Category Archives: Technology

4 Smart Technologies Modernizing Sourcing Strategy — Not Just Doctor Approved!

IBM recently published a great article on 4 smart technologies modernizing sourcing strategies that was great for two reasons. One, they are all technologies that will greatly improve your sourcing. We’ll explain why.

Automation

Business Process Automation (BPA, or RPA — Robotic Process Automation) can optimize sourcing workflows as well as procurement workflows. With good categorization, demand forecasting, inventory management, price intelligence, templates, strategies, situational analysis (that qualitatively and quantitatively define when a strategy should be applied), and workflow, you can automate sourcing just as much as you can automate Procurement. You can eliminate all of the tactical and focus solely on the strategic analysis and decision making.

Blockchain

If you need to record information in a manner that can be publicly accessed and verified, such as to ensure that records for traceability can be independently verified, or to publicly record ownership, blockchain is a great technology as its ultra secure. In Sourcing and Procurement, it can be used to track orders, payments, accounts, and more across global supply chains and multiple private and public parties.

Analytics

In addition to providing an organization with deep insights into their spend and (process level) performance, analytics engines and their “big data brains” provide real-time sourcing flexibility and visibility to enhance order management, inventory management, and logistics management. With proper intelligence, sourcing teams can understand and act on changes in the increasingly complex supply chain — as they happen.

AI

When deep data and analytics are paired with AI, the deep insights can improve forecasts, help identify risk, and provide suggestions for management.

And this brings us to the next great aspect of the article. Not once did it mention Gen-AI. Not once. As the doctor has been stating over and over, the classic analytics, optimization and machine learning you have been ignoring for almost two decades will do wonders for your supply chain. (Blockchain is not always necessary, but will help in the right situation.)

SaaS Procurement for S2P+ Goes Beyond Basic Buying Etiquette for IT Procurement

Medium recently posted an article from ArmourZero, a cyber-security platform provider*, on IT Procurement Etiquette for User and Vendor, which I guess goes to show the lack of knowledge on how to buy among some organizations. It doesn’t go nearly far enough on what S2P buyers need to know, but it does provide basics we can build on.

The advice it provides for a user are:

  1. Do Your Homework (Create a Proper SoW): take the time to provide a proper Scope of Work (and don’t just take a vendor’s sample SoW, edit it slightly, and send it out, especially to the vendor you took it from)
  2. Professional: be neutral and don’t favour any specific vendor
  3. Transparent: be clear about the process, and if all bids exceed the budget and a reduced bid is required, be clear about the reason for going back and any modifications to the SoW to allow vendors to be within a budget range
  4. Fair: stick to the rules; not even incumbents get to submit late; if you have a minimum number of bids in by the deadline, you work with those; you weight on the same scales; etc.
  5. No Personal Interest: don’t accept gifts; don’t vote on the bid where you have a relationship; etc.

However, in our space, you have to start with:

  • Do Your Tech Market Research: make sure you understand the different types of solutions in the market, what the baselines are, and what the standard terminology is (sourcing != procurement)
  • Do Your Deep Dive Tech Market Research: once you figure out the major area, figure out the right sub area — a Strategic Sourcing Solution is not a Strategic Sourcing Solution is not a Strategic Sourcing Solution; a CLM (Contract Lifecycle Management) is not a CLM is not a CLM; and an SXM is definitely not an SXM which is definitely not an SXM; in the case of Strategic Sourcing, do you mean RFX? e-Auction? or optimization-backed sourcing? in the case of CLM, do you mean Negotiation, Analysis, or Governance? in the third case, which element(s) of the CORNED QUIP mash are you looking for: compliance? orchestration? relationship? network? enablement? discovery? quality? uncertainty? information? performance? No vendor does more than half of these, and those vendors will only do a couple of areas really deep and more-or-less fake the rest!
  • Write a Process and Results Oriented RFP (& SoW): it’s not features or functions (beyond the foundational functions all applications in the class need to support) it’s the processes you need to support, the systems you need to integrate with, and the results you need to get — let the vendors describe how they will solve them, not just check meaningless yes/no boxes … they might have a more efficient way to support your process, a faster way to get results, etc.; the same goes for any implementations, integrations, services, etc. — make sure it focusses on what you need to accomplish, not meaningless check-the-box exercises
  • Do Your Due Diligence Vendor Research: once you have figured out the solutions you need and the primary capabilities you are looking for, make sure the vendors you invite not only offer the type of solution, but have (most of) the foundations of the capabilities you are looking for; use analyst firms, maps, tech matches, and expert analyst consultants to build your short-list of mandarin to tangerine to orange vendors vs random google searches that, if you are lucky, will give you apples to oranges, and if you are not, will give you rutabagas to oranges to tofu vendor matches

