Category Archives: Spend Analysis

Spendata: A True Enterprise Analytics Solution

As we indicated in our last article, while Spendata is the absolute best at spend analysis, it’s not just a spend analysis platform. It’s a general-purpose data analytics platform that can be used for much more than spend analysis.

The current end-state vision for business data analytics is a “data lake” database with a BI front end. The Big X consultancies (aided and abetted by your IT department, which is only too eager to implement another big system) will try to convince you of the data paradise you’ll have if you dump all of your business data into a data lake. Unfortunately, reality doesn’t support the vision, because organizational data is created only to the extent necessary, never verified, riddled with errors from day one, and left to decay over time as it’s never updated. The data lake is ultimately a data cesspool.

Pointing a BI tool at the (dirty) lake will spice up the data with bars, pies, waves, scatters, multi-coloured geometric shapes, and so on, but you won’t find much insight other than the realization that your data is, in fact, dirty. Worse, a published BI dashboard is like a spreadsheet you can’t modify. Try mapping new dimensions, creating new measures, adding new data, or performing even the simplest modification of an existing dimension or hierarchy, and you’ll understand why this author likes to point out that BI should actually stand for Bullsh!t Images, not Business Intelligence.

So how does a spend analysis platform like Spendata end up being a general-purpose data analytics tool? The answer is that the mechanisms and procedures associated with spend analysis and spend analysis databases, specifically data mapping and dimension derivation, can be taken to the next level — extended, generalized, and moved into real time. Once those key architectural steps are taken, the system can be further extended with view-based measures, shared cubes where custom modifications are retained across refreshes, and spreadsheet-like dependencies and recalculation at database scale.

The result is an analysis system that can be adapted not only to any of the common spend analysis problems, such as AP/PO analysis or commodity-specific cubes with item level price X quantity data, but also to savings tracking and sourcing and implementation plans. Extending the system to domains beyond spend analysis is simple: just load different data.
The bottom line is that to do real data analysis, no matter what the domain, you need:

  • the ability to extend the schema at any time
  • the ability to add new derived dimensions at any time
  • the ability to change mappings at any time
  • the ability to build derivations, data views, and mappings that are dependent on other derivations, mappings, views, inputs, linked datasets, and so on, with real-time “recalc”
  • the ability to create new views and reports relevant to the question you have … without dumping the data to Excel
  • … and preserve all of the above on cube data refreshes
  • … in your own copy of the cube so you don’t have to wait for anyone to agree
  • … and get an answer today, not on the next refresh next month when you’ve forgotten why you even had the question in the first place

You don’t get any of that from a spend analysis solution, or a BI solution, or a database pointing at a data lake. You only get that in a modern data analysis solution — which supports all of the above, and more, for any kind of data. A data analysis system works equally well across all types of numeric or set-valued data, including, but not limited to sales data, service data, warranty data, process data, and so on.

As Spendata is a real data analysis solution, it supports all of these analyses with a solution that’s easier and friendlier to use than the spreadsheet you use every day. Let’s walk through some examples so you can understand what a data analysis solution really can do.

SALES ANALYSIS

Spending data consists of numerical amounts that represent the price, tax, duty, shipping, etc. paid for items purchased. Sales data is numerical amounts that represent the price, tax, duty, shipping, etc. paid for items sold.

They are basically the inverse of each other. For every purchase, there is a sale. For every sale, there is a purchase. So, there’s absolutely no reason that you shouldn’t be able to apply the exact the same analysis (possibly in reverse) to sales data as you apply to spend data. That is, IF you have a proper data analysis tool. The latter part is the big IF because if you’re using a custom tool that needs to map all data to a schema with fixed semantics, it won’t understand the data and you’re SOL.

However, since Spendata is a general-purpose data analysis tool that builds and maintains its schema on the fly, it doesn’t care if the dataset is spend data or sales data; it’s still transactional data and it’s happy to analyze away. If you need the handholding of a workflow-oriented UI, that can also be configured out of the box using Spendata‘s new “app” capability.

Here are three types of sales analysis that Spendata supports better than CRM/Sales Forecasting systems, and that can’t be done at all with a data lake and a BI tool.

Sales Discount Variation Analysis Over Time By Salesperson … and Client Type

You run a sales team. Are your different salespeople giving the same mix of discounts by product type to the same types of customers by customer size and average sales size?

Sounds easy right? Can’t you simply plot the product/price ratio by month by salesperson in a bubble chart (where volume size correlates to bubble size) against the average trend line and calculate which salespeople are off the most (in the wrong direction)? Sure, but how do you handle client type? You could add a “color” dimension, but when the bubbles overlap and the bubbles blur, can you see it visually? Not likely. And how do you remember a low sales volume customer which is a strategic partner, so has a special deal? Theoretically you could add another column to the table “Salesperson, Product/Price Ratio, Client Type, Over/Under Average”, and that would work as long as you could pre-compute the average discount by Product/Price Ratio and Client Type.

