This is an interesting topic. Many books have been written about this; dozens of specialized software vendors and consultancies have cropped up over the past two and half decades to address this; many top tier analyst firms have been putting out frameworks for almost three decades; and every Big X consultancy has their detailed methodology, 200 page strategy and implementation deck, and a dozen or so publications on their website documenting numerous benefits and case studies.

In fact, there’s so much material out there that if you asked your favourite clod or chat, j’ai pété instance to write you a book, put together an implementation plan, prepare an executive presentation, prepare an implementation plan for your team, and prepare a training plan for your supply base, chances are, believe it or not (and the author rarely does) any particular Gen-AI instance is likely to do a decent job of it because there is so much nearly identical material on the subject that the models are likely to be decently weighted to generate a decent facsimile of the material.

But here’s the question — is everything that has been defined, prescribed, optimized, and re-prescribed over the last two and a half decades the right approach? This might appear dumb on first read and sound dumber on first vocalization, but it’s actually pretty poignant. Just because whatever has been prescribed for the past two decades has worked just fine, that doesn’t mean it’s optimal. And just like modern drugs can deal with most symptoms of any ailment you might get, at least at first, that doesn’t mean they address the root cause. And if they don’t address the root cause, you can still get really sick, and even die if the symptoms the drugs are addressing are covering up a long term, slowly progressing, [incurable] cancer or autoimmune disease.

Plus, certain practices are designed to achieve end-states with certain assumptions in mind that are based on expected market conditions and activities that are expected to optimize to those conditions. In other words, its assumptions across the board (on what to do) based on assumptions across the board (on what is success) based on expectation across the board where the assumptions on what to do assumes that what got a result before will again (without any proper definition of the scenario in which it worked or proof that its effective) and the assumptions on what is success is only based on what has achieved the best results (and not the best results that could be obtained) and the expectations are simply hopes that what came before will come again.

Of course, given the reliability of some of the methodologies out there, the consistent results obtained by the best-in-class providers, and the wide-spread adoption of certain methodologies, it’s hard to argue with the assumption, the underlying assumptions, and the expectations because, for most organizations that adopt the methodologies, and stick to them, they get great results!

But what happens when the results flatline?

I’m asking because I expect there’s a chance it will happen, and possibly sooner than I expect, for companies who are already practicing good supplier performance management. There are a number of reasons for this. As per above:

• effective is not necessarily optimal
• you can get short-term results treating the symptoms, but you can’t get long term results unless you address the route cause
• everything is based on assumptions, on assumptions, against expectations

and

• market conditions are (drastically) changing
• all good things must come to an end
• managing after the fact is not the same as laying the groundwork for success

These are the reasons in particular I suspect there is a chance that current SPM frameworks and efforts might crash.

All Good Things Must Come To An End

This universal truth holds true for anything with a limit. Since every action has a limit when it comes to the minimum time required for completion, the maximum number of uses or lifespan of a product, the value that can be delivered against a metric, and so on.

Combine this with the definition of supplier performance management which focusses on measuring, analyzing, and managing vendor performance to cut costs, minimize risks, and drive continuous improvement, this means that SPM has a limit. This, of course, is nothing new — we’ve known this for decades. But, applied properly, SPM has worked spectacularly for decades because:

• only so many suppliers can be measured, managed, and developed at a time … so there’s an endless supply of projects
• suppliers constantly change … increasing the endless supply of projects
• technology in our space has kept improving, allowing for slow, continual, improvement
• the global trade situation was relatively static for two decades
• most companies haven’t, and still aren’t, doing SPM in earnest
• SPM is done by humans, who aren’t perfect and continuously make mistakes

It will continue to work spectacularly for the majority of organizations the majority of the time as a result of all of these reasons. But it may soon start sputtering for some organizations. The reasons being:

• the suppliers not measured, managed, and developed are the ones that will need it the most and cause you the most problems
• suppliers will suddenly become (effectively) unavailable unexpectedly — often before the contract expires
• technology has stalled in SPM
• the global trade dynamic has drastically shifted and has entered a new age of continual instability
• those doing SPM are running on outdated assumptions
• Agentic AI is being injected everywhere, and decreasing the reliability and effectivness

This means that the days might be numbered for SPM-in-a-box, which is pretty much what you get because most vendors / consultancies who do SPM just do SPM.

Which is concern #1.

More importantly, SPM is pointless if the supplier is going to suddenly become unavailable or unable to produce the products that you need.

A sanction, border closing (due to conflict or epidemic with pandemic potential), seaway closing, economic collapse, etc. can all take a supplier out of the picture overnight. Chances are you’re monitoring for this for your (most) strategic suppliers, can usually see it coming with enough warning to at least take some emergency measures.

However, chances are that this sub-tier supplier, or it’s sub-tier supplier, aren’t monitoring for potential disruptions like you are. What happens when those suppliers suddenly shut down or become unavailable. Then your supplier is unable to produce the products you depend on, and it’s essentially the same as that supplier suddenly becoming unavailable.

Which means you need Supply Chain Visibility in order to not only select appropriate suppliers, but ensure your suppliers are selecting appropriate sub-tier suppliers and, if not, help them to do so. However, not only are most companies still not adopting SCV solutions, but they aren’t part of standard SPM.

In addition, it’s not just availability, it’s supply line, and dependency. Rare earths are becoming more rare, oil keeps going up, and many renewable commodities in the food stuff categories are in relatively short supply due to population increase, droughts, and wildfires. Sufficient supply demands that dependency on any one raw material be minimized to the extent possible. This means that PLM, and especially design, alternate designs, and substitution capability is becoming more and more critical. But this is completely ignored by today’s SPM that just focusses on making the production process more efficient, not the product.

In other words, while the classic SPM theory is fine, and the implementation close to flawless in a handful of suppliers, the impact will soon be minimal to non-existent if they continue to address the wrong problem or try to address the right problem in a vacuum.

Now that process performance is often not the critical problem, the effectiveness of traditional SPM may begin to wane — fast.

Which brings us back to the starting question:

How Do You Execute Effective Supplier Performance Management in Today’s World?

I don’t have a good answer yet, but I believe you need to find one before the question becomes relevant.