Category Archives: Decision Optimization

Box Nation

… most of what America is now is just boxes going back and forth …
Stewie, Family Guy, Season 15, Episode 18

Seth MacFarlane is extremely insightful when he chooses to be. We not only have boxes on pallets in containers going back and forth between countries but we have boxes in trucks going back and forth between local warehouses, stores, postal outlets, and consumer residences … it’s a boxes in, boxes out society. And it doesn’t matter how much we optimize the boxes coming in if the boxes going out still cost too much.

The point is, you don’t just optimize the inbound supply chain if the outbound supply chain consists of lots of small deliveries that will considerably eat up the savings you worked so hard to generate. In order to keep costs down, you have to optimize these little boxes as well.

This means that you not only need to optimize:

  • packaging costs
  • (outbound) distribution costs
  • insurance costs

But you shouldn’t do separate sourcing events, because packaging is used inbound and outbound. Plus, distribution inbound and outbound uses trucks … and while inbound might typically use big trucks and outbound might typically use small trucks, not only is the situation sometimes reversed, but the same carriers often have big trucks and little trucks and the more volume you can source, the better the deal you can get.

And then there is insurance. While the insurance inbound will likely be of the supply chain variety, and insurance outbound will likely be small carrier insurance / goods insurance, it doesn’t mean that both policies can’t be sourced from the same provider, and that you can’t get a better deal simultaneous sourcing.

In other words, if you really want to save money and achieve sourcing success in Box Nation, you have to consider all the boxes, not just the inbound ones. And if you want to be successful, use optimization. Check the archives (linked) for more info.

Are We About to Enter the Age of Permissive Analytics?

Right now most of the leading analytics vendors are rolling out or considering the roll out of prescriptive analytics, which goes one step beyond predictive analytics and assigns meaning to those analytics in the form of actionable insights the organization could take in order to take advantage of the likely situation suggested by the predictive analytics.

But this won’t be the end. Once a few vendors have decent predictive analytics solutions, one vendor is going to try and get an edge and start rolling out the next generation analytics, and, in particular, permissive analytics. What are permissive analytics, you ask? Before we define them, let’s take a step back.

In the beginning, there were descriptive analytics. Solutions analyzed your spend and / or metrics and gave you clear insight into your performance.

Then there are predictive analytics. Solutions analyzed your spend and / or metrics and used time-period, statistical, or other algorithms to predict likely future spend and / or metrics based on current and historical spend / metrics and present the likely outcomes to you in order to help you make better decisions.

Predictive analytics was great as long as you knew how to interpret the data, what the available actions were, and which actions were most likely to achieve the best business outcomes given the likely future trend on the spend and / or metrics. But if you didn’t know how to interpret the data, what your options were, or how to choose the best one that was most in line with the business objectives.

The answer was, of course, prescriptive analytics, which combined the predictive analytics with expert knowledge that not only prescribed a course of action but indicated why the course of action was prescribed. For example, if the system detected rising demand within the organization and predicted rising cost due to increasing market demand, the recommendation would be to negotiate for, and lock-in supply as soon as possible using either an (optimization-backed) RFX, auction, or negotiation with incumbents, depending upon which option was best suited to the current situation.

But what if the system detected that organizational demand was falling, but market demand was falling faster, there would be a surplus of supply, and the best course of action was an immediate auction with pre-approved suppliers (which were more than sufficient to create competition and satisfy demand)? And what if the auction could be automatically configured, suppliers automatically invited, ceilings automatically set, and the auction automatically launched? What if nothing needed to be done except approve, sit back, watch, and auto-award to the lowest bidder? Why would the buyer need to do anything at all? Why shouldn’t the system just go?

If the system was set up with rules that defined behaviours that the buyer allowed the system to take automatically, then the system could auto-source on behalf of the buyer and the buying organization. The permissive analytics would not only allow the system to automate non strategic sourcing and procurement activities, but do so using leading prescriptive analytics combined with rules defined by the buying organization and the buyer. And if prescriptive analytics included a machine learning engine at the core, the system could learn buyer preferences for automated vs. manual vs. semi-automated and even suggest permissive rules (that could, for example, allow the category to be resourced annually as long as the right conditions held).

In other words, the next generation of analytics vendors are going to add machine learning, flexible and dynamic rule definition, and automation to their prescriptive analytics and the integrated sourcing platforms and take automated buying and supply chain management to the next level.

But will it be the right level? Hard to say. The odds are they’ll make significantly fewer bad choices than the average sourcing professional (as the odds will increase to 98% over time), but, unlike experienced and wise sourcing professionals, won’t detect when an event happens in left-field that totally changes the dynamics and makes a former best-practice sourcing strategy mute. They’ll detect and navigate individual black swan attacks but will have no hope of detecting a coordinated black swan volley. However, if the organization also employs risk management solutions with real time event monitoring and alerts, ties the risk management system to the automation, and forces user review of higher spend / higher risk categories put through automation, it might just work.

Time will tell.

Coupa Enters into a Share Purchase Agreement for Majority Ownership of Trade Extensions

SI typically does not do analyze of acquisitions and, unlike it’s brethren, does not do public analysis of transactions until the deal is done because it ain’t over until the fat lady sings, or in silicon valley, it ain’t over until the money hits the bank. And even though the chances of this deal not completing are, in the doctor‘s view, extremely small, he’s still going to withhold his analysis until the deal is done.

That being said, there are huge implications for both parties once the deal completes, and just like you should be doing risk mitigation when a potential disruption event is identified in your supply chain, you should be doing a cost/benefit advantage/disadvantage analysis as soon as a large acquisition that impacts your primary platform is announced. Every acquisition brings with it opportunities, but if an organization is highly resistant to change or locked into an existing platform or, even worse, a current (but now no longer) partner solution, there could be disadvantages as well. So do your homework and be prepared to take advantage of any opportunity that arises.

And if you want analysis, Spend Matters US and Spend Matters UK have chimed in already. You can start there. SI is providing these links as information only. While the doctor did provide his insight into the Trade Extensions’ technology platform strengths and capabilities for an upcoming piece on Spend Matters, he is not releasing his views on the merger (announcement) until its done and none of the speculations as to the implications of the merger in that piece are his. (However, the technology assessments of Trade Extensions are likely all his, and as these are not impacted by a business transaction, he will comment on these freely if asked. Great thing about software is it’s code, and code is algorithms, and algorithms is math, and it does what it does.)

Coupa Enters an Agreement to Buy Trade Extensions: A Game Changing Move For Strategic Sourcing by the prophet, Spend Matters US

Coupa Acquires Trade Extensions, Leading Sourcing Optimisation Software Provider by the public defender, Spend Matters UK

The SEC filing is online for those interested.

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!