Category Archives: Decision Optimization

Materials Requirement Planning DOES NOT Optimize Materials Replenishment. GenLots!

If you engage in direct manufacturing, chances are you have a semi-modern Enterprise Resource Planning solution (or at least a precursor Material Requirements Planning Solution) augmented by a semi-modern Supply Chain Planning solution designed to optimize your forecasts, inventory levels, and production. Chances are also good that based on forecasts (which are at least calculated down to weekly, if not daily, intervals), inventory levels (which are updated based on weekly or daily utilization by production), desired safety stock levels, and safety buffers on lead times, you have auto-replenishment set-up and the ERP will automatically generate re-orders based on lead times, safety stock alerts, or manual forecast alterations.

Chances are even greater that your demand planners will believe these are good and automatically approve them without further thought because the material requirements plan was optimized against the forecast and all available data.

NOTHING COULD BE FURTHER FROM THE TRUTH!

An optimal plan for production is NOT an optimal plan for purchase. Production requires having the right inventory on hand when you need it for the levels you need to support, balancing inventory against stock-out and obsolescence/expiration risk. Purchasing requires buying at the right volumes to take advantage of economies of scale (and tier discounts) and using the right distribution options to optimal capacity, balancing an economic order quantity (that minimizes total landed cost at a minimum) against inventory holding costs and risks of obsolescence/expiration. Your SCP enhanced ERP/MRP does the first. NOT the second. (There may be dozens of SCP systems out there, but since NONE of them support sourcing or procurement, NONE of them have the other half of the data that they would need to do this.)

And for mid-size manufacturers, this is costing them MILLIONS of dollars a year. And for large enterprise manufacturers, tens of millions of dollars a year. (In fact, the average loss from failing to optimize replenishment is 10% of inventory! This means that for every 100 Million of inventory maintained in a large enterprise, 10M is being lost. TEN MILLION.)

This is why GenLots exists — to optimize replenishment and minimize overall material lifecycle costs while maintaining (and, if possible, increasing) service levels and reducing overall working capital needs. In fact, this is so important, that this is all GenLots does (because no one else does it — which is partially the case because none of these SCP solutions do Direct Sourcing or even Direct Procurement properly, as per an ongoing Sourcing Innovation series being co-developed with Supply Chain Matters).

There are three main parts to the GenLots solution:

  • Order AI: the core optimization engine that can be directly integrated with your ERP (and is currently directly connected to SAP as a SAP Partner on the SAP store) that automatically pulls (and deletes) all the auto-generated replenishment orders from the ERP and replaces them with MR-optimized orders
  • Order UI: the UI that allows the purchasing manager to see all of the orders generated, what the original order was, and what savings and service level increases resulted from the modified order
  • Policy Advisor: the optimization-enhanced simulation engine that advises the organization on how to set their safety stocks, lead time buffers, MOQs, preferred discount tiers (for suppliers to bid against), default re-order windows, etc.

Order AI

Built on solid decision optimization and machine learning algorithms, the Order AI automatically retrieves every generated purchase order for a raw material in the ERP and calculates the right order quantity (and order date) based upon the raw material cost model (including the logistics costs using the proper mode of transport and default mode capacity), lot constraints (due to supplier or carrier capacity), scheduling requirements (based on delivery windows and processing time), safety settings (stock levels and lead time buffers), expiry windows (for perishable or decomposable stock), and, if desired, CO2 emissions (based on the available distribution options).

Order UI

This allows you to examine the orders created by GenLots and not only see the differences in order quantity and order/ship date, but the overall impacts on cost, overhead, working capital, and service level. For each material, it will break down the difference between the original supply chain cost with the system generated orders and the current supply chain cost with the GenLots orders by computing the order savings (processing and logistics costs), inventory (overhead) savings, and waste/scrap/obsolescence savings. And while the average is 10%, they have seen savings of 50% or more due to high shipment costs from too many shipments (with trucks going half full) on low value inventory, and from high waste costs (from manufacturers that pushed the safety stock and safety buffers too high and ended up wasting a lot of materials in F&B and Pharma manufacturing where shelf life of some products is very limited). It will also indicate the (estimated) service level achieved with its plan.

