Category Archives: Market Intelligence

The More Things Change … Roll On!


Roll on highway, roll on along
Roll on daddy till you get back home
Roll on family, roll on crew
Roll on momma like I asked you to do
And roll on eighteen-wheeler, roll on (roll on)
Alabama

We’re still being promised drone delivery, but the reality is most goods still move over land by truck (even if rail is more environmentally friendly and uses less fuel and could be built to use renewable energy, especially for short haul or common lanes, if we went back to a powered track … ) … and those trucks Roll On!

Ten years ago we noted that, despite the fact you need those trucks to Roll On, your logistics could come to a screeching halt. You could wake up and there could be no one to drive the truck (due to a continuing driver shortage or a unionized strike), no fuel to gas it up (due to shortages caused by a natural disaster, etc.), or, worse yet, no truck at all (because your delivery company can’t afford the new insurance premiums, which is a harsh reality — a few years ago minimum insurance requirements were jacked up across the board and small mom-and-pop shops had to shut down, and it could happen again).

And the situation has’t changed. The sky isn’t falling, but without proper planning, that includes contingency plans, with the continual driver shortage, constantly rising fuel prices (and seemingly regular shortages due to poor planning and resiliency on the part of the providers and an increasing array of governmental regulations in developed countries), and increasing thefts of expensive automobile — and truck — parts (with restricted supply) to take advantage of high (scrap) metal prices, any company could find itself in a situation where the sky might as well be falling.

And the eight pieces of advice given by Lora Cecere (the supply chain shaman), then of AMR and now of Supply Chain Insights, are just as relevant now as then.

  • Plan for tighter capacity
  • Make Fuel Management Part of Risk Management
  • Get the Right Supply Chain Planning (SCP) Master Data
  • Rethink Customer-PickUp (CPU) Programs
  • Diversify Entry Ports
  • Face Reality — (Re)Design Your Network for Efficiency
  • Get Help from a Good Partner
  • Make U.S. Transportation Infrastructure an Item on the National Agenda*

As are the three pieces of advice the doctor gave you ten years ago:

  • Use Decision Optimization
  • Don’t Forget Security
  • Invest in Visibility

And the only thing we’d add is that when you have to spot-buy, spot-buy smartly. Use new services like Freightos. Don’t know who they are? Better find out … NOW! (SI Freightos intro … and they are Flippin’ Freight Quotes Faster than a Fleet-Footed Feline on Guarana … Still!)

*Best of luck getting anything accomplished with your current administration — hard to buy American to build American when there are no more American-made options!

The More Things Change … Supplier Intelligence

This week we’re revisiting posts from ten years ago to demonstrate that, to date, the more things change in Procurement, the more they have, unfortunately, stayed essentially the same.

Ten years ago we published a post on what you can’t afford not to know about your suppliers that summarized some key insights from Jim Lawton (who was VP of Marketing at Open Ratings until its acquisition by D&B, where he became SVP and General Manager of Supply Management Solutions).

Jim, who noted that global supplier insight can become as indispensable to sourcing and supply management as a stage is to an actor, also noted that in order to acquire this insight, an organization has to focus on:

  • supplier performance and quality management,
  • supply risk management, and
  • supplier content and connectivity

And nothing has changed. Any organization that wants to understand total landed cost from global markets and with predictability still needs these capabilities today. Considering that the the final cost of any purchased product is ultimately dependent on the supplier and its ability to delivery a product to spec on time and on budget with minimal defects, supplier performance management is as critical today as it was a decade ago.

Similarly, considering that a single disruption can wipe out the entire identified and negotiated savings on a category (as the result of a six week disruption), supply risk management still takes center stage. (This goes double when the chance of an organization not experience a disruption is 15% or less for any 12 month period.)

Finally, without an understanding of supplier policies, practices, and the providers your suppliers employ, you’ll never know whether or not they are adhering to your corporate social responsibility standards, whether or not they are implementing six sigma and other best practices to ensure quality and keep defects down, and whether or not they are buying from, or subcontracting component development to, third parties that don’t adhere to your quality, responsibility, or ethical standards.

Supplier Intelligence is as important now as it was then, and, most importantly, many organizations don’t have the depth of intelligence they should have, as evidenced on the relative lack of uptake of modern Supplier Relationship Management solutions.

Is the End of the Wild Digital West in Sight? I Hope So!

The MIT Technology Review recently published a great article on The Dark Secret at the Heart of AI which notes that decisions that are made by an AI based on deep learning cannot be explained by that AI and, more importantly, even the engineers who build these apps CAN NOT fully explain their behaviour.

The reality is that AI that is based deep learning uses artificial neural networks with hidden layers and neural networks are a collection of nodes that identify patterns using probabilistic equations whose weights change over time as similar patterns are recognized over and over again. Moreover, these systems are usually trained on very large data sets (that are much larger than a human can comprehend) and then programmed with the ability to train themselves as data is fed into them over time, leading to systems that have evolved with little or no human intervention and that have, effectively, programmed themselves.

