Category Archives: Technology

Supply Chain 2026 or Supply Chain 2008? Part II

Still continuing our “the more things change, the more things stay the same” theme, back in 2008, the Supply Chain Digest published an article on Key Trends Impacting Supply Chain Management and Logistics for 2008 where it asked a number of leading academics and practitioners what they saw coming. (Their responses are summarized in this SI post.)

Nine (9) experts weighed in and provided 24 thoughts on what they saw coming in 2008. Those thoughts more-or-less fell into seven themes, and for the most part, those themes are the same themes today. Moreover, the specific concepts addressed are more-or-less the concepts being addressed today. Let’s continue to take them theme by theme.

Automation / Tech

Three (3) of the nine (9) experts centered on automation/tech as a core theme and stated that they believed:

  • Enhanced Visibility and Automation will take hold in logistics
  • The firms that recognize that a fresh approach focussed on value, cash flow, and light, non-intrusive, web-service-based, value-add software components that work with existing solutions and technologies will be the ones that make progress.
  • Successful supply chain technologies will become solution/results focussed, not just technology-focussed.
  • On-Demand / SaaS will continue to gain traction – particularly in TMS

Enhanced visibility is now needed across the entire supply chain. If you don’t identify a disruption at the source three levels down your supply chain (mine collapse, plant fire, etc. resulting in a material or part shortage), you won’t have time to recover by the time your tier 1 supplier misses the delivery date.

The best solutions are those that plug-and-play with the ones you already have — that connect out of the box. That’s why I2O (the “term-du-jour”) solutions are so hot — they help an organization plug and play disparate systems together.

The real leaders are those that use technology as an enabler, not a talent replacement, and definitely not as the ultimate solution. They focus on next generation tech that augments intelligence and makes their talent more efficient.

Despite all the hype that Agentic AI will take over, since the majority of it is based on hallucinatory Gen-AI and failures are becoming commonplace enough that organizations are losing faith in the hype, organizations will soon return to trustworty multi-tenant SaaS based on (A)RPA and deterministically controlled/gated agents, and efficient, affordably priced next-gen SaaS will soon come to the forefront again.

Logistics

Two (2) of the nine (9) experts centered heavily on logistics as a core theme and stated that they believed:

  • The need for Integrated Logistics will increase.
  • Carrier bankruptcies will increase in 2008.
  • The biggest challenge carriers will face is staying afloat as lower volumes and reduced margins crunch their cash-flow.
  • The biggest challenge for shippers will be maintaining service levels as carriers exit unprofitable markets and lanes.

The need for integrated logistics is still increasing as the constant interruptions across the global supply chains come fast and furious. We’re in a reality where you need to re-route shipments on the fly at not only every cross-dock and modal interchange point, but at every intermediate stop. Major road, bridge and border closures will force new routes; port, canal, and strait closures will force alternative shipping options or a switch to air cargo; sanctions will force entirely new carriers, routes, and supply chains; and so on. Once you head down the wrong route, it can take days to backtrack and select a new one, potentially causing perishable cargo to be wasted. Once your cargo is boarded on a ship that might be halted mid-route, it’s too late to have any hope of getting it on time. Once it gets into the hands of a now sanctioned carrier or hits a port in a sanctioned country, you’re never getting it back.

Carrier bankruptcies are a regular occurrence every time recessions cause a significant drop in spending and bookings drop, oil prices shoot up, insurance rates shoot up, or global pandemics prevent critical maintenance parts from being obtained and too much of the fleet goes offline. We’ve had multiple instances of mass carrier shutdowns and bankruptcies over the past 20 years (2008 recession, introduction of Map-21 in 2013 [RIP-21], COVID, and now the tariff crisis (with carrier shutdowns in 2025 almost equalling the closure rate of RIP-21: about 8,000 last year compared to about 10,000 during RIP-21).

Now that volumes are still down on many lanes and in many sectors due to the tariff crisis and rising oil costs are crushing their margins again, carriers are again desperately struggling to stay afloat.