Policy Advisor

Optimal buys require not just optimal plans (because if that were enough, then maybe the SCP solutions wouldn’t be doing such a dismal job and costing you 10 Million on every 100 Million that flows through your warehouses), but also optimal parameters. The Policy advisor can be used to run multiple simulations to determine, for each material (based upon the production forecasts it is tied to), the appropriate safety settings (to optimize inventory levels against required service levels, warehouse capacities and carrying costs, and risk of waste), stock levels, lot sizes (and price tiers to request from suppliers), and service levels for the organization, which can lead to even greater cost savings in material replenishment when appropriately defined. (Remember, the optimization works within parameters you restrict it to, so if you restrict it to bad bounds, it won’t be able to save you nearly as much as you could save.)

Expert Support

Even though it’s available on the SAP Store, this is one solution where you should go direct (to GenLots). GenLots preferred methodology, even if the integration is literally plug-and-play for you as a SAP client (who has invested the effort to clean up their forecasting and ERP-based re-order and approval processes and ensure that clean, valid data is always available down to at least weekly intervals) is to work with its clients for the first six to twelve weeks (depending on organizational size), make sure everything runs smooth, and help its clients define the optimal (starting) policy to maximize the value and success of the GenLots solution. This is because they not only want you to see results, but see the full extent of results possible. When it comes to material replenishment, the reality is that just because you identify a few million in savings, that doesn’t mean the solution is working well. If your inventory value over a year exceeds 100 Million, it’s likely that you have a ten million dollar savings opportunity and they want to do everything in their power to maximize your chances of seeing that.

(And if, at the end of the day, with their expert guidance you only see a few million in savings, you can pat yourself on the back for being best in class in forecasting, re-order windows, and optimizing inventory policies, because you’d have to be to not see a massive savings in your first year. [Odds of this happening are less than 1/5 though if you are going through over 100M in inventory a year.] It’s no different than applying strategic sourcing decision optimization across your major categories — no matter how good you thought you were doing, studies showed time and time again an average savings of over 10% because you just couldn’t model all of the variables and compute all of the trade-offs [while adhering to all the constraints] through simple spreadsheet calculations.)

Proven Solution

GenLots may not be a name that you know in North America, but it’s one you should. Founded in 2017, the solution has been under consistent development for eight years, in daily production for six years, and is currently being used by 100 Billion-Plus companies to optimize their replenishment schedules, reduce inventory up to 20%, deliveries up to 50%, and save up to 10 Million for every 100 Million of inventory processed. It’s the best kept secret that needs to be exposed because you’re losing millions, your SCP and ERP providers will never admit otherwise, and you can stem the bleeding with a software license that starts at only 5 figures a year!

Calculum Charts your Course to Commerce Cultivation and Cash Cutting!

Calculum is a very interesting solution offering — it’s a working capital analytics solution meant to be the missing link between Finance and Procurement that just doesn’t exist today. Built to help their customers (which are mainly Global 3000 companies) to optimize their working capital across Procurement by optimizing payments and payment terms while taking weighted average cost of capital into effect, it offers a broader, and deeper, picture of cash needs and options than most platforms today.

Moreover, it goes well beyond the typical Procurement approach of simply recommending paying every supplier on the last possible day you are legally allowed to (based on either the contract or the country regulations, which they track for you) without penalty, and possibly the last possible day with penalty (if the contract is for less than the legally allowed maximum payment term) if the organization’s cost of capital is known to be lower than the penalty.

More specifically, it allows a company to understand the impact to working capital from

  • paying on a different (later) date
  • paying early (on a discount schedule)
  • paying up front (to reduce the supplier’s cost of working capital)
  • borrowing / using supply chain finance options to pay up front / early
  • using (virtual) cards

while taking into account its

  • cash conversion cycle (C2C)
  • days sales outstanding (DSO)
  • days payable outstanding (DPO)

and provide a company with true working capital and financing option optimization across Procurement, Finance, and Treasury and, in doing so, provide an average increase in free cash flow by 10% for every dollar analyzed.

Calculum does this by being possibly the only working capital optimization platform that is built on a solid spend analytics platform with its interface customized for working capital optimization, instead of category spend optimization.

