Why Your ProcureTech Initiative Will Fail … Part 2

We’ve written many series on best practice tech identification, tech selection, and tech implementation in the hopes of inverting the odds from an 80%+ chance of failure to an 80%+ chance of success, but given that failures are literally still happening on a daily basis, it seems that most people can’t be bothered to read best practice advice so today we’re going to flip the script and tell you all the reasons you’re going to fail and then you hope you go back and read the best practice advice we’ve freely given you (in series such as Successful Vendor Selection – The Series).

5. You Don’t Understand How To Select Proper Systems

As per our previous instalment, the answer is never to select the systems recommended by your favourite Big X consultancy whose recommendations are always in the mutual back-scratching club. With all the finders fees and recommendation fees built into the relationships, you’re paying multiples of what you should be for the tech even if the tech is appropriate. Typically the tech in the big vendors isn’t the best or most modern tech, and often the tech you fall back on only if you’re such a large enterprise that smaller vendors can’t serve you.

It’s not vendor size, Big X recommendation, marketing, or hype. And it’s never (Gen-) AI unless there’s no other solution. AI accelerates what you give it. And when you give it bad process, bad data, and bad instructions, you get an even worse result than what you have now. Until you’re best in class with last generation tech, you’re not even ready to consider AI.

The answer, as we’ve said before, is to find an independent consultant or consultant from a niche consultancy with no vendor relationships and no implementation team. The consultant/consultancies only line of business must be advisory, solution advisory, and project assurance — not implementation and integration and definitely not vendor partnerships. While the consultancies with the partnerships will tout the benefits and how they get (their customers) top tier support, first response, and the newest releases; that’s hype, not results. Results come from the systems that are right for you, not the systems right for the Big X consultancy.

Unless, of course, you are skilled enough to implement the best practice selection process yourself.

6. You Don’t Understand How Long It Will Take To Implement New Systems

A technology system implementation, replacement, or upgrade is an organizational mega-project in a mid-sized or larger organization. Even though SaaS vendors can partition a new instance in minutes, that’s not implementation, integration into your systems, incorporation into your daily processes, or institutionalization into your team’s routine. That all takes time. Lots of time — and a lot more time than your vendors will tell you. They know that you’ll be drawn to the proposal with the shortest implementation time so they will lie about how long it will really take and base their proposal on the theoretical minimum if everyone worked 12 hours a day, 7 days a week, and nothing ever, ever went wrong.

But we know the truth. Everything that can go wrong, will, to some degree. No one can dedicate as much time as the project plan says. All of the integrations will take longer than estimated. The data will be 10 times worse and 10 times as incomplete as was assumed, so there will be delays to clean the data and integrate external sources to buff it up. So things will drag out well beyond the proposal. Typically multiples.

That’s because no one will admit it’s a mega-project, and that’s why the technology project failure rate keeps increasing year-over-year despite being at an all time high of 88%+ in general, and 94% if Gen-AI. The reality is that mega-project success rates are 0.5%, as chronicled by Bent Flyvbjerg & Dan Gardner in How Big Things Get Done, where a study of over 16,000 projects across 136 countries going back to 1910 revealed the success rate. This is where technology project success rates are headed until everyone wakes up to the reality that they are dealing with mega projects and all AI does is speed up the failure.

7. You Don’t Understand How To Do Change Management

It’s not just implement the software, integrate the data feeds, flip the switch, and go. That’s a surefire guarantee for system avoidance and bypass.

Change management involves understanding how the processes are changing, what training the users will need, how best to go about it, how fast the switchover can actually happen, planning for, and managing all the non-technology details and implementing proper project assurance to make sure it actually happens. That last part is key — project assurance that starts before the first step of system implementation and continues until the adoption and usage hits the required targets — which will typically be months, if not years, after initial system implementation depending on the system. A few months for a dedicated solution or small suite, to a few years for an organization wide ERP or SCP for a large multi-national organization in dozens of countries.

8. You Don’t Know How To Recognize and Correct Issues To Avoid System Bypass

Just like no implementation will go according to plan (which is why over-optimistic schedules will never happen), no design will be as perfect as believed. Something critical will always be missed and multiple significant issues will crop up over time. The ability to recognize these issues quickly and do something about them before your employees start bypassing the system — because once they start, they won’t stop — the harder it will be to get them back on the system.

