Category Archives: Decision Optimization

Trump & Brexit Woes? Optimization is the Answer!

SI has been preaching the gospel of strategic sourcing decision optimization since day one, noting how it was the only way to not only achieve the year over year cost savings that could be identified by spend analytics but also identify additional value necessary for struggling under-staffed and under-budgeted supply management organizations to realize the value that was being demand of them. Year-over-year was key. During the noughts, thanks to the success of FreeMarkets and Ariba, everyone thought that e-Auctions were king, as the first e-Auction often returned 20%, 30%, or even 40% savings and the second a healthy 5% to 15% in a host of categories, but no one realized these savings were just a result of excess fat in supplier margins, shaved out by more aggressive, hungrier, competition looking for a chance to prove themselves and grow. Once the fat was trimmed, and inflation began to return near the end of the noughts, subsequent auctions not only failed to identify additional savings, but also resulted in cost increases.

SI knew this, as the early adopters were already beginning to experience this when SI started and multiple options for strategic sourcing decision optimization were available (CombineNet [now Jaggaer], Emptoris [now IBM], Iasta [now Determine], VerticalNet [now BravoSolution], Trade Extensions, and Algorhythm), but the auction providers had big marketing budgets (as a result of their big successes, % of savings contracts, and VC funding) and bigger mouths to spread the auction word. And by the time the blush faded from the rose, most organizations weren’t ready for what seemed to be complex solutions, so the focus turned to better RFX, should-cost models, spend analysis, and weighted evaluation models. This worked for simpler categories, and the fact-based negotiations shave the remaining fat while also identifying processes or unnecessary non-value add offerings that could be trimmed, and savings continued, but began to trail off. That’s why the leaders are slowly accepting decision optimization and why Trade Extensions has been growing aggressively year-over-year for the last five years or so.

But let’s face it … when 40% of the market still doesn’t have any Supply Management tool and only 20% of the organizations that due are leaders (which kind of explains the Hackett 8%), the adoption is still low and the usage still minimal. As long as savings can be squeaked out through other means (analytics, cost modelling, aggressive negotiation, GPOS, etc.), the average organization seems to be doing everything it can not to evolve. Cognitive Procurement is the buzzword, but cognitive dissonance is the reality.

But that could all be about to change. Why? Between Trump continually threatening new border taxes, border closings, and visa program overhauls and Brexit looming on the near-horizon, which will totally change the tax and border situation in Europe, supply chain costs are totally unknown for a large majority of global supply chains. Considering how many global organizations are headquartered (at least regionally) in the US or UK and how many more have their Procurement Centers of Excellence there (either in a distribution hub or a financial hub, of which New York and London are two of the biggest in the world), it’s looming chaos. Are your costs going up? If so, are they going up 10%, 20%, 100%? Are sources of supply going to be cut off due to trade bans? Is your best talent going to be locked out of the US or UK? It’s a nightmare waiting to happen. It’s enough to put even stock market traders into full panic mode.

So what do you do? You manage the risk? But how? Most of the traditional supply chain risk management platforms (Reslinc, Risk Methods, Achilles, etc.) are geared at supply chain visibility — attempting to identify potential disruptions [as a result of external or internal events] before they happen so that mitigation plans can be identified and put in place before they do. However, when the disruption is not an event but an unpredictable [and unaffordable] tax hike or border closing, these solutions, even those that reach level 5 on the Spend Matters scale, are pretty useless. That’s why Sourcing Innovation has recently stated that Supply Management Risk Management Needs to be Cranked to 11. (It’s important to go to 11.)

You see, the key to survival is “what if” the current supply chain becomes unsustainable due to a tax hike or border closing in the US or UK. Running a new scenario with all of the inputs except any lanes, countries of origins, and / or products where you expect to see disruptions, trade bans, or extreme import/export duties. And then running another new scenario under a different set of assumptions on lane, country, and/or product restrictions. Running scenarios at the product level and the category level. Running with current supply base, previous bidder supply base, and newly identified scenario supply base until you have a mitigation scenario that is acceptable and ready to go if something happens.