Calculum starts by uploading your suppliers, contracts, and AP (invoice and payment) data, matching the data, helping you cleanse it until they have at least 90% 3-way match across your spend data (using their large, internal, supplier database of millions of suppliers ), from which they can determine immediate working capital optimization opportunities, prioritize suppliers for the realization of those opportunities, and distribute the opportunities across their 9 boxes for term extension and financing opportunities. (And you can see the exact degree of match, as well as the reasons for exception, in the match dashboards that present statistics on the data received, match rate, data quality, exclusions, and reasons for — and give you the data you, or Calculum, needs to improve the match rate)

Let’s start with that last sentence. Once the data is matched, the platform’s built in analytics will automatically identify:

  • all of your term extension opportunities across the supply base (taking into account any country legislations and noting existing terms where they are defined) organized into 9 cash flow buckets defined by impact vs. probability of success (which can be computed based upon historical supplier decisions, tracked in their centralized supplier database with anonymized data and past decisions, and similar supplier responses)
  • all of your financing opportunities from early payment discounts that are not being realized and/or negotiable discounts for early payments based on your weighted average cost of capital vs. that of your supplier also organized into 9 cash flow buckets based upon impact vs. probability of success (calculated in a similar manner)

Once the data is loaded, matched, and verified, a user can move from matching to optimizing gtheir working capital in the Opportunity dashboards and tabs. In this set of tabs and dashboards, you can:

  • see an overview that summarizes current payment terms (by contractual, opportunity, and excepted averages), cashflow opportunity, economic profit opportunity, affected entities, opportunity by category, and opportunity by program area (spend volume, supplier count, cash flow, and economic profit)
  • undertake your supplier prioritization efforts based upon your assessment of the easiest realized significant opportunities (by getting them to agree to different terms for faster payment or larger/future orders etc.)
  • review the 9 boxes financing built during the match
  • create your waterfall plan for attacking as much opportunity as possible

Moreover, because Calculum built their working capital optimization platform on a real spend analysis platform (with real cube support), which allows them to optimize payments and payment terms on multiple factors optimized against over 3 Trillion in analyzed spend, you can filter on any (set of) dimension(s) you like down to a small group of transactions.

Once you have finalized the opportunities and your waterfall/wave plan, you can move into the manage dashboards that allow you to

  • monitor your progress in the overview dashboard that tracks progress between current and target average payment terms, cashflow improvement progress, analyzed vs. planned vs contacted (effort begun/underway) vs. agreed (which could result in an unchanged term, as the opportunity should be closed either way after an [attempted] negotiation)
  • track your negotiations and reach out

And, of course, you can drill into any supplier, parent, or spend line of interest at any time because it’s a real spend analysis platform and see all of the relevant data at any level of the hierarchy that you like.

In addition, you can get a snapshot of working capital related information (spend, spend lines, (average) contracted terms, (average) payment terms, opportunity, etc. by supplier at any time simply by entering the suppliers dashboard and drilling into the supplier (or parent) of interest. The primary view will also tell you where the supplier is in the analyzed vs. planned vs contacted vs. agreed working capital optimization workflow supported by the platform. Drilling into a supplier will bring up basic corporate details, the corporate tree, any available ratings and metrics, and a payment terms vs. pricing analysis where you can calculate impacts from changes in payment terms, financing rates, your rates vs. the supplier’s rates, etc. to determine the optimal time to pay a supplier. The platform will then calculate the cost impacts of any potential/suggested change to both you and the supplier so you can make an informed decision (because sometimes an early payment doesn’t save you anything and sometimes extending a payment term costs you dearly in the long run). This allows you to propose win-win (or at least win-neutral) options that the supplier really shouldn’t be rejecting!

In addition, the platform uses AI to analyze all of the data they have on the supplier against standard strategies and built in models to recommend a detailed strategy for each supplier in the opportunity section so that you have deep guidance on how to approach a negotiation to alter the payment terms.

Moreover, Calculum is more than just a platform, it’s also a partially managed service where they work with you to ensure your data is properly uploaded and matched, the opportunities appropriately identified, the initial plan realistic and realizable, and execution effective, especially during the first few months where results and success is critical. They’re also there to support you on an ongoing basis and, if necessary, handle the refreshes / updates for you.

