Category Archives: Best Practices

Exact Purchasing is a Pocket Cube Part 3

Today we continue our series on why Exact Purchasing is a Pocket Cube, continuing with the other two categories that are easier to effectively define.

High Complexity, High Risk, High Impact: Supply Chain Architecture

The classic “strategic” category in the Kraljic Matrix and the “cost architecture” category in the Busch Matrix, this is the toughest category to manage. It’s a category that needs to be architected, but not just from a cost perspective. The entire supply chain needs to be architected from the ground up!

Bills of material, substitution, and cost-to-serve is only the start. That’s how you deal with the high impact nature of the category. But that doesn’t deal wit the high complexity or high risk. The complexity management starts by monitoring the design stage data and understanding not only the potential material trade offs (which allows different raw materials from different regions to be used), but engineering trade offs (which allows different machining options and factories to be used), and even distribution tradeoffs (cold vs frozen, liquid vs solid, hazardous vs. not) to be considered and taken into account. And then there is the risk factor — optimizing cost vs complexity doesn’t deal with risk.

Risk in a high complexity, high impact category is the worst kind of risk you can have. Any disruption can be catastrophic. Even a little hiccup can be financially devastating.

Moreover, just monitoring for risk events isn’t enough — by the time you detect a risk event, it’s too late to do anything if you haven’t prepared for it already. You need to pre-design your supply chain in advance to absorb the risk event, because you won’t truly recover otherwise — no after-the-fact mitigation will ever be enough. You need to design your supply chain not only multi-source, but multi-regional so no single geopolitical, unrest, or (natural) disaster event can completely cut of supply, even for a limited time. You need to hedge bets in your carriers as well as your suppliers and raw materials. Your supply chain has to be designed from the ground up to adapt to any and every disruption imaginable that is likely to happen over a 5 year period.

You’re architecting your supply chain from a cost-effective managed-complexity supply assurance perspective — it’s a triple balance and overlooking any one aspect can result in serious disruption and loss.

Common categories are critical engine parts, ready-to-eat food products, key chemicals for your pharmaceuticals and health care products, and other processed chemicals and materials that make up your critical product lines. These are bill of material products that form the foundations of your primary product lines and can take your business down with them.

High Complexity, High Risk, Low Impact: Cost-First Architecture

In this situation you have a category which has all the complexity and risk of our last category, but the impact from even a worst case scenario will be manageable due to low impact. It’s the classic “bottleneck” category in the Krajic matrix and “relationship governance” in the Busch matrix. But neither is quite right. It can be a bottleneck if not replaced at some point, and relationship management might be key because the complexity limits the supply base, and this makes it a supply chain architecture category. Except, because it’s not a critical supply chain category and the organization can only design and monitor so many critical supply chain infrastructures at once (and this is one place where AI is of limited help … humans have to consider more factors than AI ever could due to lack of data), this is where the modelling focusses on the cost- and design-based aspects of the category and runs the model in real-time (on near real-time data) on every (re)sourcing event. If a disruption occurs, the model is spun back up, all current and projected data plugged in, alternate suppliers and carriers contacted to (re)confirm (product and route) availability and prices, and set up an autonomous sourcing event off of those pre-approved suppliers, carriers, and routes and re-secure supply at the best possible price as soon as possible.

This is where Busch’s model is mostly accurate. Fully up-to-date BoM, current material and ingredient options being tracked in real time, allowable substitutions from preferred materials and ingredients, typical cost-to-serve model, and relevant design stage data is a start — but it’s not mostly internal data — it’s internal data and external market price data, product availability data, and event monitoring data that could impact the decision you plan to make (and avoid options that might be as risky as the option you have now so that you get the lowest cost in a manner that assures supply at least in the short term.

It’s cost first, but not cost only. This is where Busch’s categories of packaging (when it has to be customized for the product), private label food (where you’re slightly altering and relabelling someone else’s TV, or is that Youtube, diner), contingent labour for sophisticated utility/commissioning projects, print and marketing (for traditional paper campaigns), and NPD.

