Cost Savings is NOT Cost Cutting …

… and we need more articles that hammer this point home!

A recent article over on the Supply Chain Management Review (SCMR) focussed on how strategic cost savings differ from cutting costs, highlighted a recent survey from Boston Consulting Group (BCG) that found that while 65% of executives are prioritizing supply chain and manufacturing costs as the biggest levels for organizations to pull for cost savings, 52% [are still focussed on] labour and non-labour overhead costs. OUCH!

Most Supply Chain / Procurement Departments are understaffed and/or under platformed due to lack of talent and lack of available budget. They’re also a very small part of the organizational headcount, which in many organizations is now a small part of total spend. As a result, labour is not the problem. External spend is.

And kudos to the SCMR and Laura Juliano from the Boston Consulting Group for pointing out that strategic cost control is the right approach.

If you’re spending 100M on a category, you should be doing a lot more than just a 3-bids-and-a-buy RFX, cutting a PO, and paying an invoice. A lot more. And looking at more than just the unit cost — at the very least the total cost of ownership from initial acquisition through warranty/repair and eventual disposal, if not full total value management which also looks at brand value, bundled services, etc. Even well managed direct categories usually have 3% or more savings opportunities, and those that were not well managed can have two to three times that (in the 6% to 9% range). In other words, giving one person the time to properly source one category, even if it takes 3 months of man effort, can save 3M. Even if the fully burdened resource costs your organization 240K a year, that’s an ROI of 50X on the proper use of that one resource’s time.

This one example surfaces the key point of strategic cost control. It requires strategy and strategy requires PEOPLE with real HUMAN INTELLIGENCE (HI!). (Not hallucinatory Gen-AI like “chat, j’ai pÊtÊ”). People who can analyze the situation, the available data, case studies from similar (historical) market situations, suppliers, products, and make the overall best decision(s) for the organization. And, preferably, people who can also consider the sustainability of their decision (and the implications with respect to any regulations in laws in countries they source from and sell to). (Senior Procurement leaders can’t ignore any sustainability requirements they are subject to [40% are], they definitely can’t be unaware of legislation that could affect them [37% are], and they definitely can’t be making awards to suppliers and/or for products that might just disappear in a year or three.)

In other words, you can’t reduce headcount. (You may need to replace people if you initially hired people who thought strategic procurement was catalog comparison or invoice verification, of which 95% to 99% can be fully automated, but never, ever reduce the number of people in Procurement.)

Advanced Sourcing Today — No Gen-AI Needed!

Back in late 2018 and early 2019, before the GENizah Artificial Idiocy craze began, the doctor did a sequence of AI Series (totalling 22 articles) on Spend Matters on AI in X Today, Tomorrow, and The Day After Tomorrow for Procurement, Sourcing, Sourcing Optimization, Supplier Discovery, and Supplier Management. All of which was implemented, about to be implemented, capable of being implemented, and most definitely not doable with, Gen-AI.

To make it abundantly clear that you don’t need Gen-AI for any advanced enterprise back-office (fin)tech, and that, in fact, you should never even consider it for advanced tech in these categories (because it cannot reason, cannot guarantee consistency, and confidence on the quality of its outputs can’t even be measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence that were (about to be) in development five years ago and are now available in leading best of-breed systems. And we’re continuing with Sourcing.

Unlike prior series, we’re identifying the sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently found, or will soon be found, in Source to Pay technologies that are truly AI-enhanced. (Which, FYI, may not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we continue with AI-Enhanced Sourcing that was in development “yesterday” when we wrote our first series five years ago but is now available in mature best of breed platforms for your Sourcing success. (This article sort of corresponds with AI in Sourcing Tomorrow Part I and AI in Sourcing Tomorrow Part II that were published in January, 2019 on Spend Matters.)

TODAY

Event-Based Category Alighnment

As per our Procurement series, a good AI based platform continuously analyzes (i.e. re-runs an analysis on a monthly basis) every product or service for inclusion against every organizational category and comes up with the most logical mix for the procurement organization based on likeness, current supply-base, spend-mix, and other existing parameters.