Only a good supply management decision optimization solution with what-if scenario support can do this – nothing else.

So, since we’ve all forgotten Kermit’s Lesson, this is what we’re left with. But considering how it will enhance your overall supply chain operations in these turbulent times, that’s not a bad thing.


On the Twelfth day of X-Mas (2016)

On the twelfth day of X-Mas
my blogger gave to me:
Optimizing Posts
Analysis Posts
Standard Sourcing Posts
Direct Sourcing Posts
Risk Management Posts
Sustainable Posts
e-Procurement Posts
some SRM Posts
some CLM Posts
some Best Practice Posts
some Trend Bashing Posts
and some ranting on stupidity …

The archives are full of posts on optimization. It’s the doctor‘s passion as it is one of only two advanced sourcing methodologies found to deliver double digit returns year after year after year, the one that is least applied, and the one with the most untapped potential. Data insights only take you so far. Optimization helps you do something about it.

Regardless of what any vendor might claim,
True Savings Can Only Be Identified Through Multi-Factor Optimization!
Anything else is just trimming the fat.

At the end of the day when the proverbial sh!t is about to hit the proverbial fan as the organization is still seeing red,
Only an Optimization-Backed Sourcing Platform will Answer a Buyer’s SOS.

That’s Why You Need Mass Adoption of An Optimization-Backed Sourcing Platform!

And in case you need a refresher, here’s
What Strategic Sourcing Decision Optimization Can Do!

In case you’re wondering, or still think optimization technology is in the usability dark ages, here’s a post on
Optimization: What’s Changed Since 2009.

And, before you think you have a hope of doing this in-house, here’s
Why You Should Not Build Your Own Decision Optimization solution!
the doctor is the leading independent authority on strategic sourcing decision optimization. Please heed his word.

The reality is that we’ve reached a point where it should be
Optimization-Backed Sourcing Platform … Or Bust!
Part I
Part II
Part III
Part IV
Part V

And even if you are applying strategic sourcing decision optimization today, all we can say is
So You Think You’ve Mastered Strategic Sourcing Decision Optimization? (Hint: you haven’t. But that’s a good thing. Just means there is more value to come your way.)

And, finally, we will wrap up this series by asking how we accelerate the adoption of optimization and analytics:
Part I
Part II

And leave you with our final thought-rant.
Millions Saved. Pennies Spent. Why Won’t They Learn?

Merry X-mas!

How Do We Accelerate the Adoption of Optimization and Analytics? Part II

In Part I, we began by noting that every organization that adopts Strategic Sourcing saves time, money, and reputation but that any organization that adopts Advanced Sourcing processes and platforms saves more. A lot more. Only advanced sourcing, based on analytics and optimization, saves an organization an average of 10%+ year after year after year, even when traditional sourcing methods fail.

We also noted that even though a tremendous saving opportunity exists, the adoption of modern analytics platforms and optimization-backed sourcing platforms is still minimal, even though modern platforms, unlike their predecessors, are easy to use (and can be used by even the most junior of buyers), suitable for all purchases, and ten times as affordable as they used to be (which means that the ROI they can deliver can be up to 100X their cost). This caused us to ask why they still aren’t being adopted.

We noted that leading minds, including the prophet, have speculated that the reason(s) may include perceived complexity, lack of visualization, lack of integration, lack of guidance, and lack of collaboration, but pointed out that modern systems addressed all of this and this means that the commonly perceived reasons for lack of adoption aren’t the real reason.

So what’s the real reason? Well, as far as the doctor can tell, either people aren’t getting the message, don’t believe the message, or still inherently fear the platform despite the message. The sad fact is all of these are true to some extent.

People aren’t getting the message.

First of all, there are only six providers that offer strategic-sourcing decision optimization (SSDO), but most of these providers still offer SSDO as a stand-alone module, with only two platforms being optimization backed sourcing platform (that makes the optimization and analytics capability available across the platform, where it can be used to score and compare RFX responses and select auction winners in real time against complex cost models laden with constraints). There is only so much education, and marketing, two providers can do.