It’s a very unique offering and one that complements many Source-to-Pay or Procure-to-Pay platforms nicely for mid-market-plus organizations that need to maximize the value of their cash in these difficult times. It’s certainly a platform to check out if working capital optimization is front-and-center on the CFO’s mind.

We like what it’s doing and how it’s doing it and believe it is very valuable to a large segment of the mid-market. Upon a first review, there were no obvious holes or situations where we would say “the platform really should do this“, and the only point of sorrow we walked away with is that it’s not being sold by Calculon 2.0 (but then again, they are 988 years too early).

2025 Is Just Another Year … But Is It All Doom and Gloom? Part 2 (Real Tech!)

As per our first instalment, it all depends on your point of view and whether you are willing to look beyond the hype, buckle down, and get the real job done.

For instance, just the following five technologies will eliminate 95% or more of your tactical sourcing, procurement, and supplier monitoring work — and all you have to do is find them, properly implement them, and use them. Let’s talk about them briefly.

Real DIY Analytics

The ability to analyze the data you want, when you want, how you want, enriched and augmented using the auxiliary data you want … and not in predefined dash-boards or hidden “AI Agents” which may, or may not, do the analysis you want (and need) … cannot be underestimated! Real value comes from ad-hoc analysis and investigating hunches, abnormalities, and trend changes when you discover them; not days, weeks, or months later when the “cube” has been refreshed, and it might be too late to correct a problem or capture an opportunity!

Remember, this is not 2005, this is 2025, and there are at least half a dozen great DIY (spend) analysis solutions that will do most of what you want, for a price tag that is a fraction of what you might expect, and if you are okay with full DIY, some of these start at a price you can put on a P-Card. For example, Spendata Classic (which can handle data sets up to 5 Million Rows) can be obtained for $699 a year, and Enterprise, which can handle data sets up to 15 Million records, which comes with unlimited use for 5 users (and view licenses for more), and some consulting and setup, starts at an amount that will surprise you. (You can still put it on a P-Card if you pay monthly.) And there is literally nothing you can’t do in it if you’re willing to apply a little elbow grease. It truly is The Power Tool for the Power Analyst.

(Strategic Sourcing Decision / Supply Chain Network) Optimization

Yes, it’s math. But you know what? Math works! And when you use deterministic math, it’s 100% accurate, every time! And it’s one of only two technologies in S2P+ that was been proven (by multiple analyst firms) to repeatedly identify 10%+ savings year-over-year (but since this was pre-COVID and pre- the 47th, we need to amend this finding to adjust for inflation and tariffs). And as an FYI, the other technology was NOT AI. (It was proper DIY spend analysis. Only Human Intelligence can intuit where to look for previously unidentified opportunities, the best AI can do is just follow a script and run standard analysis. Furthermore, the thing about spend analysis is that an analysis that identifies an opportunity only helps you ONCE — once you capture the opportunity, the analysis is useless. You need to do a new, and different, one.)

Rule Based Automation

When you think about most tasks across Source to Pay, most of them are just execution of simple, easily defined processes — most of which don’t require much (if any) intelligence and, thus, don’t need AI (and shouldn’t use an unpredictable AI agent when you can encode a process that gets it right, guaranteed, every single time. (Plus, the way you want to source, buy, pay, track, manage, etc. is probably a little bit different than your peers, and who knows how the AI Agent would do it for you. You certainly don’t!)

With rule based automation, you can easily execute an entire sourcing event in the background all the way to award if you like. It can run auctions, it can run multi-round RFPs with detailed feedback (it’s all calculations, response comparisons, and decisions on what data you want to share and how blinded you want it), it can run analyses and optimizations, it can calculate recommended award decisions subject to the constraints and goals that matter to you, present that to you for acceptance, or, if it’s a simple winner take all or top 2 situation, create the award automatically, send it out, get supplier acceptance, assemble the contract, and send it for e-Signature. You don’t need Agentric/Gen-AI, just tech we’ve had for over a decade!

