Category Archives: Technology

The Best Way Procurement Chiefs Can Create a Solid Foundation to Capitalize on AI

As per our recent post on how I want to be Gen AI Free, the best way to capitalize on Gen-AI is to avoid it entirety. That being said, the last thing you should avoid is the acquisition of modern technology, including traditional ML-AI that has been tried and tested and proven to work extremely well in the right situation.

That being said, if you ignore the reference to Gen-AI, a recent article on Acceleration Economy on 5 Ways Procurement Chiefs Can Create a Solid Foundation had some good tips on how to go about adopting ML-AI with success.

The five foundations were quite appropriate.

1. Organize

A plan for

  1. exactly where the solution will be deployed,
  2. what use cases it will be deployed for,
  3. how valid use cases will be identified, and
  4. how the solution is expected to perform on them.

There’s no solution, even AI, that can do everything. Even limited to a domain, no AI will work for all situations that may arise. As a result, you need a methodology to identify the valid use cases and the invalid use cases and ensure that only the valid uses cases are processed. You also need to ensure you know the expected ranges of the answers that will be provided. Then you need to implement checks to ensure that no only are only valid situations processed but that only output in an expected range is accepted in any automated process, and if anything is outside the expected norms anywhere, a human with appropriate education and training is brought into the loop.

2. Create a Policy

No technology should be deployed in critical situations without a policy dictating valid, and invalid, use. Moreover, any technology definitely shouldn’t be used by people who aren’t trained in both the job they need to do and proper use of the tool. Even though most AI is not as dangerous as Gen-AI, any AI, if improperly used, can be dangerous. It’s critical to remember that computers cannot think, and only thunk on the data they are given (performing millions of calculations in the time it takes an average person to perform two). As such, the quality of output is limited both to the quality of data input and the knowledge built into the model used. Neither will be complete or perfect, and there will always be external factors not considered, which, even if normally not relevant, could be relevant — and only an educated and experienced human will know that. (Moreover, that human needs to be involved in the policy creation to ensure the technology is only used where, when, and how appropriate.)

3. Understand Your Platform(s) of Choice

Just like there are a plethora of Gen-AI applications, a lot of different vendors offer AI applications, and even if most are similar, not all are created equal. It’s important to understand the similarities and differences between them and select the one that is right for your business. (Consider the algorithms and models used, the extent of human validated training available, typical accuracy / results, and the vendor’s experience in your use case in particular when evaluating an AI solution.)

4. Practice

Introducing new tools requires process changes. Before introducing the tool, make sure you can execute the associated process changes, first by executing training exercises on the different types of output you might get and then, possibly by way of a third party who uses a tool on your behalf, using real inputs and associated outputs. While the AI may automate more of the process, it’s even more critical that you respond appropriately to parts of the process that cannot be automated or where the application throws an exception because the situation is not appropriate to either the use of AI or the use of the AI output. (And if you don’t get any exceptions, question the AI … it’s not likely not working right! And if you get too many exceptions, it’s not the right AI for you.)

5. ALWAYS Ask Yourself: “Does that Make Sense?”

Just like Gen-AI hallucinates, traditional AI, even tried-and-true AI that is highly predictable, will sometimes give wrong results. This will usually happen if bad data slips in, if the use case is on the boundary of expected use cases, or the external situation has changed considerably since the last time the use case arose. Thus, it’s always important to ask yourself if the output makes sense. For tried-and-true AI where the confidence is high, it will make sense the vast majority of the time, but there will still be the occasional exception. Human confirmation is, thus, always required!

With proper use, AI, unlike Gen-AI (which fails regularly and sometimes hallucinates so convincingly that even an expert has a hard time identifying false results), will give great results the majority of the time — so you should seek it out and implement it. Just also implement checks and balances to catch those rare situations it doesn’t and put a human in the loop when that happens. Because traditional use-cases are more constrained, and predictable, it’s a lot easier to identify and implement these checks and balances. So do it … and see great success!

