Category Archives: Best Practices

The More Things Change … Outsourcing and Procurement Mastery

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 penned a post on outsourcing and procurement mastery that summarized the results of an Accenture study that found that, on 1B of controlled (normalized) spend, procurement masters achieved 30% higher savings with costs that were 50% lower.

Nothing has changed. If you have been following the Hackett group publications for the past decade, you’ll note that top performers always perform significantly better than average performers. Maybe not 30% cost reductions, but pretty close. For example, in Hackett’s most recent study, World Class Procurement organizations see 35% process cost reduction, which is quite significant. And just about every GPO publishes typical category-based cost reductions in the 10% to 30% range, which is easily achievable through advanced sourcing technologies such as spend analysis (to identify the opportunity) and decision optimization (to capture the opportunities).

The only thing that has changed is how disturbing it is that there is still so much overspend in the average organization — and how easy it is to identify it. By now the majority of organizations should own advanced sourcing and procurement technologies and be identifying the majority of these savings on a regular basis. But it’s still not the case. Over 40% of organizations don’t have a single modern sourcing or procurement solution.

We’re still way behind where we should be. In this regard, unfortunately, nothing significant has changed in a decade.

The More Things Change … Global Product Development

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

We’re starting with a piece we published a decade ago on the benefits and risks of global product development. In this piece we noted that while the risks of global product development are many, so are the benefits as outsourcing can often open the organization to talent pools it wouldn’t have otherwise.

However, as we pointed out, the benefits won’t materialize if the risks aren’t mitigated, as any risk can destroy an entire sourcing and new product development plan. And the strategies for mitigating risk, as identified in the original article, are as relevant today as they were then.

NPD (New Product Development) still requires product road-mapping and portfolio management, iterative design and validation, product architecture and system design across the value chain, knowledge management so nothing gets lost, IP management, talent management, and, most importantly the right Product Lifecycle Management platform.

Without an integrated platform to track what is coming from where in the supply chain, who is doing what, what events are occurring, which of those impacts could cause a disruption, and what the potential (cost) impact could be, the organization is literally flying blind.

However, we still don’t have one platform for NPD that also manages end-to-end supply chain risk. And this is risky business. We have great platforms for NPD and product costing (including, but not limited to, Apriori, I-Cubed, and Supply Dynamics) and great platforms for risk identification and management (Achilles, Resilinc, and Risk Methods) — but not an integrated risk-centric new product design platform.

The missing strategy is still missing. Will it finally materialize ten years from now?

Right Now, Savings Are Everywhere …

… because you don’t have your costs under control. While there is no such thing as true savings, because finding savings just means that you weren’t spending optimally to begin with, the reality is that you are not spending optimally. Not even in your most strategic categories where you are putting the most of your effort. This is because you are not applying both leading strategic sourcing decision optimization and leading spend analysis to this category across multiple levels on a global category scale. (Even if you own both technologies, chances are you don’t own best of breed in both, and even if you are that one in a thousand company, the doctor has seen the most complex optimization models that are being built by the average company, and they are still elementary compared to what models could, and should, be built.)

So, even if you are given an unrealistic savings target, if it’s 10% or less, it is easy to meet because, until you have applied these two advanced sourcing technologies to every single category, and done so in a three-year time span (as costs always creep back in to a category over time, and that’s why GPOs and niche consultancies find you savings on the same category again and again if sourced three to five years apart), there is overspending everywhere. So, if you can just get your CFO to write the cheque, acquire these technologies, and apply them appropriately, you’re going to find significant savings on the 60% to 80% of your non-tail spend, which hides even higher levels of savings (as we have discussed here on SI in the past).

And then, since the secret to cost control is to source everything, make sure you are buying everything that costs 5 figures or more through an RFX or Auction, and, in many cases, preferably one that is automatically configured and run for you by the platform with little buyer involvement beyond keeping the approved supplier database up to date and verifying the award before the contract or PO is sent to the winning supplier. And if you actually manage to find the majority of savings across your leading spend and tail spend, limiting potential year-over-year cost reductions to 3% in the following year, you’ve still only scratched the surface.

Just because your organization has optimized it’s spend, that doesn’t mean that your strategic / high volume supply base has optimized their spend. This is where supplier development and supplier (relationship) management comes into play. If you help your top x suppliers, where this X constitutes 80% of your strategic spend, and over 50% of your spend, save 10% by optimizing their procurement, you lower your costs on this half of your spend by 10%, and there’s another 5% without doing anything but process improvement. But we always know that savings don’t stop at process improvement, they continue with product improvements that enhance quality, reduce manufacturing costs, and reduce reliance on rare earth metals or non-renewable materials — all of which can be identified with the right innovation.

So, in CFO speak, savings are everywhere, and you should have no problem finding significant savings as long as you acquire, and apply, the right tools for the job. This means if you don’t have appropriate advanced sourcing technologies, you have to go get them. They are worth it.

There are 4 Modes of Innovation, But Only Two Types!

A recent article over on HBR.org on the 4 types of innovation and they problems they solve didn’t really discuss the types of innovation, but rather the modes. The author, who broke innovation down into the age-old 2*2 matrix, with domain definition on one axis and problem definition on the other, indicated that their was basic research — typically carried out by or with academia, breakthrough innovation — typically accomplished by skunk work projects, sustaining innovation — typically done by R&D labs, and disruptive innovation — that often comes out of VC-funded innovation labs.

As you can say, these are not really “types” but methods of innovation which can each lead to innovations that might be classified as basic, sustaining, breakthrough, or even disruptive innovations (so the names are quite confusing), and this leaves the question, what are the real types of innovation and how does innovation happen. (An academic might come up with a disruptive way to create new communications technology and the best-funded VC lab might, after years of research, just come up with a way to make a fabrication process more efficient, saving 20% of time and 10% of cost, and not discover a single revolution.)

So how is innovation accomplished? These days, it’s fundamentally accomplished in one of two ways — either using the tried and true method of good old fashioned human ingenuity or the new method of deep learning that can discover patterns, formulas, or correlations that humans can miss. But is this the kind of innovation we need? Or even want?

As per our last article where we asked if the end of the digital west was in sight, while these deep learning systems can, with enough data, make predictions that are much more accurate than the best human experts, the fact that they cannot explain their reasoning is very disturbing. Very disturbing indeed. Do we really want to trust them with a new drug formula that, while having the potential to save thousands, also has the potential to kill hundreds, with no knowledge of which individuals are at risk of instant death? the doctor hopes not!

While it’s okay to use these systems to identify the most likely directions of success, it’s not okay to use these systems to blindly choose those directions without independent verification and confirmation with rationale, deterministic explanations. In other words, while we should use every tool at our disposal, we should never replace human intelligence and ingenuity with dumb systems. Because, while there are two types of innovation in use these days, there’s only one real type of innovation — human innovation. the doctor hopes that we never forget it and return to the glory days where all innovation was human innovation.

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.