Category Archives: Decision Optimization

CombineNet Communiqué II: Comparisons

Disclaimer: This blog, including this post, is not sponsored by CombineNet (acquired by Jaggaer). The author is not employed by, contractually engaged with, or affiliated with CombineNet. Any and all opinions expressed herein are solely those of the author. Furthermore, the opinions expressed herein should be contrasted with the opinions of other educated professionals before the reader forms his or her own opinion. Finally, the author is neither endorsing nor dissenting the use of CombineNet’s products or services – merely trying to spread awareness on the importance of optimization and the relative uniqueness of an offering like that of CombineNet. This disclaimer holds true for each post in this multi-part series and will be repeated.

Yesterday we recounted The Story To Date, and in response to the statements of CombineNet’s representatives that their product is designed for “everything in the bucket sourcing”, their optimization is easily accessible, their speed allows for more scenarios to be run in the same time frame – increasing the probability of a successful event -, they are now a hybrid of BoB (Best of Breed) and POE (Platform Optimization Engine), offering their clients the best of both worlds with their preconfigured templates, and even Jay Reddy, founder of MindFlow, openly acknowledged CombineNet’s optimization capabilities were without peer I noted that the reality was, more or less, (much?) better than it was (but easy is a relative term), definitely, more or less (but that’s not necessarily a bad thing), and pseudo revisionist history.

Today I’m going to indirectly address these issues by tackling CombineNet’s post The Make vs. Buy Dilemma, which was in response to my comments on their Analytics Support Negotiations posting.

In this post, Paul Martyn endeavors to offer a specific example of make versus buy to illustrate the saving potential an optimization-enabled sourcing process can unlock to demonstrate how Expressive Bidding unleashes savings and offers new insight into supply plans.

In this example, Paul uses the example of a seat assembly to illustrate that in addition to:

  • sourcing the individual components and assembling them (make)
  • sourcing the assembled components (buy)

one can take a hybrid approach where one considers

  • sourcing bundles of parts in combination with individual parts.

In his example, Paul illustrates a situation where sourcing individual parts (make) costs $129, sourcing the final product (buy) costs $120, but sourcing subassemblies, which may consist of the odd individual part, only costs $92.

Although this was a very good post, and one of the clearest posts out there on the power of optimization, it did not quite meet its goal because this is a problem that could be more than adequately solved by way of a leading platform optimization engine, such as Iasta’s, although it would take three scenarios instead of one (and possibly a few extra milliseconds of solve time), and a problem that could have been solved with a single scenario using an unreleased version of MindFlow’s optimizer, the former leader in the platform optimization engine category. In other words, it might take the right approach, a little creativity, or a little extra work, but some make vs. buy analysis can be done with platform optimization engines.

Does this mean that you don’t need best of breed? Not necessarily. There are problems that the platform optimization engines cannot solve. However, these tend to fall into two categories: deep and complex or highly specific. However, as it was defined, this was not one of them. But Paul was closing in on the right path, and tomorrow we will discuss a problem that you would be (very) hard pressed to solve using a platform optimization engine.

CombineNet Communiqué I: The Story to Date

Disclaimer: This blog, including this post, is not sponsored by CombineNet (which was acquired by Jaggaer). The author is not employed by, contractually engaged with, or affiliated with CombineNet. Any and all opinions expressed herein are solely those of the author. Furthermore, the opinions expressed herein should be contrasted with the opinions of other educated professionals before the reader forms his or her own opinion. Finally, the author is neither endorsing nor dissenting the use of CombineNet’s products or services – merely trying to spread awareness on the importance of optimization and the relative uniqueness of an offering like that of CombineNet. This disclaimer holds true for each post in this multi-part series and will be repeated.

For those of you following the sourcing blogsphere, you’ll know that I’ve been giving CombineNet a bit of a (good spirited) hard time lately on Spend Matters, e-Sourcing Forum (ESF) [WayBackMachine], and here on SI, but I’m just trying to poke and prod them into educating the sourcing community as a whole since I believe that decision optimization is still not well understood overall, and optimization is a much more involved topic than most people realize. After all, they have what should be the only real optimization blog out there, CombineNotes.

If you haven’t, I would highly recommend you read the spirited debates over on Spend Matters that resulted from the following posts:

Old News Keeps Flowing*
What Do Rubik’s Cube and Expressive Bidding Have in Common*
An Optimization Knock-Down!*

as well as the following CombineNet posts:

  • Project Zander and Comprehensive Network Design
  • CombineNet – The Allocation Company
  • Analytics Support Negotiations
  • Expressive Commerce and the Long Tail
  • Perspectives in Puzzling
  • The Make vs. Buy Dilemma
  • ‘Right Tool for the Job’ Sourcing

If you’re new to SI and missed my Optimization series over on ESF, you can still review part I, part II, part III, and part IV, and if you missed it, my first post on Decision Optimization is still available in the archives.