In our next two installments we will move onto the more involved categories were complexity doesn’t match risk, where we end up with multiple categories being grouped into one in the Kraljic matrix (because high complexity or high risk means high on the blended dimensions), and we can’t source primarily based on impact (which is supposedly the big differentiator in the matrix model).

Exact Purchasing is a Pocket Cube Part 2

Today we continue our series on why Exact Purchasing is a Pocket Cube, starting with two of the easier categories to effectively define.

Low Complexity, Low Risk, Low Impact: Transaction Capture

The classic “non-critical” category in the Kraljic Matrix and the “transaction capture” category in the Busch Matrix, this category can be managed simply by capturing the transactions, ensuring they match the spend intent, and occasionally checking the market price. It’s simply checking the purchase against the plan.

As per Busch, the way to manage this category is to:

  • check every PO & invoice line against the contracted price
  • every shipping and handling charge against the current carrier rates
  • every delivery receipt against the order amount

It’s simply a matter of paying what you contract for and contracting at market price.

Great examples are Busch’s examples of MRO, office products, and commodity IT hardware. You’re just buying the lowest cost you can and making sure you pinch every penny and don’t allow your suppliers to purloin any of those pennies you negotiated.

This is a perfect category for (deterministic) autonomous sourcing. Once you verify the suppliers (which can also be automated if you are integrated into government, compliance, carbon, and legal registries), verify their account info (which can also be automated if you are integrated with the right financial entity verification systems), and verify the products, you can auto-source with tail spend auctions and / or RFQs, auto contract with integrated e-signature, auto-order based on projected utilization, and auto-pay on receipt. After all, if it’s low impact, there’s no real risk of a supplier going out of business or their supply becoming unavailable — you can just find another source. And if you have to pay a bit more, no big deal.

Low Complexity, Low Risk, High Impact: Continual Transaction Monitoring

Now, things get a bit more complicated when it’s low complexity, low risk, but high impact. According to Kraljic, it’s a “leverage” item and according to Busch, it’s a “market risk”. But it’s not market risk. The market risk is low. The risk is the degree of impact of sudden unavailability that could cause a price surge, the missed opportunity if prices plunge and you aren’t able to capitalize on the opportunity, or big delays in delivery that cause temporary stock-outs and missed sales opportunities.

But there’s no complexity to the category, and always another supplier, so it’s still a transaction focussed category. Except it’s not just capture, it’s continuous monitoring of your supply chain AND your potential supply chain. It’s not just the PO price, the invoice price, and the receipt — but also the ASN and the PO ACK and the degree of conformance. Lack of PO Ack could mean a lack of a sophistication, lack of ASN can mean a missed shipment, and “errors”, particularly billed amounts (well) over contracts can signal financial jeopardy — all of which can mean that shipment ain’t coming on time, if at all!

But full end-to-end transaction monitoring on its own is not enough! You need market monitoring as well, because, if you detect an issue, you will have to find a new (temporary) source of supply quickly and/or expedite the current one. More importantly, if you’re contract is coming to an end soon or your strategy is spot-buy every six months on a winner-take-all RFQ or e-auction, and a sudden influx of supply in the market creates a significant drop in price that you might not see again for years, you have to rapidly react to take advantage of the opportunity. It’s just good procurement.

This is where you might include Jason’s energy category, even though that could be a market risk, but would more likely include critical commodity categories (like RAM, that skyrockets after every decennial RAM plant fire), MRO categories like fertilizer (that spike with every strait closure), or cellular (internet) plans (where every change in regulation or new entrant causes a shift in prices).

In other words, you need to be monitoring the information supply chain and the corresponding market information chain 24/7 and logging not only delays and discrepancies between expectations but also discrepancies between your supply chain and the market — if prices or delivery times go down, quality increases, anything that would benefit you, you need to be aware and take advantage of that situation as soon as you can — but if the opposite happens, you need to take steps to ensure your continued relationship with suppliers who charge you less, delivery higher quality, and do so faster than your peers.