However, when it comes time for sourcing, the category should be appropriate for a sourcing event. This depends on volume, available supply base, and the category strategy (see the next item).

When it comes to sourcing, the AI will look at not only the product specifications, but also ensure there is a sufficiently large supply-base, with supply availability, spend-mix, and price trends. It will do this based on key material analysis (to identify additional suppliers in the market not yet supplying the organization), identification of market offers and volume disclosures from third party distributors vs. organizational need and overall percentages, analysis of spend vs. typical sourcing event sizes using simple (k-means) analysis, and price trends using basic curve fitting/projection. Nothing fancy.

Based upon the demand (volume), available supply base, supply availability, spend mix, price trends, and defacto templated sourcing strategy, the platform will recommend the event proceed using the standard strategy and template, proceed with modifications, or not proceed (alerting the buyer it’s not a good time, not a good event, or a new strategy is needed). It’s all traditional analytics, a smattering of machine learning, a sprinkling of pattern matching, tolerances, and confidence calculations. Nothing super fancy. The recommendation(s) will depend on a number of factors that revolve around the market conditions at the time. Current prices. Available supply base. Category dynamics in the consumer marketplace. Etc.

Category-Based Sourcing Strategy Identification

In our prior series, we indicated we’d have market-based sourcing strategy identification, and while that is in development, we’re not quite there yet. Market-based strategy identification requires a lot of data — market, supplier, marketplace, (anonymized) community intelligence, past event data, and past data from similar situations … the global marketplace has been so dynamic in recent years that we haven’t seen anything like it since pre-2000 … which was before the introduction of mass-market sourcing / procurement / modern supply chain software and we just don’t have the data.

That being said, for the majority of commodity categories, a number of leading firms have developed one or more standard sourcing strategies for the category and categorized the market conditions under which the strategies work. Modern sourcing platforms will run all the analytics against the specified demand ranges, supply vs. demand imbalance, historical price variances (since the last event), current market prices, check the thresholds, compute the match percentage and confidence, and then recommend go, go with changes/caution, don’t go — all using straight-forward trend analysis and mathematical calculations — no Gen-AI needed!

Real-Time Market vs. Response Monitoring and Automatic Pauses/Updates

As the responses come in, the application will not only track bids vs open market prices (and current prices), but compute the averages and if the bids coming in are worse than expected, alert the buyer. In a multi-round scenario, or RFQ-powered auction, the trends will be analyzed and if they are not as expected, the buyer will be alerted. In both cases, if something is off beyond a tolerance, which will adjust over time as buyer feedback on go-no go is collected, the event will automatically be paused if necessary. This just requires simple calculations against means and expectations. Good old math, a few business rules, and some workflow automation is all that is required.

Suggested Award Scenarios

Even if the platform doesn’t contain (true) strategic sourcing decision optimization [SSDO] (and see this recently updated article on Questions to Ask Your Optimization vendor for the requirements for a true SSDO solution), most modern platforms will recommend one or more award scenarios that take into account cost, business constraints, risk and carbon. It’s just a lot of combinatorial mathematical calculations and basic analytic verifications.

Carbon Impact Analysis

Using standard models for carbon production based on available data by industry, country, and when available, factory, modern platforms will use standard models and formulas to compute the carbon footprint by item, based on the supplier, the source location, and the location it is going to (and even take into account logistics based carbon production). It will do this for every item you’ve purchased, every item you’re considering, and show you the carbon impact of different award decisions vs. the status quo. No Gen-AI required! (Just a lot of formulae and data!)

SUMMARY

Now, we realize some of these descriptions, like yesterday’s, are also quite brief, but again, that’s because this is not entirely new tech, as the beginnings have been around for a few years, have been in development and discussed as “the future of” Sourcing tech before Gen-AI hit the scene, and all of these capabilities are pretty straight-forward to understand (especially with many of the fake-take and Gen-AI providers marketing these, or similar, claims, even though they are not entirely realizable within their platforms). And, if you want to dive deeper, the baseline requirements for most of these capabilities were described in depth in the doctor‘s January 2019 articles on Spend Matters. The primary purpose of this article, as with the last, was to explain how more sophisticated versions of traditional ML methodologies could be implemented in unison with human intelligence (HI!) to create smarter Sourcing applications that buyers could rely on with confidence.