The Public Sector is Giving Procurement Integrity A Bad Name … Can the Private Sector Fix It?

A recent article over on Global Government Forum on Procurement Integrity: A Big Problem That’s Worse Than Most Organizations Think, pointed out that errors, fraud and abuse in procurement cost governments and organizations millions of dollars every year, and even though recent headlines in the US (TriMark, Booz Allen Hamilton), UK (NHS, Royal Mail), and Canada (ArriveCan) are starting to shine the light on the extent of (public sector) procurement fraud, the problem is still bigger than you think. Much bigger.

Current estimates are that organizations, across the public and private sectors, lose 5% per year due to procurement errors, abuse, and fraud. Given that Global GDP is about 85 Trillion dollars, at 5%, that’s 4 TRILLION dollars estimated to be lost annually to errors, abuse, and fraud. And that’s probably a low-ball estimate due to the fact that we just calculated that Over One TRILLION dollars will be wasted on IT software and services due, primarily, to lack of knowledge and/or outright stupidity (and not malicious intent, but if it’s easy for consultancies and third parties to considerably over bill for legitimate goods and services that you need, imagine how much they are fleecing you for goods and services that you don’t need and may not even receive).

It’s highly likely that the true cost of errors, abuse, and fraud (internal, collusion, and external) is closer to 10% of total GDP, or close to EIGHT TRILLION. That’s at least twice the GDP of every country on the planet except China and the United States. That’s a BIG PROBLEM, which is definitely not being helped by the 100M to Multi Billion Procurement Frauds being reported almost monthly across major western economies — and multi-million dollar fines don’t repair the damage. (They don’t even come close.)

This is damage which Procurement needs to repair — because Procurement is the only department that has any hope of putting proper procedures, processes, and platforms in place to minimize the errors; training the organizational employees on proper procedures and monitoring the implementations to prevent abuse; and putting in place proper detection systems to detect, and prevent, potential fraud and quickly identify and track it when it happens.

Unless all the bucks go through, and stop at, a modern Procurement department run by a CPO who puts in place proper people, processes, and platforms, loss is going to continue to run rampant. Which means that while the public sector is failing us daily, the Private sector has to step up and restore the integrity of Procurement. It can start by utilizing some of the the techniques in the linked article, and continue by continually learning and implementing the best technology and processes it finds to not only uncover significant savings in inflationary times, but return integrity and trust into big business, and give governments who have lost their way a model to follow.

And for more details on Bad Buying to avoid, and how to achieve Procurement with Purpose, the doctor suggests you start by following the great public procurement defender, Peter Smith.

Strategic Sourcing & Procurement for Technology Cost Optimization

Given that we recently published a piece noting that Roughly Half a Trillion Dollars Will Be Wasted on SaaS Spend This Year and up to One Trillion Dollars on IT Services, it’s obvious that one has to be very careful with technology acquisition as it is very easy to overspend on the license and the implementation for something that doesn’t even solve your problem.

As a result, you need to be very strategic about it. While you certainly can’t put the majority of your technology acquisitions (which can be 6, 7, and even 8 figures) up for auction (as products are never truly apples to apples to apples), you definitely have to be strategic about it. As a result, you should be doing multi-round RFPs and then awarding to the vendor who brings you the best overall value for the term you want to commit to, once all things are considered.

But these have to be well thought out … you need to make sure that you are only inviting providers that are likely to meet 100% of your must haves, 80% of your should haves, and 60% of your nice to haves (and, moreover, that you have really separated out absolute vs highly desired vs wanted but not needed because the more you insist on, especially when it’s not necessary, the shallower the vendor pool, and the more you are going to end up paying*).

To do this, as the article notes, you have to know what processes you need to support, what improvements you are expecting, what measurements you need the platform to take, and what business objectives it needs to support. Then you need to align your go-to-market sourcing/procurement strategy with those objectives and make sure the RFP covers all the core requirements (without asking 100 unnecessary questions about features you’ll never actually use in practice).