Exact Purchasing is a Pocket Cube Part 1

Last month we told you that Jason was right when he said that we need exact purchasing, but as we clearly stated then, and stated now, it’s NOT a new matrix. Especially when the original Kraljic matrix didn’t really fix anything in the first place (as it just gave us a methodology to start thinking about Procurement methodically so that we could start on a journey to actually fixing Procurement).

However, any methodology that wants to fix Procurement can’t just try to reinvent the Kraljic Matrix, even if it takes a data, vs process, centric approach. (Although the correct answer will involve both data and process.)

There’s two reasons for this.

First, any answer must take into account people, process, and data. (It’s not tech, tech is just that which implements the process on the right data with the support of people, who at least need to define the process the tech will employ if automation is being deployed.)

Second, any answer must properly take into account the complexity, market risk, and category impact. The only way this can be done is if EACH dimension is analyzed separately — not bundled together in some arbitrary mish-mash of factors that tries to pretend two (or more) dimensions are more-or-less the same.

In traditional Kraljic, you balanced profit vs a risk-complexity mish-mash. It sounded good, except risk and complexity are NOT the same thing. Risk is external (market) risk that you can try to mitigate, but that you have no control over. Category and product complexity is completely under your control — you control the design, the raw material mix, the production process, etc. You can choose to make the product simpler or more complex, use better or worse materials (as long as they meet minimum/maximum industry and government safety and compliance requirements), or less (or more) intensive production processes. Your choice.

In the proposed Busch model, you replace impact with influence and map that against a risk-complexity mish-mash, and then you use this mapping to translate Kraljic’s definition of what a category is into an actual data-backed strategy to purchase it. It’s progress, but not the answer.

The answer, as per our last post, is the pocket cube, where you break out risk and complexity into their own dimensions and deal with the categories accordingly. Especially when there is a mis-match between the risk and complexity ratings.

It’s easy when the risk and complexity match in severity, and Jason is dead on when the risk, complexity, and category impact (not cost, or profit, but criticality) are low and when the risk, complexity, and category impact (again, not cost, or profit, but criticality) are high. In the first case, it’s transaction focussed (but not necessarily continuous real-time transaction monitoring) and in the latter case it’s fundamentally a cost-based architecture, but more complex than Jason presents.

Where it gets tricky is the grey areas when there is a mismatch in two of the categories and, more specifically, when risk and complexity are diametrically opposite. But we’ll get to that in a later post. Starting tomorrow, we’ll take the first two of the four easy categories.

AI has NOT changed the fundamentals of Procurement. It HAS Strengthened Them.

Procurement, one of the last-areas of the back-office to be hit, is still drowning in the AI-Hype machine that is going full-force 24/7/365, as a result of the self-propagating A.S.S.H.O.L.E. that does nothing but excrete derivative nonsense on a continuous basis, piling it so high that it’s hard not be be Blinded By The Hype!

But, as we’ve seen, this new age of Agentic AI is not accelerating us into the Intelligence Age, but instead devolving us into the Neolithic Age (as it’s now been proven that these technologies are eroding [our] critical thinking skills, and only a few critical thinkers seem to realize that AI is dulling our minds).

Plus, it’s not effective. Studies by MIT and McKinsey last year demonstrated that only 5%/6% of early adopters saw a return. That’s a 94% failure rate, which is even worse than the general technology failure rate of 88% that is the highest it’s ever been in two and a half decades of project failure.

All AI has proven is that you can fail much faster than ever before, but still lost just as much money. That’s because the situation in Procurement is the same as in every other back-office function. Results come from the classic formula of:

  1. PEOPLE first
  2. PROCESS second
  3. TECHNOLOGY third

You need good people more than ever. Sure AI can “process” mounds of data at speeds we’ve never seen, but that doesn’t mean it can extract meaningful intelligence, and even if the intelligence is accurate, that it’s actually useful. Remember, these systems not only process data faster, they hallucinate faster than a field full of hippies at a Woodstock revival concert. But since their grammar and paragraph construction is now better than 90% of the population thanks to the social media revolution that has resulted in the average person having an attention span less than a goldfish and an IQ significantly less than our great-great-great Victorian grandparents, the majority of the population is willing to accept anything they pump out as accurate (even when it’s not).