In comparison, there are dozens of sourcing platform providers that don’t offer optimization, and don’t profit by focussing on the optimization (or even the analytics) message, and, as such, don’t preach those messages. But that’s not the problem. The providers that are threatened by optimization tend to promote the myths and do everything they can to discredit these providers.

People don’t believe the message.

Many first generation providers greatly over-promised and under-delivered with respect to their optimization platforms and, as the saying goes, once bitten twice shy. When your organization spent high six, or sometimes seven, figures and got a solution only the vendor could use, and only successfully on the categories they understood, your return was limited as you were only able to get big savings on a few categories, and could only revisit those categories every few years. As a result, given that these providers promised better usability, flexibility, and ease of use for years, and never delivered, many people don’t believe that modern platforms are years ahead and useable by even junior buyers.

People fear the message.

And different people fear it for different reasons, but they generally fall into two camps. The camp that fears they can’t handle it, or the camp that fears the organization can’t handle the results it will deliver.

They can’t handle it.

They fear that they will have to be highly technical, capable of understanding advanced models, and, most importantly, have a solid command of advanced mathematics. When you consider that, with declining education, only a small percentage of American adults are “proficient” at math (as the US ranked 21st out of of 23 first world countries in numeracy in an OECD survey in 2013), and optimization is based on some of the most complex math there is, the fear is understandable, even though its unfounded as modern platforms hide the complex math and all users have to do is make sure all of the costs, constraints, and associated data elements are entered.

The organization can’t handle the results.

Many fear that if the solutions deliver and they identify savings of 10% or more on a category they are supposed to be experts in, their competency will be called into question and the whole existence of the Procurement organization as it now stands reviewed, because if they were good at their jobs, why were they overspending by so much. This is a valid fear if the executives are idiots, but a smart executive should realize that no human can analyze millions of scenarios in a few hours; that while costs rise in some categories, they fall in others; and that when entire categories can be put out to bid at once, economies of scale in both production and transportation materialize and their interplay can achieve savings that would not otherwise be realizable.

So what do you we do?

Attack the bullshit.

First of all, since the education isn’t working, let’s counter the false claims made by the providers without optimization-backed sourcing platforms. Demonstrate that their claims are baseless. Even though the laggards will be swayed, the leaders and followers will come around.

Make it personal.

Stop selling and start enabling. Thanks to the lies, damn lies, and false promises of early vendors, the buyers that were burned no longer believe vendors. Providers, even direct competitors, of these platforms have to create communities where their customers can engage with their peers and discuss the truths of the platforms they have adopted, warts and all. A dedicated buyer will gladly look past a few warts if it means better job performance (and a bigger bonus) at the end of the day.

Educate the C-Suite.

Help potential buyers educate the C-Suite that the system will deliver better results ONLY in the hand of a true professional, that a significant part of the savings come from the economies of scale and new supply chain models enabled by the software, that optimization can only save so much when applied tactically to the status quo (or, more importantly, like an auction will only generate savings once if not properly applied). Make sure they understand that the current situation is not the result of poor performance by the buyer, but the organization providing the buyer with poor tools.

Any differing thoughts?

How Do We Accelerate the Adoption of Optimization and Analytics? Part I

Every organization that adopts Strategic Sourcing saves time, money, and reputation (that would result from poor sourcing that typically results from tactical buying), but any organization that adopts Advanced Sourcing processes and platforms saves more. A lot more. Only advanced sourcing, based on analytics and optimization, saves an organization an average of 10%+ year after year after year, even when traditional sourcing methods fail.

But despite this, the adoption of modern analytics platforms and optimization-backed sourcing platforms is still minimal, and considering second generation platforms have been in existence for about ten years, and third generation platforms have been hitting the scene for the last couple of years (which can do more than first generation systems ever imagined) that can now be used by even the most junior buyer. There’s no reason that these systems are not in every leading Supply Management organization and every organization that wants to be a leading Supply Management organization.

Why aren’t these systems, which can deliver an ROI not only many times their cost but many times that of every other system, not being adopted?