You also need to know what quantifiable benefits the platform should deliver, both in terms in tactical work(force) reduction (as the tech you acquire should be good at thunking), and the value that will be obtained from the strategic enablement (in terms of analysis, intelligence gathering, guided events, etc.) the platform should deliver. If it is a P2P platform, how much invoice processing is it going to automate, and, based on that, how much is it going to reduce your average invoice processing cost? If it’s a sourcing platform, how much more spend will you be able to source (without increasing person-power) and what is a reasonable savings percentage to expect on that? Understand the value before you go to market.

Then you need to understand how much support and help you need from the vendor. If you just want a platform that does a function, then you just need to know the vendor can support the platform in supporting that function. But if you need help in process transformation or optimization, customized development or third party tool integration for advanced/custom processes, etc. you need a vendor that cannot only provide services, but also be a strategic provider for you as well.

And so on. For more insights, we suggest you check out a recent article by Alix Partners on Strategic Sourcing and Procurement for Technology Cost Optimisation. It has a lot of great advice for those starting their strategic procurement technology journey.

*Just remember, if you’re a mid-market, and you’re flexible (i.e. define what a module needs to accomplish for you vs. a highly specific process) you can get your absolute functionality and most of your desired functionality for 120K in annual SaaS license fees, excluding data feeds and services. If you’re not flexible, or not really strict in really separating out absolute vs strongly desired vs nice-to-have, you can easily be paying four times that.

Also remember, if you’re enterprise, your absolutes and strongly desired are much more extensive, typically require a lot more advanced tech (like optimization, predictive analytics, ML/AI, etc.), and licenses fees alone will cost you in the 500K to 1M range annually at a minimum, not counting the 100K to 1M you will need to spend on the implementation, data cleansing and enrichment, integration, training, and real-time data feed access, so it is absolutely vital you get it right!

Finally! A “Think Tank” Article that Gets It Right!

the doctor has been reading a lot of “think tank” and “thought leader” articles lately that are completely off the mark. Some are so bad that he’s wondering if the publications are paying interns who know nothing about the space to use “chat j’ai pété” (Chat-GPT) to hallucinate content for them. (And, as you’ve seen, some are so bad and/or make him so angry that he just has to rant about them. Our space don’t get no regard at all as it is. The last thing we should be doing is providing anyone who takes the time to read about it with misleading or wrong information).

All that being said, Supply Chain Brain recently published an article on 2024 Predictions: A New Era of Strategic Supply Chain Design by Donald Hicks, the CEO of Optilogic. In it, he makes six predictions for the new era of strategic supply chain design in 2024.

The first five predictions were good.

1. A shift from short-term to strategic thinking.

COVID demonstrated that we’ve reached the end of short-term JIT thinking, and the recent geopolitical turmoil since has only heightened that reality. Any company that wants to survive has to go back to focus on mid-to-long term strategic thinking that will help it mitigate the plethora of risks it is being hit with and assure supply.

2. An end of the age of unlimited cheap suppliers.

Especially since the majority of these were based in China. As the author notes, China-US relations are deteriorating fast and the Chinese economy is underperforming. Moreover, as a result of COVID, logistics are uncertain and considerably more expensive from China (due to less carrier space, as many ships were scrapped during COVID for insurance settlements, and the need to sail around the capes, due to the Red Sea situation and the prolonged Panamanian drought). So, companies need to start looking elsewhere, and since they let their best suppliers in Mexico and South America wither and die, there aren’t many good options at the moment.

3. Demand for vendor transparency.

In addition to customers becoming more discerning, as the author notes, there are more supply chain regulations that need to be adhered to globally, more sustainability regulations, more denied party regulations, and so on. Companies need to know who they’re dealing with; that all supply chain, sustainability, and regulatory requirements are met; and that any desires of its customers can be met.