Which Solution Provider Do You Want To Work With? NONE OF THE ABOVE!

In a recent LinkedIn Post, THE REVELATOR asked:

Under which category does your solution provider demo fall?

  1. đŸ¤Ģ Selectively Stealth With A Reason
  2. 🎩 Smoke And Mirrors
  3. 🌟 Courageous Dreamers

And, more importantly, which one would you, as a practitioner, prefer to work with?

the doctor, who has reviewed over 500 solutions in our space over the last two decades (and interacted with considerably more vendors than that) answered for you:

  • 𝐍𝐎𝐍𝐄 𝐎𝐅 𝐓𝐇𝐄 𝐀𝐁𝐎𝐕𝐄!

a) Selectively Stealth vendors are either

  1. considerably overrating their solution against the market (usually due to lack of homework) or
  2. hiding their solution because they know there is absolutely positively nothing unique about their offering (which is NOT a bad thing if it is easier to use, quicker to implement, better supported, and cheaper than competitors, but if that was the case, why would they be stealth?)

b) Smoke and Mirrors are

  1. greatly overselling a significantly underperforming solution (and usually trying to gouge you with a high price tag while they are at it)

c) Courageous Dreamers are

  1. selling you on a vision they may realize someday, but are usually doing so while trying to sell a woefully inadequate solution (or, a solution with one new great capability but none of the critical baseline functionality)

So what type of vendor do you want?

e. Open, Honest, and Informed

Even if they don’t have anything explicitly unique.

As SI has noted before, a good vendor is one who will be focussed on

  • a particular market size
  • one or more related industries
  • a subset of functionality where the founders / core team have strength

In addition, it will consult with organizations in that niche, analysts and consultants who serve that niche, and third party experts to get feedback during design, development, initial implementation, etc. and take all that into account in order to design a solution that will solve the problems of the aforementioned identified market niche in a manner that will be usable, and used by, the market they are going after.

It’s not about who has the most features, who has the best bells and whistles, who has the coolest sounding tech under the hood, …

IT IS ABOUT WHAT SOLUTION WILL WORK FOR YOU!

It’s the solution that will solve the 80% of your problems, that will contain all the functionality to do the tasks you do every day (not every quarter or every year), that will make those daily tasks more efficient and effective, that will be used in the majority (not the minority), that will be affordable for a business of your size, and generate an ROI.

And, sometimes the best solution is the NO-AI inside solution with nothing new, but the solution that was form fit for companies of your size in your industry, that streamlines your daily processes, that is easier to use than avoid, that solves the problems you wanted solved, and does so at a fraction of the price of the mega-suite that is just complete overkill with respect to what you are looking for.

Some of the vendors that received the best coverage here on SI are those that didn’t contain a single capability the doctor hadn’t seen ten (to one hundred) times before, but came from vendors who designed a solution for an underserved market niche, made it valuable for that market niche, and were completely honest about what they had and who they were selling to. That’s what the market needs.

AND THAT IS WHAT YOU NEED!

Want A Good Solution, Ask Vendors The Hardest Questions Off The Bat!

Even though they don’t always do so with their slimy sales and misleading marketing practices, be sure to keep it above the waist as you repeatedly hit them as hard and fast as you can. (You don’t want to dance around with a vendor unless you know they can take a few hits and are in it to win it, because once you start to dance they’ll duck and dodge until the end of time).

This post was inspired by THE REVELATOR who asked us What are the most important questions to ask a potential solution provider partner?

1) Can, and will, you show me (not tell me) live … preferably on use cases or data I give you on the spot?

I’ve said it before, and I’ll say it again: Dear Procurement Practitioner, when it comes to solution selection from today’s vendor, your mantra is Show Me, Don’t Tell Me! There’s too much hogwash out there today, buzzwords don’t solve problems, and when you dive into the marketing madness, you see there’s absolutely no value, or even core capability, in what’s being sold by many vendors.