Let’s say you buy a new e-Procurement system designed to curb P-Card tail spend and allow the vast majority of spend to follow procedure, be tracked against budgets, and properly managed over time. Sounds great in theory, but lets say that any purchase not in the catalog requires an RFQ with at least 3 vendors, or a 3-way price comparison between 3 online vendors with a manager’s sign-off, which adds time and hassle for purchases that used to be one and done because the amounts were under thresholds, all of the options were always within a few % of each other, and saving $2 on a $100 purchase is NOT worth 15 minutes of an employee’s time who’s fully burdened cost is $100 an hour.

If you don’t pick up on this quickly, and let them one-and-done purchases under the old P-card amount through easy-peasy punch-out, they’ll go back to P-carding everything and if you take the P-card away, they’ll go back to the personal credit card and monthly expense report. You need to quickly figure out who is bypassing, why, and how you make the system easier to use than bypass.

Why Your ProcureTech Initiative Will Fail … Part 1

We’ve written many series on best practice tech identification, tech selection, and tech implementation in the hopes of inverting the odds from an 80%+ chance of failure to an 80%+ chance of success, but given that failures are literally still happening on a daily basis, it seems that most people can’t be bothered to read best practice advice so today we’re going to flip the script and tell you all the reasons you’re going to fail and then you hope you go back and read the best practice advice we’ve freely given you (in series such as Successful Vendor Selection – The Series).

1. You Don’t Understand Your True Needs

You’ve never done a full end-to-end process analysis on your organization, you don’t understand how inefficient your processes are, what processes you actually need, why you need them, and how much better you could be doing. You just know that the KPI metrics you are tracking are not on par with industry averages based on what your overpriced consultants are telling you, that your balance sheet isn’t as good as best in class, and that you need to do something … and that something is get a shiny new tech toy that the overpriced consultants will help you select by telling you who to invite to your RFP. (And you should know all the problems with this — they’ll only recommend the partners they have sycophant partnerships with, get referral and implementation fees from, and who will ensure that they remain your overpriced consultancy of choice.)

2. You Don’t Understand What You Already Have

Once you understand what the correct processes are, why, and where the automation points are, you need to revisit the systems you have to see where they can solve the problems. Chances are you have a number of suites, supply chain platforms, and ERPs with easy to implement plug-in modules that solve a lot of the problems you have without buying any new systems. And even if new systems might do it better, chances are the improvement won’t be worth the extra money, downtime, and change management — which will all cost you dearly. The reality is that if you can get an 80% solution today, with tools your people are already using, that’s much better than a potential 95% years in the future.

3. You Don’t Understand What Your Capabilities Actually Are

By this we don’t mean your process capabilities or technological capabilities, we mean your actual functional capabilities. Your domain knowledge, your ability to execute on that domain knowledge, and your natural efficiency. It’s pointless improving processes to apply more advanced techniques or employing modern technology to speed up processes when you’re not capable of managing those advanced processes or technology. If you employ processes and systems you’re not ready for, they won’t deliver any results while costing you millions of dollars in the system selection and implementation processes.

4. You Don’t Understand How Long It Will Take to Upgrade Your Capabilities

Even if you figure out you need to upgrade your skills and those of your team’s, even if you posses a fair degree of human intelligence, you don’t know how long it will take. It’s not just buying a knowledge dump from a consultancy or giving your team a 5-day crash course, because knowledge that is not applied is not retained. There’s a reason College and University courses give assignments and projects as well as exams — the more you apply, the more you retain. If the imparted knowledge is not applied, it will not be retained. Until your team can start applying, repetitively, the new knowledge in improved processes, they won’t retain it and they won’t advance. The best training will be a day or two a month over months, not a week. And that’s for stage 1. It will take years to get your team from average to mastery. We’ve known for decades that major transformation projects take 5 to 10 years, and that the average journey to best in class for the committed is 8 years. Technology doesn’t change that. The longer you choose to ignore this fact, the longer you will fail.

To be continued in Part 2.

The Procurement Knowledge Devolution

The internet was supposed to kick off the procurement knowledge revolution, allowing Procurement pros to quickly:

  • find out about best practices
  • get commodity and market pricing for products and services
  • build reasonable (should) cost models
  • find out about new suppliers, carriers, and consulting partners
  • research new products and services
  • hold truly global sourcing and procurement events in real time
  • effectively communicate with, manage, and develop suppliers
  • etc.