Then apply the rest of the advice in the linked article by ArmourZero.

You’ll have better success in your RFP, negotiations, and your implementation if you do all of your homework first, even though it is a lot more extensive than you want it to be. (But remember, there are expert analyst consultants who can help you. No one says you can’t hire an expert tutor! And the reality is that you should spend five figures before making a six to seven figure investment (as there will be implementation, integration, and support costs on top of that six-plus license fee), and maybe even do a six-figure deep dive process and technical maturity assessment, market scan, and custom RFP/SoW generation project with an expert analyst consultant before signing a recurring [high] seven figure suite deal.

* A CyberSecurity firm is the last vendor you’d expect to be authoring such a post (given the massive increase in CyberAttacks since 2019), but I guess it shows just how bad buying can be if they felt the need to write on this vs. a SaaS Management Vendor

Will AI Make Us Irrelevant?

Short Answer: No. But Improper Use Will Make Us “Redundant.

James Meads asks “Will AI in Procurement make us all irrelevant?”

So I will answer. No, it won’t! But it will make those companies who dive off the deep-end on Gen-AI irrelevant as their supply chains crumble with no real human intelligence there to save them when the next crisis hits. (See the myriad of rants here on Sourcing Innovation on just how over-hyped Open Gen-AI technology is and what you actually need to solve your problems.) Also, if we’re lucky, they will take a few providers with no actual platform capability (or Procurement value) down with them. (We need them to get out of the way for those platforms that have been offering real, deterministic, math-based, tried-and-true analytics, optimization, and machine learning solutions [for up to two decades] as there are many companies that need those solutions today.)

While custom-trained closed LLMs can seemingly do a lot of the work for us, they are NOT intelligent, they don’t know good from bad, they don’t know right from wrong, and they definitely don’t know critical from irrelevant. Thus, even though they can put together an NDA or RFP in seconds, it doesn’t mean it’s “fully functional”, that it protects you from all the risks, or that it captures all your requirements. Only an expert human can verify that. [And it doesn’t matter how good your “prompting” is. It can still fail, with a reasonably high probability to boot! (Which is what you can give it!) There’s a reason that Tonkean, an intake automation/enterprise orchestration solution provider, ALWAYS does pre-validation on inputs and-post validation on outputs before showing you anything when it incorporates your LLM technology, because they know just how often it fails and if the response doesn’t closely resemble something expected with very high probability, they won’t even show it to you.]

“AI”, or, more accurately, rules-based automation, will replace humans who are just doing tactical data processing, but it cannot replace humans who can do real strategic analysis, interpretation, and problem solving. Unfortunately for Procurement, given that 80%+ of the time is tactical data processing and fire-fighting, this will cause companies to think they can eliminate 80% of the Procurement team, even though the reality is that the Procurement team isn’t even addressing 20% of spend strategically in any given year, meaning that they should be augmenting the Procurement team with every useful technology they can find to try and get that spend coverage above 80%!

And if you want to know what companies are truly offering valuable “AI” (where the best you will get is Augmented Intelligence, level 2 on the 4 tier scale, as there is no such thing as Artificial Intelligence and many companies still don’t even offer Assisted Intelligence, level 1, and instead disguise their Artificial Idiocy in slick marketing), talk to an analyst who CAN do the math AND the programming.

First published on LinkedIn.

Even Forbes is Falling for the the Gen-AI Garbage!