Maintaining service levels is a considerable challenge with declining margins and the need to maintain secondary lanes that will never operate at capacity in order to secure major contracts.

What Data Do You Need For Successful Procurement?

In a comment to a recent post over on Linked in, Mr. Buckingham asked Do you think that data driven decisions are clearly the correct thing to do, but that they tend to maintain the status quo and can be restrictive to innovation?

I couldn’t leave this one alone and responded that:

“They only maintain the status quo IF the data collected and constraints created are limited to those that support the status quo … which, sadly, they usually are …

As the Sourcing Optimization Grand Master Paul Martyn recently pointed out — the real context never gets mentioned, stakeholders just add “necessary” constraints, costs, and weightings that they know will heavily favour the incumbent

And as the Sourcing Simplifier Garry Mansell regularly points out, organizations fail because they only include the best lagging indicators in board presentations to ensure they can keep doing the same old, same old

But if you instead collect [only] external data on the market, and not internal data that supports the status quo, the tendency will be to focus heavily on innovation and change.

Data is the way to go, but it has to be evenly distributed across internal and external so you can get the full picture.”

Seemed like this is something that should be elaborated on.

For example, let’s say Procurement is trying to gauge the effectiveness of their category sourcing in a key category. They could demonstrate this through internal or external metrics. Internally they could show the average price per unit decreased year-over-year by a significant percentage. Externally, they could retrieve the average market price for the primary products and show they are paying less. If they only ever use one of these metrics, and it stays good, as Mr. Buckingham notes, it maintains the status quo, even if it shouldn’t. In order to truly gauge effectiveness it has to, at the very least, measure both. Just reducing costs doesn’t mean you are getting the best price, and just beating the market doesn’t either. In some categories, market quotes / GPO rates / etc. are never the best price, which is dependent on what you actually need, who, and where, you are getting it from.

Digging deeper, if the specs are over engineered, you restrict to a suppliers in a certain geography, or only deal with incumbents, you’re not getting the best price. So you not only need to take internal and external measures and benchmarks, but ensure you are taking the right internal and external measures and benchmarks.

And then, if there are hidden costs (from increased risk, revised shipping / order lead times, quality reductions, etc.), take this into account as well. (Which is why you need strategic sourcing decision optimization with what-if scenarios, as we’ve been telling you for the past 20 years.)

Only when you identify the right data and collect the right data can you make the right decision, which might reinforce the status quo, might tell you do do something completely different, or tell you to split your bets and do both!

The reality is, you should always make data-driven decisions, but you can only do so if you are collecting the right data for your needs.

AI is Not AI … And AI-Related Tech is Not Created Equal

Joël Collin-Demers recently made a post that correctly stated that real “AI solutions” tell you exactly what they do, in which sequence, and [help you] understand how it solves your exact problem. the doctor totally agrees. Otherwise, they are using buzzwords and trying to cash in on the hype to sell you old-school automation at best, or third party (Gen-AI LLM) wrappers at worst, and don’t have any real AI.

He covered ten different types of technology and attempted to capture the positives, the negatives, and the best uses therefore in source-to-pay. For a relative non-techie (compared to the doctor with a PhD, degrees in CS and Mathematics, and actual experience implementing everything but BS LLMs from scratch), he got a lot right. But he got a few things wrong. As a result, there was a need to correct him (in this post) and ensure the corrections have as much permanence as the original post.

We do recommend you read his original post, but for each tech addressed, integrate the correction below.