For twenty years, that’s what it was. Procurement departments who learned how to use the internet properly for research, deployed the right SaaS to support their processes, and identified the right data for their processes were successful in their endeavours.

But then came Gen-AI LLMs, chatbots were replaced with chat, j’ai pété‘s and clod‘s, and knowledge was replaced with whatever content the LLM generated. Maybe it was correct, maybe it was mostly correct, and maybe it was a 100% fabrication — a hallucination if you please. The problem with LLMs is that they are NOT intelligent. They are essentially super sophisticated multi-level cross-connected deep neural nets that go beyond classification to generation of responses built up from sub-responses built up from deep training on incredibly large data sets.

Therein lies all of the problems. It generates built on random probabilities. Those are dependent on what’s in the training data set, what questions were asked, what results are reinforced, and how it’s used. If the training data is bad and full of bad data and falsehoods, the chances of incorrect, and even dangerous, responses being generated are quite high. If the training was biased, the output is likely to be very biased. And if it’s not “trained to please”, the models are fundamentally designed to “learn to please”, so if computations that are a complete lie will increase utilization of, and faith in the model, that’s what will happen.

Moreover, every vendor is now believing the hype from the big LLM players, treating the technology as real Artificial Intelligence (when it should be called Artificial Idiocy), and trying to plug it in everywhere … promising that it will provide their clients with true natural language interfaces, agentic tech, and even BS AI Employees. Those who are adopting it are literally getting dumber by the day. Not only has the cognitive impairment, atrophy, and potential long-term decline from regular use been well documented, but the failure rate has been well documented as well with MIT and McKinsey studies demonstrating success rates of 5% and 6% successfully. Most pilots are being abandoned, sometimes before they even begin, because the tech isn’t even good enough to put into employee’s hands for the tasks the overpriced Big X consultancies claimed the LLMs would be perfect for.

Furthermore, even when organizations are smart enough to ignore Gen-AI, over-automating using the most advanced last-gen (A)RPA and AI technologies will still give Procurement teams a false sense of security and, due to their very low error rate, as time goes on, the team’s skills will go rusty and their ability to deal with true exceptions quickly disappear, especially as the old Pros (get forced to early) retire and the younglings have never dealt with exceptional situations.

The age of AI hype has ushered in a knowledge devolution faster than any age that has come before.

I hope the profession can survive it!

Cost Reduction … it Starts With Cost Increase

It used to be cost reduction, which was focussed on cost cutting, started with the one-trick pony of cost cutting by any means necessary, which typically took the form of e-Auctions, RFPs to new suppliers, and GPOs that could aggregate and leverage huge volumes — all tricks that are rearing their ugly heads again with the rapidly rising costs thanks to inflation, tariffs, and global instability.

They all work just fine in the short term, but they all come back to bite you in the backside in the long term. Here’s why:

  • e-Auctions: find savings by squeezing margins, and you can only take those out once, and once inflation comes back, costs go up
  • RFPs: designed just to find the absolute lowest price attracts suppliers who cut corners, underpay their staff, and offer no service while alienating your current, more trustworthy, suppliers
  • GPOs: can aggregate volumes and lower prices, but then you are dependent on them, and paying their markup … forever

None of these is the long term answer.

When we first started discussing cost reduction two decades ago, the key methods we focussed on were:

  • strategic supplier relationships and customer of choice: so that they put the effort into being your supplier of choice and finding their own ways to keep costs down (streamlined operations, better raw material sourcing, etc.)
  • supplier investment and development: if the supplier is smaller, or not as advanced, they’ll only do so much on their own, so your efforts to invest, improve, and guide them (through early payments, low-cost new line financing, etc.) could greatly lower your costs over a multi-year engagement
  • strategic sourcing decision optimization: where you did a multi-objective optimization that took all of the cost factors (unit, transportation, warranty, service, waste etc.) into account as well as risk (that could cause “savings” to evaporate over night) and quantitative assessments of other key factors

And those are all good techniques in (semi) normal times. But these are not (semi) normal times. These are almost unprecedented times. Between natural disasters, geo-political conflicts and wars, and terrorism, we are dealing with unprecedented simultaneous reductions and closures of major maritime shipping lanes (the Panama Canal, the Red Sea, the Strait of Hormuz), unstable (and rapidly escalating) fuel costs, regular supplier and carrier failures, unpredictable crop and raw material availability, etc. all at the same time. Old friends becoming foes, or at least frenemies; friend-shoring, near-shoring, and home-shoring finally gaining ground (despite being promoted and the right answer for decades); and supply chains being swapped whenever possible.