This recent article in Forbes on the Supply Chain Shift to Intelligent Technology is what inspired last week’s and this week’s rant because, while supply chains should be shifting to intelligent technology, the situations in which that is Gen-AI are still extremely rare (to the point that a blue moon is much more common). But what really got the doctor‘s goat is the ridiculous claims as to what Gen-AI can do. Claims with are simultaneously maddening and saddening because, if they just left out Gen-AI, then everything they claimed is not only doable, but doable with fantastic results.

Of the first three claims, Gen-AI can only be used to solve one — and only partially.

Procurement and Regulatory Compliance
This is one example where a Closed Private Gen-AI LLM is half the battle — it can process, summarize, and highlight key areas of hundred page texts faster and better than prior NLP tech. But it can’t tell you if your current contracts, processes, efforts, or plans will meet the requirements. Not even close. In fact, no AI can — the best AI can just indicate the presence or absence of data, processes, or tech that are most likely to be relevant and then an intelligent human needs to make the decision, possibly only after obtaining appropriate expert Legal advice.
Manufacturing Efficiency
streamline production workflows? optimize processes? reduce errors? No, Hell No, and even the Joker wouldn’t make that joke! You want streamlining? You first have to do a deep process cycle time analysis, compare it to whatever benchmarks you can get, identify the inefficiencies, identify potential processes and tech for improvement, and implement them. Optimize processes? Detailed step by step analysis, identification of opportunities, expert process redesign, training, implementation, and monitoring. Reduce errors? No! People and tech do the processes, not Gen-AI — implement better monitoring, rules, and safeguards.
Virtual Supply Collaboration
A super-charged chatbot on steroids is NOT a virtual assistant. Now, properly sandwiched between classical AI and rules-based intelligence it can deal with 80% of routine inquiries, but not on its own, and it’s arguable if it’s even worth it when a well designed app can get the user to the info they need 10 times faster with just a couple of clicks. Supply chain communicating? People HATE getting a “robot” on a support line as much as you do, to the point some of us start screaming profanities at it if we don’t get a real operator within 10 seconds. Based on this, do you really think your supplier wants to talk to a dumb bot that has NO authority to make a decision (or, at least, should NEVER have the authority — though the doctor is sure someone’s going to be dumb enough to give the bot the authority … let’s just hope they can live with the inevitable consequences)?

And maybe if the article had stopped there the doctor would let it pass, but
first of all, it went on to state the following for “AI”, without clarifying that Gen-AI doesn’t fit in the process, leading us to conclude that, since the first part of the article is about Gen-AI, this part is too, and thus is totally wrong when it claims that:

“AI” understands dirty data
with about 70% accuracy where it counts IF you’re lucky; that’s about how accurate it is at identifying a supplier from your ERP/AP transaction records; an admin assistant will get about 98% accuracy by comparison
it can “confirm” inventories
all it can do is regurgitate what’s in the inventory system — that’s not confirmation!
it can identify duplicate materials
first it has to identify two records that are actually duplicates;
and how likely do you think this is with a supplier mapping accuracy of 70%?
it can identify materials to be shared among facilities
well, okay, it can identify materials that are used across facilities and could be located in a central location — but how useful is that? it’s not because, first of all, YOU ALREADY KNOW THIS, and, second, IT CAN’T DO SUPPLY CHAIN OPTIMIZATION — THAT’S WHAT A SUPPLY CHAIN OPTIMIZATION SOLUTION IS FOR! OPTIMIZATION!!! We’ll break it down syllabically for you so you know what to ask for. OP – TUH – MY – ZAY – SHUN!
it can recommend ideal storage locations
again, NO! This requires solving a very sophisticated optimization model it doesn’t have the data for, doesn’t know how to build, and definitely doesn’t know how to solve.
it can revamp outdated stocking policies
well, only the solution of a proper Inventory OPTIMIZATION Model that identifies the appropriate locations and safety stock levels can identify how these should be revamped
it can recommend order patterns by consumption and lead time
that’s classical curve fitting and tend projection

And, secondly, as the doctor just explained, most of what they were saying AI could do CAN’T be done with AI, and instead can only be done with analytics, optimization, and advanced mathematical models! (You know, the advanced tech (that works) that you’ve been ignoring for over two decades!)