Well, there are still the rampant myths that they are hard to use, require a PhD, and are only applicable for complex strategic categories, but anyone who does even a bit of research will realize that these myths only had (a shred of) validity with respect to first generation systems. There is also the belief that they are unaffordable (as first generation systems required high six figures, if not seven figures), but again research will illustrate that powerful systems are available in the five figure range and best in class systems, which support the organization end to end, can be obtained on an enterprise basis in the low six figures (and often deliver eight figures of value year after year, a 100X return). But what’s the real reason, and how do we overcome it?

If we want to really accelerate adoption, we have to figure out the critical roadblock. Last year, the prophet, in his post on brainstorming how to accelerate the adoption of sourcing optimization suggested the answer resided in:

  • simplifying the non-power user experience,
  • providing dynamic global/geo analysis from a visibility and risk perspective,
  • including greater API-based connectivity to back-end systems,
  • providing decision guidance as to the best models to use and scenarios to run, and
  • allowing for the sharing of models, scenarios, and best practice guidance between users

suggesting that the real reasons were

  • perceived complexity,
  • lack of visualization beyond cost tables,
  • lack of integration,
  • lack of guidance, and
  • lack of collaboration.

But, just like the myths, these reasons don’t apply to modern optimization-backed platforms, which make it easy to import and export data (for file-based integration, which is all that is needed); visualize data on a map and against constraints and identified risks; share models and scenarios; use pre-packaged cost and constraint templates (which is guidance); and walk a user through the advanced sourcing process using a wizard.

So what’s the problem? Why isn’t the adoption being accelerated? We’ll address this in Part II.

True Savings Can Only Be Identified through Multi-Factor Optimization

A recent guest post from a vendor-employed guest contributor over on Spend Matters said to Calculate Your True Savings Using Predictive Analytics. While the doctor agrees predictive analytics can often give you a good data point as to projected savings, the reality is that it’s not always as accurate as you would like to believe and typically does not capture your best savings opportunities.

Why? Before we discuss the guest post, which did have some good points, we have to note that most predictive analytics algorithms work on trending and statistics on historical or market data, and while this can be highly accurate (95%+) the majority of the time (95%+), because market data is only historical and typically does not include data points on new (not yet introduced or announced innovations), detailed cost breakdowns on consumer / market prices, or operational insights into hidden inefficiencies whose correction can do more than shaving a few points off the top.

Going back to the post, the author states that if you use a Savings Regression Analysis (SRA) model based on multivariate regression of past-realized savings for a given subcategory to compute the savings potential under current market conditions, the target computed will be realistic, achievable, and likely mirror what you will do (despite the savings targets you set).

And this statistically based model will work if it is the same buyer (group) employing the same strategy on the same market base under similar conditions, but what could happen if a new buyer comes in that totally redefines the demand and the market strategy, or the market conditions have suddenly changed from supply shortage to supply surplus, or new production technologies could revolutionize production and trim overhead 20%? In this situation, this type of model will be significantly off.

Now, anything you can do to better predict savings is a positive, because, as the author points out, this allows for

  • better cash flow management (as you will better know your costs)
  • time to market optimization (as you will know the best time to source if you have leeway)
  • goal setting (as you won’t be trying to achieve the impossible)
  • performance management (as you can track against a realistic goal)

But while predictive analytics give a good data point, the best data point is when you use your market intelligence to build good should cost models, use optimization to minimize transportation and incidental storage and sales (and even taxation) costs (when sourcing globally), and use six sigma analysis to see if there is any opportunity to take cost out of a supplier’s overhead production cost. Going into this level of detail may indicate that while the product cost is likely to increase 1% this year (and explains why the predictive software says only 2% savings should be expected after heavy negotiations), an extensive analysis could show that a transportation network redesign could shave 3% and lean process improvements at your supplier could shave 2%, meaning that a cost reduction of up to 7% could be achieved with the right footwork (which is something the predictive model will never tell you). So use the predictive algorithms to establish a baseline, but never, ever stop there.