(Guest Post) Optimization on the Ground: Making Complex Decisions Easier, Enabling Hard and Fast Savings

By Dr. Olga Raskina, Lead Scientist, Emptoris, Inc.

This article is the third in a three-part series of guest articles on optimization by Dr. Olga Raskina, Lead Scientist, with Emptoris, the supply and contract management solutions provider.

In the two previous posts I have provided somewhat of a birds-eye view of optimization, particularly in terms of the benefits of optimization in the sourcing domain, as well as a general view on the innovation and vision of the field.

I’ve been creating and promoting sourcing optimization technology for several years, and during this time I have heard various concerns regarding the applicability optimization technology when it comes down to real procurement events.  Three particular concerns or questions stand out as the most commonly raised:

• This is a very complex technology; it seems you need to be a rocket scientist to be able to use it.
• This is all great, in theory, but how does it apply to the specific procurement event I need to run next week?
• I have been working in procurement for years. What can it tell me that I don’t already know?

All of these are very fair concerns, so in this post I am going to zoom in a little more and try to address these questions by examining two recent experiences at two very different companies.

The first example involved several sourcing events at a large, global pharmaceutical company.

Procuring chemicals and chemical packaging is a seemingly straightforward process with a set of well-known out-of-the-box strategies. Yet there are a lot of inherent complexities to it, and manual procurement processes leave many questions unanswered, or answered approximately without full evaluation of all options and their costs.

The reason for this lack of evaluation is quite simply the complexity of the analysis.  Not the complexity of categories nor the complexity of the procurement itself.  Rather, it’s the complexity of making the right decision when a lot of factors and variations are involved.

As part of the process the procurement manager needed to answer questions like whether to go with cheaper plastic or more expensive glass containers, as well as which alternate specifications to select for each of the items.  Of course, they also need to examine the pricing for each possible alternative – and the trade-offs between going with incumbents vs. new suppliers.

With rigid processes set over the years, the company tended to deal mostly with incumbent suppliers, and made an occasional piecemeal evaluation of some of the alternate spec trade-offs.  With tens of thousands of items and a dozen of suppliers a more global assessment of all decision factors was simply out of reach.  The time alone to lay out all various decision factors was prohibitive.

Employing advanced analytical tools and specifically optimization to ensure all internal benchmarks and regulations are met ( e.g., awarding a certain amount of business to minority suppliers), as well as to evaluate the costs of dealing with incumbents and finding the best cost-quality combination of alternative specs,  allowed the company to save, conservatively, $8M.

The second example is an event from a Fortune 500 company sourcing transportation.  Like many other transportation events, this event had a large number of lanes across multiple countries, complex international tariffs and taxes calculations, and involved pricing strategies for close to a hundred of suppliers. In addition they needed to respect the various volume limitations of the suppliers across single or multiple lanes – and ensure adequate coverage of lanes on different levels (e.g., individual, region, country).  This was done to ensure they hedged risk, yet did not scatter the resources by working with too many suppliers or too many small suppliers on each level.

To complicate things even further, they needed to consider and present to the stakeholders the trade-off between working with incumbents vs. new suppliers (also on all different levels).  I am sure you can imagine the difficulty of combining and evaluating all these factors in a single analysis process

Employing optimization technology allowed the procurement managers to take full advantage of various volume discounts, to encourage suppliers to package lanes together with various price breaks, and to make sure all regulations and requirements were met.  The technology allowed for easy “one-click” what-if evaluations, such as what if I award 50% of all lanes in a region or everything out of a region to an incumbent, and everything else to the best new supplier?  What if that percentage drops to 30% and everything else goes to the best two new suppliers? If you think that’s easy, imagine the complexity of a much simpler task of just finding the best supplier from a region given all discounts, complex bundling of lanes, price breaks, tariffs regulations and capacity and coverage requirements.

With skyrocketing oil prices at the time, based on their extensive experience, the company estimated total cost would by at least 5% compared to the previous year.  However, with optimization to analyze the complex scenarios and help inform their decisions, the company was able to actually save well over $1M compared to the previous year.  The total savings vs. the projected spend were well over $10M. In my mind this is a perfect example of going from speculative theoretical benefits, to hard saved dollars, all in the face of current economic conditions and the well beaten path of past experience.

One very important detail to point out in both of these cases is that, despite common misconceptions, these events were not run by rocket scientist, or by any technical expert for that matter. These events were created and executed by internal procurement and category managers.

To me, this is a perfect illustration of the difference between manual processes and decision making – and technology enabled analysis and decision making.  The difference between guessing and knowing, between best and “good enough.”  It is the hard quantifiable difference of tens of millions of dollars, enabled by scientists like me, but achieved by procurement experts like you.

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