The Gen-AI garbage is getting out of control. It’s time to stop putting up with it and start pushing back against any provider who’s trying to sell you this miracle cure silicon snake oil and show them the door. There are real solutions that work, and have worked, for two decades that will revolutionize your supply chain. You don’t need false promises and tech that isn’t ready for prime time.

Somedays the doctor just wishes he was the Scarecrow. Only someone without a brain can deal with this constant level of Gen-AI bullsh!t and not be stressed about the deluge of misinformation being spread on a daily basis! But then again, without a brain, he might be fooled by the slick salespeople that Gen-AI could give him one, instead of remembering the wise words of the True Scarecrow.

You Don’t Need Gen-AI to Revolutionize Procurement and Supply Chain Management — Classic Analytics, Optimization, and Machine Learning that You Have Been Ignoring for Two Decades Will Do Just Fine!

Open Gen-AI technology may be about as reliable as a career politician managing your Nigerian bank account, but somehow it’s won the PR war (since there is longer any requirement to speak the truth or state actual facts in sales and marketing in most “first” world countries [where they believe Alternative Math is a real thing … and that’s why they can’t balance their budgets, FYI]) as every Big X is pushing Open Gen-AI as the greatest revolution in technology since the abacus. the doctor shouldn’t be surprised, given that most of the turkeys on their rafters can’t even do basic math* (but yet profess to deeply understand this technology) and thus believe the hype (and downplay the serious risks, which we summarized in this article, where we didn’t even mention the quality of the results when you unexpectedly get a result that doesn’t exhibit any of the six major issues).

The Power of Real Spend Analysis

If you have a real Spend Analysis tool, like Spendata (The Spend Analysis Power Tool), simple data exploration will find you a 10% or more savings opportunity in just a few days (well, maybe a few weeks, but that’s still just a matter of days). It’s one of only two technologies that has been demonstrated, when properly deployed and used, to identify returns of 10% or more, year after year after year, since the mid 2000s (when the technology wasn’t nearly as good as it is today), and it can be used by any Procurement or Finance Analyst that has a basic understanding of their data.

When you have a tool that will let you analyze data around any dimension of interest — supplier, category, product — restrict it to any subset of interest — timeframe, geographic location, off-contract spend — and roll-up, compare against, and drill down by variance — the opportunities you will find will be considerable. Even in the best sourced top spend categories, you’ll usually find 2% to 3%, in the mid-spend likely 5% or more, in the tail, likely 15% or more … and that’s before you identify unexpected opportunities by division (who aren’t adhering to the new contracts), geography (where a new local supplier can slash transportation costs), product line (where subtle shifts in pricing — and yes, real spend analysis can also handle sales and pricing data — lead to unexpected sales increases and greater savings when you bump your orders to the next discount level), and even in warranty costs (when you identify that a certain supplier location is continually delivering low quality goods compared to its peers).

And that’s just the Procurement spend … it can also handle the supply chain spend, logistics spend, warranty spend, utility and HR spend — and while you can’t control the HR spend, you can get a handle on your average cost by position by location and possibly restructure your hubs during expansion time to where resources are lower cost! Savings, savings, savings … you’ll find them ’round the clock … savings, savings, savings … analytics rocks!

The Power of Strategic Sourcing Decision Optimization

Decision optimization has been around in the Procurement space for almost 25 years, but it still has less than 10% penetration! This is utterly abysmal. It’s not only the only other technology that has been generating returns of 10% or more, in good times and bad, for any leading organization that consistently uses it, but the only technology that the doctor has seen that has consistently generated 20% to 30% savings opportunities on large multi-national complex categories that just can’t be solved with RFQ and a spreadsheet, no matter how hard you try. (But if you want to pay them, a Big X will still claim they can with the old college try if you pay their top analyst’s salary for a few months … and at 5K a day, there goes three times any savings they identify.)