RPA – does not “break” when processes change; it breaks when you feed it bad data; when you change processes, it simply loses its usefulness until you change it to match the process — with a good RPA system, that shouldn’t be hard

Machine Learning – requiring clean data is NOT a bad thing; it ensures the algorithm “learns” the patterns you need it to learn to use it effectively

Natural Language Processing – doesn’t struggle with jargon, just context — you define the dictionaries, the grammar, the language — it’s accuracy boils down to that; if you are feeding in documents that use the same words/phrases in multiple contexts, it will always struggle with that to a point

Predictive Analytics – in lay terms, classical predictive analytics is essentially just multi-dimensional curve fitting based on the data available — when something has not been modelled, there is nothing the algorithm can learn to fit against — but to be fair, nothing you’ve listed will succeed with unprecedented events/data

Anomaly Detection – this is based on outlier detection, and well trained outlier detection does NOT have high false positive rates (which are no higher than false negatives), and any “wrong” classifications from a business perspective simply means that the definition of a valid transactions needs to be amended to reduce the “outliers”

Computer Vision – lighting is not as much of a problem as you think as most good algorithmic interpretations will always mathematically adjust the brightness and contrast to a consistent range in pre-processing, and sometimes even greyscale; angles are a problem, because if they can’t be determined, the right transform can’t be applied to appropriately orient the image to maximize identification likelihood

Optimization Algorithms – “requires precise problem definition” is not a bad thing, it’s a good thing — if you don’t have a precise problem definition, you cannot get a precise answer with any technique; one of the best uses is product mix, not just supplier portfolio

LLMs – not “can” hallucinate false info; “will” hallucinate false into — every single time, just a question of the degree; it’s “generative” AI which literally means it makes stuff up, and how accurate what it makes up is with respect to your problem depends on how it was trained, what was asked of it, and how you ask it … very unreliable all around

Pattern Based Recommendations – the whole point is to “filter” to what you would normally buy so that you don’t have to sort through everything, that’s not a problem

Reinforcement Learning – it does not require extensive time, it requires extensive data — computers process mathematical calculations billions of time faster than we do — it’s never time anymore!

Execution Capacity has Always Been the Competitive Advantage in Supply Chain

And The Key is Still Automation, NOT AI!

A recent article over on Global Trade Magazine on Why Execution Capacity Is Becoming the Next Competitive Advantage in Supply Chain gets a number of things right.

1. Procurement and Supply Chain leaders are constantly being asked to do more with less, and, yes, this has been going on for years (and, to be precise, decades).

2. They are managing larger supplier ecosystems, responding to geopolitical disruptions, navigating inflationary pressures, adapting to shifting tariffs, and controlling costs across increasingly complex global operations. At the same time, executive teams expect procurement organizations to move faster, identify new savings opportunities, and strengthen business resilience.

3. The challenge is capacity.

Most enterprises already have capable procurement teams, well-defined sourcing strategies, and clear objectives. What they often lack is the bandwidth to execute those strategies consistently across thousands of suppliers, transactions, and commercial opportunities.

With one supply chain catastrophe after another of the man-made and natural variety (port strikes, border closings, tariffs, wars, strait and canal closings, factory fires, droughts, wild fires, volcanic eruptions, earthquakes, tsunamis, mine collapses, etc.), the constant uncertainty in your supply chain and underlying costs, and supply lines disappearing without warning, execution gaps are becoming increasingly visible, disruptive, and costly.

In order to manage these turbulent times, your organization needs alternate suppliers, alternate supply lanes, the ability to re-allocate orders daily, the ability to re-route shipments in real-time, and the ability to optimize your suppliers, supply lanes, order allocations, and shipment routings. And, most importantly, the ability to identify, and manage, these (alternate) suppliers, supply lanes, re-allocations, and re-routings across all products that must be sourced globally! In other words, execution capability across the Procurement and Supply Chain organizations.

But, as the article notes, in an average organization, only so many new suppliers can be identified, existing supplier relationships can be optimized, products can be strategically sourced, orders can be re-allocated, and shipments re-directed.

This is because, while most organizations have invested heavy in classic analytics, supplier intelligence, sourcing platforms, and risk management tools, they have simply invested in visibility, not execution!

According to the article, the answer is to go from “AI Assistance to AI Execution”. But that’s not the answer. It’s not “AI Assistance to AI Execution”. It’s “Tech Assistance to Tech Execution”, whatever form that tech may be … and for the most part, it’s classic automation, which, we’re sad to say, has existed for over a decade, and been largely ignored for that time.

