by helping the supplier achieve a lower raw material cost (by leveraging its negotiating skills on the supplier's behalf or by using its buying power to lock up all of the raw material it needs across its supply base at a lower cost and providing it to the supply base at cost).

If the organization also has data on production methodology for each (custom) manufactured component the organization buys, then the analytics platform can automatically compute the average overhead percentage and identify those products where, if a different production methodology is available, it might be advantageous to consider an alternate methodology and create a new should cost model.

Moreover, the power of analytics with should cost modelling doesn't end there.  It can identify those products where the organization is spending the most but has the least chance of reducing costs with a market event and where a lot of that cost appears to be in overhead.  These are prime situations for the organization to reduce costs by working with a strategic supplier to implement lean processes or six sigma to lower costs over time, or, if raw material costs are rising, keep costs flat.

In any case, analytics does a great job of allowing an organization to identify where it is over spending with respect to market costs or market quotes, but without should cost modelling, especially where the organization is buying direct (custom manufactured products), the organization will never understand how low the costs could be or how much value could be realized.

About the Guest Writer

Michael Lamoureux, aka the doctor, is the Editor-in-Chief of Sourcing Innovation (.com), a resource for sourcing, procurement, and supply chain professionals who are interested in improving themselves and the overall performance of their organizations. A regular contributor to Spend Matters, he is a Computer Science PhD who has been heavily involved in the Sourcing and Supply Chain Space since 2000 and the e-Commerce space since 1997. As a freelance procurement consultant with extensive expertise in sourcing, procurement, and supply chain processes, he aims to continually push innovation in and beyond the supply chain space. With particular expertise in analytics, modeling, and optimization, he is able to dive much deeper into technology and core issues, striving to help businesses with their internal knowledge transfer, positioning, and planning problems. 

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