You need solid solutions with substance, not glitzy fake-take UX, broken Gen-AI, or slack-like conversations that don’t do anything. You also need solutions that digitize and automate the 80% of Procurement / Sourcing / Supplier (Data) Management / etc. that you do day-in and day-out, week-in and week-out, month-in and month-out, and year-in and year-out and not the odd special case that comes up once a week, month, or year. And you definitely don’t need solutions that don’t fit your domain.

Moreover, you want relevant demos. If you’re buying janitorial and building maintenance supplies, you don’t want a demo on how the catalog makes it easy to buy sneakers and shoes. (FYI: I’m not being unnecessarily ridiculous here. I have been in demos where a primarily MRO buyer was demoing the top three platforms, and one showed them how to buy sneakers and shoes!) If you get such a ridiculous demo, you want to show that vendor the door as fast as possible because you have one of two situations: they didn’t do their homework on you (and don’t have a clue if their solution is appropriate), or their solution was built, and over customized, for one niche industry and will not easily support yours.

2) Once you show me the core use cases, can, and will, you explain the breadth of use cases you developed your solution for and how they are specific to my business?

You want someone who both understands what they are selling and how it might help your business. Any less and you might as well just roll the bones to select your solution provider. At the end of the day you need to understand the following:

  • there may be 666 logos on the mega map but there is no perfect solution; and nothing that will meet all of the requirements in your RFP
  • the best solution will meet the majority, and, more specifically, the requirements related to the tasks you do all the time, not the ones that you do once a quarter or once a year
  • the best solution for you will be one that comes close and comes from a vendor who both understands this and makes an effort to customize their solution for you in a manner that will achieve maximum results … from the first demo
  • the best solution provider will take the time to explain that which they don’t have time to demo, or can’t demo without your data; even the sales person will attempt it at a high level, and then bring in a product specialist for where more depth is needed

3) Once we tell you the extent of your solution we feel is appropriate, can you talk us through what the implementation and integration to our environment would require without bringing in a paid third party “expert” consultant? And how long will that take?

A great solution provider for you will be one that understands your needs, their platform, and what will need to be done in order to implement it for you, integrate it into your stack, and prepare it for you to extract the value they promise. They won’t need third party help to figure all this out and walk you through it. If a lot of integrations or data migrations are required, they, or you, may need to partner to get it done, but they should still know what’s required.

And, as we said in the introduction, while you should keep your rapid, one-two-three punch, above the belt until the vendor sales rep goes below in their tactics, you should also endeavour to hit ’em as hard as you can with that 1-2-3 combo. If they can’t take it, you want them knocked out before you waste any time on them, because any good vendor will be able to take them and come back with the best damn demo, details, and arguments as to why they are sure their solution will work for your problems, with concrete examples, in a very short timeframe. (And yes, odds are you could be unlucky and have to knock out the first 6 before you find that first 1/7 that is still there so solve problems. But the wait will be worth it.)

So You Admit You Might Be a Dead-Company Walking. How Do You Avoid the Graveyard? Part 3

In short, as per Part 1, you

  1. keep admitting to every mistake you are making and do something about it, then
  2. continue by looking for cost-effective opportunities for improvement and pursue them and finally
  3. never, ever, ever forget the timeless basics.

Today, we’ll continue by describing what you do when you identify, and admit to, one of the next two mistakes (mistakes 3 & 4) we chronicled in our two part introduction to our “dead company walking” (Part 1 and Part 2) series (where we helped your potential customers identify problems that signify you are a SaaS supplier they should be walking away from). (You can find Part 2 here.)