4. Market turmoil and the rise of new leaders.

This year is projected to witness down rounds, market turmoil, and a reassessment of strategies. Most definitely. VC went too hot and heavy before COVID trying to force unicorns where the foals weren’t even breeding stock, and then lost heavy in the SVB failure; and PE, trying to get a piece of the payments, online collaboration, and/or FinTech market during COVID paid ridiculous multiples for rather basic offerings that weren’t even complete — and that would never demand the price tag the investors expected. As a result, these PE firms are now looking at payback timeframes of a decade or more, if they’re lucky. This means that cash is sparse, investments will be sparser, and some companies (that overspent and can’t get the valuation) will not survive.

5. Digital Twin Skepticism.

Every supply chain technology vendor is clamouring to tell you about their digital twin capability, but the term “digital twin” is a marketing creation that can’t live up to its ambitious name. Companies don’t always have all the data (or quality data) relating to supplier orders and timelines, inventory levels and factory production in separate operational systems, much less a single location.

There’s no digital twin without complete data, and there’s no complete data. Modern manufacturing companies and direct buyers are figuring this out and not falling for outlandish claims anymore.

The sixth prediction was absolutely fantastic!

6. Artificial intelligence exhaustion, and a return to old-school evaluation.

Hear, hear! Smart companies are getting fed up of the ridiculous claims made by new Open/Gen-AI companies and the paltry results that were delivered, if any. They’re also fed up of the high-price tags relative to the limited value they’re received from “AI” so far.

Thus, rather than relying on the mere claim of being AI-enabled, companies should be expected to showcase their capabilities, substantiate their claims with proof, and provide clear reasons for belief, signalling the return to a more traditional approach to purchasing decisions.

Hear, hear!

Another “think tank” article on digitizing procurement that’s off-the-mark!

A recent article in Supply Chain Brain noted that you should be seizing the opportunity for digitizing procurement and the doctor completely agrees. Nothing should be paper based in Procurement today. There’s no excuse for it.

And yes, multiple developments in supply chain are converging to create an unprecedented digital opportunity for procurement professionals. Furthermore, if you work on mastering and combining emerging and maturing technologies in strategic ways since procurement teams are in a position to reshape how they work, and create value across the supply chain, you can revolutionize Procurement and business performance.

But digitizing, by definition, means moving processes from scrolls to systems, from the dark basement to the illuminated screens. It DOES NOT mean that:

  • you use Gen-AI or even machine learning
    there may be tasks where you apply point-based ML, but that comes after the digitization of an appropriate process
  • you use cognification to illuminate (concealed) processes
    especially when it could illuminate you should never have digitized the process in the first place
  • you accelerate workflow through automation
    you automate what you can, and while that includes the acceleration of tactical paperwork processing and thunking, sometimes humans have to step back and think about the data received, insights produced, and options available before making a decision … you don’t accelerate whatever amount of time it takes a human to make a good decision (and, instead, focus on automating and accelerating any non-strategic tactical “thunking” tasks that prevent them from focussing their brain power where it’s really needed)
  • you go straight to content personalization
    when the users might not even know how to use the baseline systems (and, in the process, create a nightmare for the support personnel)

Digitizing Procurement starts by:

  • understanding what processes you are using now
  • understanding if they are appropriate or they should be optimized
  • identifying off-the-shelf best-of-breed modules, mini-suites, suites, and/or
    intake-to-orchestrate platforms and implementing them
  • identifying key points where RPA, ML, or other advanced techs can make the process even more efficient
  • then identifying the right advanced tech to use

Not starting with it. You should never try to run a race before you can walk. The only “impactful opportunity” identified in the article you should start with is

  • adopting ecosystem thinking to enhance data

At the end of the day, nothing works well without good data. So get the data right, and everyone aligned to get the data right, and that will get you further, and help you do better, than any piece of modern tech you can try to throw at the problem.