Category Archives: Decision Optimization

And the (technology) brain-drain is finally official …

Today Emptoris (acquired by IBM, sunset in 2017) finally announces what we’ve all known for a long time (see David’s post on e-Sourcing Forum back in February), that it has acquired MindFlow Technologies, a leader in inbound supply chain planning and sourcing optimization.  I’m going to refrain from commenting at this time*, but say that I’m pleased that a North American company acquired MindFlow, because in today’s economy, brain-drain is a global phenomenon and I personally think that the last thing you want is your country’s best and brightest packing up and moving halfway around the globe after a merger or acquisition!

The press release should be up on their site by the time you read this, so you can check it out at your leisure.  They are also announcing a new service offering, Overdrive, to help companies drive adoption and accelerate the business impact of Emptoris solutions.  The offering includes assessment tools, adoption workshops, analytical reporting, and access to a knowledge sharing user community with benchmarking metrics.  I’m sure my fellow blogger Jason Busch over at SpendMatters will have a few gems to offer on this last topic, as it’s part of his vision for next generation on-demand spend management solutions#, so I’d keep a close eye on his blog to see what he has to say.

Personally, I think Overdrive is a step in the right direction for Emptoris.  They’ve done a great job acquiring companies with leading solutions in various areas of sourcing, and recently produced an integrated solution through SAP NetWeaver, but technology is only part of the solution.  Knowing how to apply it for maximum benefit is the other half.  I’m interested to see what happens next.

* However I did comment on Jason Busch’s take, Old News Keeps Flowing#, which I recommend you check out.  (CombineNet, acquired by Jaggaer in 2013, has even chimed in!)

# Link no longer available.  All posts pre-2012 disappeared with the site revamp in June 2023.

Procurement Lead Time Optimization

As I pointed out in my companion post on e-Sourcing Forum today (WayBackMachine) today, Lead Time Optimization, or applied Total Value Management Decision Optimization, is another innovative capability that some leading sourcing organizations are latching on to.

When you translate Lead Time Optimization, which Zara has used to design a flexible supply chain that allows the company to take a garment from design through the manufacturing process to store shelves in 10 days, to Procurement you focus not on maximizing profit but on minimizing costs against possible demand fluctuations.

In this scenario, you do not optimize your awards on a forecasted demand value, but a forecasted demand range and the solution you select is not the lowest cost solution at any specific demand point but the solution which maintains a lower cost over a demand range. The solution you select will, on-average, be lower than other solutions and yield a solution that is expected to be near-optimal regardless of what happens.

This requires a tool that allows you to capture not only all of your business constraints and supply chain flexibility requirements, but the costs associated with new suppliers, supply base consolidation, and mixed transport options. This in turn requires the ability to define global costs, cost modifiers that specify transportation mixes, and what if scenarios to take different possibilities into account. Outside of SupplyChainge’s (now Infor’s) offerings, these tools are rare, but I know for a fact that Iasta is pursuing a solution that will incorporate many of these best practices. I personally can not wait as there are too few players in the decision optimization market place and I personally think that many needs are currently going unmet because of it.

Decision Optimization Defined

Monday we defined a basic strategic sourcing process, indicated there were five critical process driven phases that can be greatly enhanced by software solutions, and indicated that we would spend one day discussing each of these technologies this week.  Monday we discussed spend management and spend analysis, Tuesday we discussed RFX, and yesterday we discussed auctions.  Today we are going to discuss my personal favorite: Decision Optimization.

There are a lot of definitions out there for decision optimization (often called bid optimization, award optimization, etc.) as it relates to strategic sourcing, but there are very few fully correct ones.  As far as I am concerned, decision optimization is the application of one or more rigorous analytical techniques to a well-defined model to generate the absolute best decision from a multitude of possible alternatives in a rigorous, repeatable, and provable fashion.

There are four key components to this definition.

(1) Rigorous Analytical Technique
Mathematically speaking, the analytical techniques used must be sound and complete.  In everyday English, the algorithms must always produce correct results and be capable of producing the optimal result.  In my book, heuristic, simulation, or evolutionary approaches, favored by some providers, that cannot always guarantee an optimal answer do not count as decision optimization, falling into the category of decision support.  However, hybrid approaches that use (mixed integer) linear programming would count since the heuristics merely guide the search in the most likely direction of the optimal solution, but do not prohibit the identification of the optimal solution even if it turns out to be an unexpected solution.

(2) Well Defined Model
The decision optimization component must not only insist on a well defined model but also allow you to completely and accurately represent your problem in the scenario definition.  Many optimization products on the market force you to over-simplify your problem to the point where the result is truly not the optimal result because you are missing key costs, constraints, or relationships.  For example, many early products (still) assume(d) that you are always shipping to one location or always buying from one location and do not allow true lane support.

(3) Best Decision
The optimizer must be capable of producing the absolute best decision given a sufficient amount of time.  As we mentioned yesterday, decision optimization is very hard and it is conceivable that an optimizer could take a considerable amount of run-time to find the optimal answer.  However, the implementation must support a configuration where the optimizer will not return until it proves the answer is optimal to whatever level of tolerance you specify, not just when it believes it has the right answer with high probability.

(4) Repeatable
The optimizer must produce the same solution or an equivalent solution each time it is run (for approximately the same amount of time).

Of course, this means that only CombineNet and SCA Technologies appear to be offering true decision optimization solutions now that MindFlow is out of the picture, but even then I find their modeling capabilities lacking in certain areas with respect to strategic sourcing needs. (On the other hand, I do not believe that anyone comes close to CombineNet’s logistical modeling capabilities.)  However, a few other companies are starting to make strong showings, and I fully expect that within a year Iasta in particular might have one of the best offerings based on what I saw in their initial foray into what they call Bid Optimization (released last December) and what I’ve been reading in e-Sourcing Forum (WayBackMachine) over the last few months.

When you consider the recent rampant inflation in energy and raw materials, the constrained capacity of many suppliers, the pressing need for improved top line and bottom results on the balance sheet, and the diminishing returns from traditional auctions at early adopters, decision optimization technology is only going to get more important. As I hinted at yesterday, I think the future leaders in the e-Sourcing space are going to be those that master decision optimization technology and its various applications.

Since this is one of those topics I plan on discussing a lot on this blog, I’ll keep this first entry short and conclude by saying that I firmly believe true decision optimization is the heart of a good strategic sourcing process and one of the best sources of value innovation that money can buy.