We’re in times where these techniques, while still good, can’t always address all of the situations. Plus, if you’re constantly adapting to what’s available now, versus focussing on what you should be building, you’ll be in a constant, unstable, state of affairs, caught off guard with every flux, and constantly on the brink of ruin.

You need to stop working sourcing event to sourcing event, procurement to procurement, and disruption to disruption and start working on transforming your supply chain to a more resilient long term supply chain. This will require identifying which safe countries and regions (likely to have long term geo-political and trade stability with your home and/or destination countries) you should be doing business with, where solid supply bases could be, and how you could construct a real supply chain from the source countries to the destination countries that don’t depend on unstable source points.

Then you have to engage the carriers, find partners to help you manage the export and import requirements and take advantage of FTZs (free trade zones), build or acquire intermediate warehouses and cross-docks, and be ready for trade with the local suppliers. Those will typically include multiple suppliers you are not currently working with, and they may need to upgrade their production lines, operations, services, etc. to serve you to your level of expectation. This will incur costs that your suppliers and partners will need to incur, which will need to be passed onto you. Which means, in the short-to-mid-term, your costs will increase. But if you design the right, stable, supply chain networks that you can use for years (or decades), develop the right suppliers, and maintain volumes, as operations improve, up-front costs get amortized, and economies of scale get optimized, costs will go down, and with long-term agreements, over multiple years, your company will see previously unrealized savings while your peers see their costs go through the roof.

So if you want to save money, you better be prepared to spend.

We Know You Should Have A Lot Of Global Trade Data …

As per our last post, if you want to design and run a relatively smooth running supply chain, you need a lot of data. As per our post, at a minimum:

  • product compositions
  • supplier locations
  • route details
  • alternate products, suppliers, and routes
  • sanctions
  • denied parties
  • tariffs, export and import
  • taxes and recoverables
  • carbon/GHG
  • commodity market and product cost data
  • currency conversions and trends
  • natural disaster risks
  • man-made disaster data
  • consumer and market sentiment data

Which leaves you with two major problems.

  1. where do you get it?
  2. how do you manage it?

There are two choices on where to get it:

  1. data consolidators and brokers
  2. government / public sector sources

As much as possible, you want to rely on option b), because, in this AI-HYPE filled world, data is now the most valuable commodity, the data brokers know it, and even when they are getting it from a government / public source (for free) and then processing it for your consumption, they are charging a premium for it. A subscription to even a fraction of the above data could cost you more than the annual SaaS subscriptions you are feeding it into (not counting your AI token costs which are going to continue to increase without bound if you are unnecessarily relying on AI for tasks you do not, and should not, be using Gen-AI LLMs for).

So you want to use the cheap/free government and public sources as much as possible. While this sounds easy enough, every single source will be in a different format, with different access requirements, different update frequencies, and different levels of completeness. We’re talking about everything from Excel files to real-time json requests, with multiple types of authorizations, access protocols, and transport protocols.

This bring us to the second issue — how do you manage it?

You basically need a DIY data orchestration platform. And the reality is, the majority of today’s orchestration providers, despite their grand claims, don’t really do that! Out-of-the-box, you can only integrate solutions they have already integrated with, and only import any data they have previously mapped. Plus, they are limited in intake to what they have designed for. Most of them aren’t even as powerful as last generation data mapping frameworks that allowed a data analyst to integrate all of the data from various sources into one common, central, database. (Now, it typically involved creating yet another data warehouse / lake / lakehouse.)

What you need is a next-gen supply chain orchestration platform that was built from the ground up to allow you to plug, play, and orchestrate data sources as well as workflows and applications using pre-defined mappings, Web 3.0 Markup, standard terminology, and statistical AI (with known confidence) that will auto-map as much as possible, minimize what can’t be auto-mapped, and generate additional data objects and tables for storage where your systems aren’t already handling that type of data.

A next generation system that also incorporates auto-mapping, auto-schema-extension, and auto-data-orchestration alongside workflow construction and third party system integration using APIs, MCP, and other integration technologies along with all of the common authorization protocols. A fully dynamic data platform that can serve as the core of a next generation SCP platform. One that is a level beyond the majority of platforms calling them orchestration platforms today,

And, finally, one that understands how to organize, federate, and classify data for true analysis.

But that’s another post!