Let’s take each of these requirements one by one:

Supplier Discovery: when the organization needs to source, or re-source, a product, the tool automatically searches the supplier network for all suppliers that supply a similar product and then weights them on key dimensions of product similarity and organizational supplier scoring criteria for suppliers in that category (based on information on the supplier in the network)

Supplier Optimization: for a product/category, automatically run analyses that identify the right mix of current and potentially new suppliers based upon a combined ability to supply the organization’s demand with enough “slush” to allow for a supplier becoming unavailable due to supplier issues, supply chain issues, or other issues without adding unnecessary bloat to the supply base. (Considering that organizations typically spend 80% or more with 20% of suppliers, most organizations have too many suppliers but not enough for key products or materials.) This mix will be automatically optimized with the right automation solutions.

Order (Re) Allocation: re-run forecasts weekly/daily, re-allocate orders based on stock-levels, probabilistic forecast predictions, current and expected lead-times, expected supplier/lane availability, contractual commitments, etc. and choose a balanced solution that will satisfy all the probable outcomes (using optimization, not random AI predictions)

Real-time Re-routing: for every multi-modal lane, re-routings can happen at every waypoint (where modes shift, cross-docking at warehouses/FTZs is utilized, or where stops occur); re-run the models based on supply chain updates daily and if carriers/routes for segments are expected to become unavailable, costs become too high, or delivery times would stretch out too long (or could be stretched out to lower costs), possibly issue re-routing orders

Required Data: Automation can automatically pull/push data on a daily/real-time basis

When you consider that modern AI falls into Gen-AI which is literally “make stuff up”, you can’t depend on it for critical supply chains where one mistake can be catastrophic. But, fortunately, there are systems out there that do all of the above reliably on classic RPA, optimization, and analytics. (And have been for about a decade.) Plus, with the recent SaaS price compression as a result of the AI Hype wave, it’s all very affordable.

So if you want to succeed, get these systems. They’ll allow you to manage all suppliers, all items, and all lanes. You’ll be able to execute on your strategy, provided you can come up with a strategy that is adaptive enough in today’s global economy.

If You’re Spending 250K Annually Per Engineer On AI …

Then not only are you contributing to planetary destruction (through the generation of between 1.32 tons (high end models, 1 joule per token) and 84 tons (low end models, 2 joules per token) of CO2 to power those data centres, which is about 0.2 to 12.7 times the average individual carbon footprint, with an expectation of 7 to 11 tons (Source), and the utilization of 300,000 gallons to 5,000,000 gallons of water a day to keep those servers cool, or a town’s worth of water every day!

BUT YOU ARE NEEDLESSLY WASTING 400K+ A YEAR

1. Less than 20% of AI generated code survives unscathed in a commercial enterprise software product once senior developers weed out all the security errors, boundary condition errors, and generated code that doesn’t even solve the problem. So, that’s 200K of 250K down the drain as only 20% of output is usable.

2. Having to fix AI generated slop will consume 80% of a good senior developer’s time — a developer you should also be paying 250K a year.

End result, you’ll losing 200K + 200K per developer you force AI coding tools upon!

But hey, it’s your money. If you want to p!ss it away so NVIDEA’s CEO can get richer selling more CPUs we don’t need, that’ up to you!

The linked article contains some metrics, but here are a few others.

  • token prices vary widely, from an average of around 50c/M tokens on the smallest, cheaper models to $75/M tokens (or higher) for higher end “workhorse” models
  • energy processing requirements per token are estimated to be between 1 joule and 2 joules
  • you can buy 14.3 Trillion tokens at the median of around $17.5/M tokens (and 35 times that at the lower end)
  • processing 14.3 T tokens will take about 4000 kwH @ 1 joule/token
  • on an average NA grid, expect to produce 500 to 600 g of Co2 per kWh (since most of our grids are still dirty)