One Night Stand or Dating? Sourcing Meets Supply Chain: Optimization Goes Dutch (Part 1)

I recently talked to Trade Extensions about how one customer, a fast-moving consumer goods (FMCG) company, used optimization beyond sourcing to factor in broader supply chain design and planning processes. The example resonated to such an extent that I asked Garry Mansell, Trade Extensions' CEO, to provide further explanation that we could give Spend Matters readers with the intent to inspire them into considering how to extend the influence of their sourcing capabilities, addressing potentially larger cost reduction opportunities. I should also note that the "client" in this case was actually a consultancy carrying out this work on behalf of the company mentioned above.

This particular case example involves leveraging a set of data points to optimize production planning based on demand, specific price points and capacity across multiple facilities. As further background, the project required cost elements to include product, transport, duty and other related areas. In addition, supplier lines had to be qualified for each product before any allocation could be made and the ultimate "expressive pricing" included regional variances, volume bands, and multiple discount structures. In other words, the ultimate solution required significant complexity, even compared to hairy sourcing-specific optimization events.

Garry told Spend Matters that the "problem presented and solved was to supply over 500 components to any one of over 30 manufacturing plants to meet a demand plan for finished goods in the USA FMCG arena. The planning horizon for the finished goods and the components they are comprised from was a 5-year rolling demand forecast ... the requirements of the client and the products themselves made this a problem beyond most planning applications on the market today."

Specifically, "the task was simply to produce the lowest-cost solution to meet the provided demand forecast." But as Garry suggests, "those of you familiar with such things will know problems of this nature are well known as being 'computationally hard'."

Stay tuned as we dig into the situation and outcome in Part 2 of this post.

Jason Busch

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