And what these systems are doing is scary. As per the article, last year, a new self-driving car was released onto New Jersey roads (presumably, because, the developers felt it couldn’t drive any worse than the locals) that didn’t follow a single instruction provided by an engineer or programmer. Specifically, the self-driving car ran entirely on an algorithm that had taught itself to drive by watching a human do it. Ack! The whole point of AI is to develop something flawless that will prevent accidents, not create a system that mimic us error prone humans! And, as the MIT article states, what if someday it [the algorithm] did something unexpected — crashed into a tree. There’s nothing to stop the algorithm from doing so and no warning will be coming our way. If it happens, it will just happen.

And the scarier thing is that these algorithms aren’t just being used to set insurance rates, but to determine who gets insurance, who gets a loan, and who gets, or doesn’t get, parole. Wait, what? Yes, they are even used to project recidivacy rates and influence parole decisions based on data that may or may not be complete or correct. And they are likely being used to determine if you even get an interview, yet alone a job, in this new economy.

And that’s scary, because a company might reject you for something you deserved only because the computer said so, and you deserve a better explanation than that. And, fortunately for us, the European Union thinks so too. So much so that companies therein may soon be required to provide an adequate, and accurate, explanation for decisions that automated systems reach. They are considering making it a legal right for individuals to know exactly why they were accepted for, or declined, anything based on the decision of an AI system.

This will, of course, pose a problem for those companies that want to continue using deep-learning based AI systems, but the doctor thinks that is a good thing. If the system is right, we really need to understand why it is right. We can continue to use these systems to detect patterns or possibilities that we would miss otherwise, many of which will likely be correct, but we can’t make decisions based on this until we identify the [likely] reasons therefore. We have to either develop tests, that will allow us to make a decision, or use other learning systems to find the correlations that will allow us to arrive at the same decision in a deterministic, and identifiable, fashion. And if we can’t, we can’t deny people their rights on an AI’s whim, as we all know that AI’s just give us probabilities, not actualities. We cannot forget the wisdom of the great Benjamin Franklin who said that it is better 100 guilty persons should escape than that one innocent person should suffer, and if we accept the un-interrogable word of an AI, that person will suffer. In fact, many such persons will suffer — and all for not of a reason why.

So, in terms of AI, the doctor truly hopes that the EU stands up and brings us out of the wild digital west and into the modern age. Deep Learning is great, but only as a way to help us find our way out of the dark paths it can take us into and into the lighted paths we need.

Will Cognitive Dissonance Lead to the Inadvertent Rise of Cognitive Procurement?

Despite the fact that machines aren’t intelligent, can’t think, and know nothing more about themselves and their surroundings than we program them to, cognitive is the new buzzword and it seems cognitive is inching it’s way into every aspect of Procurement. It’s become so common that over on SpendMatters UK, the public defender has stated that “this house believes that robots will run (and rule) procurement by 2020”. Not movie robots, but automated systems that, like hedge fund trading algorithms, will automate the acquisition and buying of products and services for the organization.

And while machine learning and automated reasoning is getting better and better by the day, it’s still a long way from anything resembling true intelligence and just because it’s trend prediction algorithms are right 95% of the time, that doesn’t mean that they are right 100% of the time or that they are smarter than Procurement pros. Maybe those pros are only right 80% of the time, but the real question is, how much does it cost when those pros are wrong vs. how much does it cost when a robot is wrong and makes an allocation to a supplier about to go bankrupt, forcing the organization to quickly find a new source of supply at a 30% increase when supply goes from abundant to tight.

The reality is that a machine only knows what it knows, it doesn’t know what it doesn’t know, and that’s the first problem. The second problem is that when the systems work great, and do so the first dozen or so times, we don’t want to think about that someday they wouldn’t. We want the results, especially when they come with little or no effort on our parts. It’s too easy to just forget our knowledge that as great as these systems can be, they can also be bad. Very bad. Much more bad than Mr. Thorogood, who claims to be bad to the bone.

We forget because it’s very discomforting to simultaneously think about how much these systems can save us when they identify trends we miss while also realizing that when they screw up, they screw up so bad that it’s devastating. So, rather than suffer this cognitive dissonance, we just forget about the bad if it hasn’t reared about it’s ugly head in a while and dwell on the good. And if we’ve never experienced the real bad, it’s all too easy to proclaim the virtue to those who don’t understand how bad things can be when they fail. And this is problematic. Because one of these days those that don’t understand will select those systems, but not to augment our ability (as we would only use them as decision support), but to replace part of us and that will be bad. Very bad indeed.

So don’t let your cognitive dissonance get in the way. Always proclaim the value of these systems as decision support and tactical execution guidance, but never proclaim their ability to get it right. They give us what we need to make the right decision (and when they don’t, we’re smart enough to realize it, feed them more data, or just go the other way). They should never make it for us.