Only top trained people can properly process complex situations, come up with the right solutions, and execute them. They should be using the most advanced tools available to them to process and make sense of the data using modern Augmented Intelligence technologies, but they should NOT be doing what a dumb system, guaranteed to hallucinate on a regular basis, tells them.

Once you have good people, they need to implement good processes that ensure best practice execution not only by them, but by everyone else who is involved in the process, inside and outside the organization (in partners, providers, and clients). Process allows emerging talent (with good education, great cognitive capacity, and an exceptional [dumb AI free] work ethic) to execute at the level of top talent with the guidance the top talent built into the process, and get the experience they need to become the next generation top talent in the organization.

Finally, once you have the right people, who know what to do, and the right processes, that help them get things done, then, and only then, do you identify the right technology to fit into, and accelerate, the processes. Maybe it’s AI, but chances are it’s traditional, domain-specific, (A)RPA that supports the process to automation levels of 95% to 99%. Dependable, fit-for-purpose, technology is always faster, better, and significantly cheaper than general purpose hallucinatory AI that may, or may not, work on any particular problem.

If you want to survive the current chaos, remember these fundamentals.

And if you can’t remember more than one fundamental, just remember PEOPLE first!

(While you can still find, and hire, people who know what they’re doing. Those of us who grew up before tech took over are getting older and greyer. Without us, not only will you not survive today, but you’ll have no one to train your staff for tomorrow. To think that, as a race, we survived The Great Extinction and, more recently, the The Great Decline during the Younger Dryas era only to risk global civilization collapse as a result of The Great Retardation.)

The optimization era is finally beginning!

In a recent article, Koray Köse states that the EU just killed global supply chain optimization.

When, actually, they just ushered in the real optimization era.

If you are a true multi-national, as Koray has said, you have to pick 2 options out of the 3 options available since you can not simultaneously satisfy US CHIPS Act, EU IAA origin/low-carbon requirements, and Chinese local content rules. So you have to decide which 2 options are the most valuable to you (based on costs and revenue opportunity in the market). That’s an expected profit optimization based on predicted sale prices and the localized supply chain optimizations for cost computation.

So you have to run 3 different sets of scenarios against different assumptions and Pareto efficiencies — and as humans we just can’t do that, and today’s AI can’t do that either (despite the over-hyped claim to the contrary). You need optimization to pick/justify your options, and then ongoing optimization to keep costs, and revenue, in line with prediction as global events force you to reroute regionalized and localized supply chains, substitute materials due to shortages, etc.

What was killed was the simple concept of global optimization that was relatively easy to do without optimization (and what passed as optimization for the past 25 years). Up until now, the reality was that, if you had even a few constraints, and the ability to do simple math, you could quickly eliminate the most expensive suppliers and the suppliers that couldn’t meet your constraints, and then, using your constraints, cherry pick the lowest or second-lowest cost supplier/distributor, and come up with a solution that was within 1% to 2% of theoretical optimal, but that was actually more optimal in practice as it was more stable and easier to maintain.

Optimization is only needed when you need to make choices that can’t be made without considering multiple sub-cases, regionalizations, and localizations — and this is exactly what this messed up world has given us!

It becomes even more important if you are a true multi-national with business in, and government commitments in, the US, EU, and China. You have to adhere to all of the rules globally, but you can’t with any one product formulation, so you have to create at least 2 different products where you figure out what 2 of the three combinations are easiest AND cheapest to make, where to make them, and how to supply them to the countries you serve (with there will be one country each mix cannot be imported into). This requires a host of scenarios to be run before a selection to be made, and a host of models to be continually run during production and distribution to ensure everything aligns with changing market conditions.

So while the classic optimization vendors who can’t do anything more than minimally constrained global optimization are now dead, it’s finally opened up the era of real optimization. The question is, what vendors are going to step up to fill the void?