Machine Learning

Now, when it comes to Enterprise Master Data Management and Administration (E-MDMA) and Invoice Processing, it can be quite a lot of work to keep up with the mapping, cleansing, and enrichment rules, and you don’t want to have to manually define all the new rules every time a new data element appears or a new invoice format arrives, especially if the system can auto-detect/”guess” 90% of the time through rule re-use and generalization. With machine learning, the system can keep track of your corrections, mathematically extract models, and adjust it’s rules to handle the new mapping again automatically as well as improve its suggestion logic when it doesn’t know what to do — increasing the chance that you just have to “accept” a new rule vs. defining it from scratch. (Unlike Gen-AI which just tries to find similar patterns somewhere to present you with something that may or may not have any correlation to your business and even reality!) And we’ve had great non-(pure-)Neural Network machine learning that works great with enough data for decades! Predictive analytics was making huge progress late last decade before this Gen-AI BS took over and could have helped Procurement departments automate 90%+ of what they wanted to automate with just a bit more development and effort by the leading vendors — it just required a bit more time, money, and focus. (Gen-AI has set us back a decade!)

Analytics Backed Augmented Intelligence

We don’t need machines to make decisions for us (especially when they can’t think, or even reason), we need machines to do calculations for us that help us make the right decision quickly and effectively. We need the machine to automatically identify and retrieve all of the relevant data, do all the relevant situational and market analysis, do all the predictive trend analysis, identify all of the typical responses with respect to the situation, predict the likely success of each, and present us with a set of ordered recommendations, complete with the calculations and supporting analysis, so we can pick one or realize that the machine didn’t/couldn’t know about a recent event or a human factor and that none of the responses are right (and that only we could craft one, with full information on the situation). The machine may not think, but the thunking it can do far exceeds our computational ability (billions of computations a second, all flawless), and that’s EXACTLY what we should be using the machine for.

If we give up on this Artificial Intelligence BS (even if the current models are right, machines need to be 100 Million times more powerful for it to even “mimic” human intelligence. That’s not happening any time soon) and instead just give all the machines all the (boring) grunt work, leaving us free to do what they can’t (strategy and relationships). If we do so, we can be at least 10 times as productive as we are now and deliver on the promises Gen-AI / Agentric AI / AGI never will, and do so at a small fraction of the cost. And oh, we have that tech today … we just need to deploy and integrate it properly!

And this is just the beginning of what you can do when you look beyond the hype and use your Human Intelligence [HI!] to cut through all the BS.

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!

This originally posted on March 22 (2024).  It is being reposted because we need solutions, Gartner (who co-created the hype cycle) published a study which found that Gen-AI/technology implementations fail  85% of time, and its because we have abandoned the foundations — which work wonders in the hands of properly applied Human Intelligence (HI!).  Gen-AI, like all technologies, has its place, and it’s not wherever the Vendor of the Week pushes it, but where it belongs.  Please remember that.

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, Mid-Sized Consultancy, and the majority of software vendors are 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, an expert consultant will still claim they can with the old college try if you pay their top analyst’s salary for a few months … and at, say, 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 and Mid-Sized 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 / Mid-Size- 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!

Optimization Still Saves Double Digits — Why Aren’t You Using It?

Sourcing Innovation has been publishing for eighteen (18) years (over which it has published over 6,000 articles — inspired by the GruntMaster), with the first article published on June 15, 2006 with regular coverage since, including a push for all events to use sourcing optimization in Supercalifragilisticexpialidocious.

The reason is simple. It’s one of only two technologies that has been proven to identify savings in excess of 10% for almost 20 years (the other being spend analysis). The International Business Times recently reminded us of the power of this solution when it published an article on how Procurement Expert Sylvia Zhou Reduces Operational Costs by 13% Through Strategic Supply Chain Optimisation.

When we read how Zhou’s shift in sourcing strategies and supplier relations management allowed for a drastic reduction in operational costs by 13%, it reminded us of how decision optimization is not restricted just to sourcing and logistics, where it has traditionally been used, but saves across the supply chain, as discussed in our recent post on comprehensive optimization.

According to Zhou, with her team, she assessed their entire supply network, identifying bottlenecks and inefficiencies. By partnering with suppliers aligned with their operational goals and technological capabilities, they could streamline processes and cut costs. This approach worked so well that post-optimisation, her company reported a 33% increase in profits, attributed mainly to the reduced cost of goods sold and improved operational efficiencies.

And the best way to identify logistics efficiencies, product-based savings, and opportunities for operational efficiency is optimization. Sometimes there’s no better way to identify significant savings. So, go forth and optimize!