Examples where the doctor has repeatedly seen stellar results include:

  • national service provider contract optimization across national, regional, and local providers where rates, expected utilization, and all-in costs for remote resources are considered; With just an RFX solution, the usual solution is to go to all the relevant Big X Bodyshops and get their rate cards by role by location by base rate (with expenses picked up by the org) and all-in rate; calc. the expected local overhead rate by location; then, for each Big X – role – location, determine if the Big X all-in rate or the Big X base rate plus their overhead is cheaper and select that as the final bid for analysis; then mark the lowest bid for each role-location and determine the three top providers; then distribute the award between the three “top” providers in the lowest cost fashion; and, in big companies using a lot of contract labour, leave millions on the table because 1) sometimes the cheapest 3 will actually be the providers with the middle of the road bids across the board and 2) for some areas/roles, regional, and definitely local, providers will often be cheaper — but since the complexity is beyond manageable, this isn’t done, even though the doctor has seen multiple real-world events generate 30% to 40% savings since optimization can handle hundreds of suppliers and tens of thousands of bids and find the perfect mix (even while limiting the number of global providers and the number of providers who can service a location)
  • global mailer / catalog production —
    paper won’t go away, and when you have to balance inks, papers, printing, distribution, and mailing — it’s not always local or one country in a region that minimizes costs, it’s a very complex sourcing AND logistics distribution that optimizes costs … and the real-world model gets dizzying fast unless you use optimization, which will find 10% or more savings beyond your current best efforts
  • build-to-order assembly — don’t just leave that to the contract manufacturer, when you can simultaneously analyze the entire BoM and supply chain, which can easily dwarf the above two models if you have 50 or more items, as savings will just appear when you do so

… but yet, because it’s “math”, it doesn’t get used, even though you don’t have to do the math — the platform does!

Curve Fitting Trend Analysis

Dozens (and dozens) of “AI” models have been developed over the past few years to provide you with “predictive” forecasts, insights, and analytics, but guess what? Not a SINGLE model has outdone classical curve-fitting trend analysis — and NOT a single model ever will. (This is because all these fancy-smancy black box solutions do is attempt to identify the record/transaction “fingerprint” that contains the most relevant data and then attempt to identify the “curve” or “line” to fit it too all at once, which means the upper bound is a classical model that uses the right data and fits to the right curve from the beginning, without wasting an entire plant’s worth of energy powering entire data centers as the algorithm repeatedly guesses random fingerprints and models until one seems to work well.)

And the reality is that these standard techniques (which have been refined since the 60s and 70s), which now run blindingly fast on large data sets thanks to today’s computing, can achieve 95% to 98% accuracy in some domains, with no misfires. A 95% accurate forecast on inventory, sales, etc. is pretty damn good and minimizes the buffer stock, and lead time, you need. Detailed, fine tuned, correlation analysis can accurately predict the impact of sales and industry events. And so on.

Going one step further, there exists a host of clustering techniques that can identify emergent trends in outlier behaviour as well as pockets of customers or demand. And so on. But chances are you aren’t using any of these techniques.

So given that most of you haven’t adopted any of this technology that has proven to be reliable, effective, and extremely valuable, why on earth would you want to adopt an unproven technology that hallucinates daily, might tell of your sensitive employees with hate speech, and even leak your data? It makes ZERO sense!

While we admit that someday semi-private LLMs will be an appropriate solution for certain areas of your business where large amount of textual analysis is required on a regular basis, even these are still iffy today and can’t always be trusted. And the doctor doesn’t care how slick that chatbot is because if you have to spend days learning how to expertly craft a prompt just to get a single result, you might as well just learn to code and use a classic open source Neural Net library — you’ll get better, more reliable, results faster.

Keep an eye on the tech if you like, but nothing stops you from using the tech that works. Let your peers be the test pilots. You really don’t want to be in the cockpit when it crashes.

* And if you don’t understand why a deep understand of university level mathematics, preferably at the graduate level, is important, then you shouldn’t be touching the turkey who touches the Gen-AI solution with a 10-foot pole!