3) Shiny New Tech is More Important than Tried and True Methodology

Tried and true ALWAYS trumps shiny new tech in enterprise software. Especially when the total cost of ownership after factoring in license fees, maintenance, hardware & software updates, services, data feeds, integration fees, etc. usually push a solution into the realm of seven figures (and eight figures for large enterprises). Companies want a return, and shiny doesn’t generate a return. So focus on algorithms, processes, and approaches that are guaranteed to yield solutions. Do this by:

i) As THE REVELATOR would say, take an agent-based approach and focus in on what the solution should do

It’s supposed to be people-process-technology (or, in the doctor‘s words, talent-transformation-technology since you should first look for opportunities to improve your processes before investing in technology), not technology-forced workflow-prisoner! When you focus on the tech first and forget the process it is supposed to support and the people who have to use it, you’re never, ever, ever going to get it right. And believing that an over-hyped unproven technology will eventually show emergent behaviour (that it has zero chance of doing because of the underlying technology) and “evolve” to solve the problem is just stupid.

Real tech encodes real solutions identified by real human intelligence that have been implemented, repeatedly verified, tested in real world operating conditions, and understood to the point that the success can be repeated predictably. Sometimes it’s traditional AI, sometimes it’s RPA, sometimes it’s workflow, and sometimes it’s a simple calculation. It all depends on the problem and the people-process combo that can be applied to solve that problem. Tech is only transformative when it supports the business. You don’t lead with tech, you follow.

ii) Then identify the best tech options for each step

Once you have fully documented the problem(s) the people need to solve, the processes they can support, and the solutions that will work acceptably (maybe not perfectly, but it’s rare that a solution is perfect — plus, as we’ve repeatedly indicated, most enterprise users would cry tears of joys if they found something that just worked), identify all the potential tech options at your disposal, from open source to out-of-the-box from third parties to custom development.

Then, for each option, evaluate:

  • it’s cost
  • the relative return (for the customer and how that will translate into sales)

And identify the options with the best balance. You’ll be surprised at how often it’s not the new shiny. The best hotness is the old busted hotness.

iii) Select tried-and-true when all things are equal (and save)

Finally, when all things are equal, go for the most stable, tried-and-true, methodology — don’t do experimental new “AI” development if a sold optimization or analytics-based solution already exists.

4) Over Reliance on Third Party Tech is a Sustainable Business, Especially if its Gen-AI!

Reliance on third party tech, especially that which has not been proven to be on stable ground in the market, is not a good business plan. What do you do if the provider goes bankrupt, or, realizes they are on the path to bankruptcy and turns the tech off? If you have nothing else to pivot to, you go bankrupt too. Not a good scenario!

Even if you are building on tried-and-true stable tech (with a large install base), unless your business plan is to be acquired by the tech you’re building on and you actually have a chance of making that happen (you came from the provider, have good connections, etc.), your options are very, very limited if you don’t succeed.

You need to focus on a solution to a real problem that companies will pay to address first. And while it can be based 100% on third party tech to start, that shouldn’t be the game plan. The plan should be to acquire it, build something better in house, or at least find three or four options that will serve the same function until you can acquire or build appropriate tech. (The exception being a product maintained for you by a third party that is based on open source that will always be there for you to take ownership of and then assign to a new team.)

Once you have a solution, and know what tech you need, do the cost benefit analysis of:

  • licensing someone else’s tech
  • acquiring the third party tech
  • building on, and contributing to, open source
  • building your own

for each aspect of the solution, as well as the points at which one option becomes better.

In addition, even when you select a solution, always keep your eyes out for an alternative that has been demonstrated to be more reliable, efficient, or cost effective. When a problem has been newly identified, some companies will just throw anything at it to see what appears to work, without doing a proper analysis and designing a proper solution from the ground up. Eventually, some smart minds powered by Human Intelligence (HI!) will come up with a better solution. Once that becomes economical, you’ll want to switch to that if you don’t have an equivalent solution in house, while keeping back-up options open.

And definitely don’t (over) rely on third party Gen-AI — even the biggest companies can only afford to bleed for so long. There’s nothing Gen-AI can do that traditional tech can’t. It’s best uses are as an interface layer for (very) low TQ (Technology Quotient) folk who are being forced to use tech but just don’t get it (as a more natural language chatbot interface) or as a massive document store search and summarization solution (when traditional semantic / neural networks would just be overloaded). That’s it. And in all of these cases, it needs an underlying application that actually does the work and manages the data.

Stay tuned for Part 4!