For those of you who’ve read the posts, and just want a quick recap, here it is.

I fear that optimization is not well understood and that much needs to be done to educate different users on the strengths and weaknesses of differing methodologies and solutions. There are upsides and downsides – the Rubik’s cube is simultaneously the best and worst analogy I’ve seen yet – the apparent complexity is that of a Rubik’s cube, but the actual complexity is much more so.

With respect to optimization, there is a sharp distinction between the problem, model, and solution (algorithm) and confusing them can be dangerous. If the model, and the modeling capabilities of the tool, are not appropriate to the problem, or not useable by the end user, it does not matter how good the solver (or solution algorithm) is. (MindFlow proved this point.)

There are embedded POE (Platform Optimization Engine) solutions, based on COTS (Commercial Off-The-Shelf) optimizers, BoB (Best of Breed) solutions with custom (proprietary) solution algorithms, and solutions that merge the two. The best solution all comes down to the problem at hand and its modeling capabilities – a better algorithm does not necessarily imply a better solution, although it will probably reach one faster. The model is key. Furthermore, there is a cost associated with pure speed – when it comes to optimization, you can only have any two of deep, fast, and accurate.

Depending on the problem, a better model may not save you more money – it depends on how fine grained the data you have available is and whether or not the model’s constraint representation abilities can support more advanced costing models. If your data is coarse grained, chances are the additional savings provided by a BoB solution will be negligible (less than 1%), if any. If your embedded solution limits the cost factors (i.e. forces you to combine unit, usage and/or transportation costs, for example), then a fine grained model may save you a couple of percentage points if you have detailed transportation and / or usage costs at multiple freight brackets. If your embedded solution does not support sophisticated discounts or bundle based costing, then a Best of Breed solution may save you significantly more, and the quoted range of 5% to 10% is very realistic. (However, if your embedded solution does support discounts and bundle-based costing, then a best of breed solution may not save you more than a few percentage points, if that much.)

CombineNet has some of the deepest models out there, they are one of the few companies with proprietary solution algorithms (which require a lot of brain power and development time), I thoroughly believe that their logistic models are best-in-class, but I’m not entirely sure that they are “without peer” when it comes to sourcing, primarily because of what I said in the last paragraph. It depends on the problem and, since this is business, the ROI.

Compared to many of its “peers”, CombineNet has a price tag that is directly correlated to its capability – quite high! (Based on quotes I have heard, one event could cost you as much as a year of unlimited events from an on-demand provider for a small team of sourcing professionals.) For some problems, I strongly believe that, in the words of the SpendFool a honda engine will do the job just as well as a lear engine, and if we are talking about a spend in the 10M range, then I would doubt the price tag is worth it. But if we are talking 100M+ spend on a very complicated category where you need to do an embedded make-vs-buy analysis (which I’ll elaborate on in a forthcoming post), I might actually advise you to spend money hand over fist on CombineNet because even an extra percentage point will generate a significant ROI for you. (For example, even if it only saved you two percent, on a 100M category, that’s two extra million to apply against your bottom line!)

My comments, as you might have guessed, did not go unanswered. According to CombineNet, their product is designed for “everything in the bucket sourcing”, their optimization is easily accessible, their speed allows for more scenarios to be run in the same time frame – increasing the probability of a successful event -, they are now a hybrid of BoB and POE, offering their clients the best of both worlds with their preconfigured templates, and even Jay Reddy, founder of MindFlow, openly acknowledged CombineNet’s optimization capabilities were without peer.

The reality, more or less, (much?) better than it was (but easy is a relative term), definitely, more or less (but that’s not necessarily a bad thing), and pseudo revisionist history. But these are topics for the forthcoming posts in this series, so I’ll leave you with a quote from the SpendFool:

This stuff (i.e. POE & BoB Optimization) isn’t mutually exclusive! Nothing wrong with using the consultants with big brains and tools to solve the really strategic problems AND also using mass deployed tools from ERP and/or SaaS vendors. Pick the right tool for the job. Anything else would be foolish.

Thanks SpendFool!

Looking forward to your comments on my (next) posts!

* All posts prior to 2012 were removed in the Spend Matters site refresh in June, 2023.

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.