And then you realize that unless you group by category, you have entirely different products in the same product/price ratio and your multi-stage analysis is worthless, so you have to go back and start again, only to find out that the bubble chart is only pseudo-useful (as you can’t really figure it out visually because what is that shade of pink (from the multiple red and white bubbles overlapping) — Fuchsia, Bright, or Barbie — and what does it mean) and you will have to focus on the fixed table to extract any value at all from the analysis.

But then you’ll realize that you still need to see monthly variations in the chart, meaning you want the ability to drag a slider or change the month and have the bubble chart update. Uh-oh, you forgot to individually compute all the amounts by month or select the slider graph! Back to square one, doing it all over again by month. Then you notice some customers have long-term, fixed prices on some products, which messes up the average discount on these products as the prices for these customers are not changing over time. You redo the work for the third (or is it the fourth? time), and then you realize that your definitions of client type “large, medium, and small” are slightly off as a client that should be in large is in medium and two that should be in small were made medium. Aaarrrggghhh!!!

But with Spendata, you simply create or modify dimensions to the cube to segment the data (customer type, product groups, etc.) You leverage a dynamic view-based measure by customer type to set the average prices per time period (used to calculate the discount). You then use filters to define the time range of interest, another view with filters to click through the months over time, a derived view to see the performance by quarter, another by year. If you change the definition of client type (which customers belong to which client type), which products for customers are fixed prices, which SKU’s that are the same type, time range of interest, etc. you simply map them and the entire analysis auto-updates.

This flexibility and power (with no wasted effort) gives you a very deep analysis capability NOT available in any other data analysis platform. For example, you can find out with a few clicks that your “best” salesperson in terms of giving the lowest average discount is actually costing you the most. Turns out, he’s not serving any large customers (who get good discounts) and has several fixed price contracts (which mess up the average discounts). So, the discounts he’s giving the small clients, while less than what large customers get, are significantly more than what other salespeople provide to other small customers. This is something you’d never know if you didn’t have the power of Spendata as your data consultant would give up on the variance analysis at the global level because the salesman’s overall ratio looked good.

Post-Merger White-Space Analysis

White space sales analysis is looking for spaces in the market where you should be selling but are not. For example, if you sell to restaurants, you could look at your sales by geography, normalized by the number of establishments by type or the sales of the restaurants by type in that geography. In a merger, you could measure your penetration at each customer for each of the original companies. You can find white space by looking at each customer (or customer segment) and measuring revenue per customer employee across the two companies. Where is one more effective than the other?

You might think this is no big deal because this was theoretically done during the due diligence and the opportunity for overlap was deemed to be there, as well as the opportunity for whitespace, and whatever was done was good enough. The reality couldn’t be further from the truth.

If the whitespace analysis was done with a standard analytics tool, it has all the following problems:

  • matching vendors were missed due to different name entries and missing ids
  • vendors were not familied by parent (within industry, geography, etc.)
  • the improperly merged vendors were only compared against a target file built by the consultants and misses vendors
  • i.e. it’s poor, but no worse than you’d do with a traditional analytics tool

But with Spendata, these problems would be at least minimized, if not eliminated because:

  • Spendata comes with auto-matching capability
  • … that can be used to enrich the suppliers with NAICS categorization (for example)
  • Spendata comes with auto-familying capability so parent-child relationships aren’t missed
  • Spendata can load all of the companies from a firmographic database with their NAICS codes in a separate cube …
  • … and then federation can be used to match the suppliers in use with the suppliers in the appropriate NAICS category for the white space analysis

It’s thus trivial to

  1. load up a cube with organization A’s sales by supplier (which can be the output from a view on a transaction database), and run it through a view that embeds a normalization routine so that all records that actually correspond to the same supplier (or parent-child where only the parent is relevant) are grouped into one line
  2. load up a cube with organization B’s sales by supplier and do the same … and now you know you have exact matches between supplier names
  3. load up the NAICS code database – which is a list of possible customers
  4. build a view that pulls in, for each supplier in the NAICS category of interest, Org A spend, Org B Spend, and Total Spend
  5. create a filter to only show zero spend suppliers — and there’s the whitespace … 100% complete. Now send your sales teams after these.
  6. Create a filter to show where your sales are less than expected (eg. from comparable other customers or Org A or Org B). This is additional whitespace where upselling or further customer penetration is appropriate.

Bill Rate Analysis

A smart company doesn’t just analyze their (total) spend by service provider, they analyze by service role and against the service role average when different divisions/locations are contracting for the same service that should be fulfilled by a professional with roughly the same skills and same experience level. Why? Because if you’re paying, on average, 150/hr for an intermediate DBA across 80% of locations and 250/hr across the remaining 20%, you’re paying as much as 66% too much at those remaining locations, with the exception being San Francisco or New York where your service provider has to pay their locals a cost-of-living top-up just so they can afford to live there.

By the same token, a smart service company is analyzing what they are getting by role, location, and customer and trying to identify the customers that are (the most) profitable and those that are the least (or unprofitable when you take contract size or support requirements into account), so they can focus on those customers that are profitable, and, hopefully, keep them happy with their better talent (and not just the newest turkey on the rafter).

However, just like sales discount variation analysis over time by client type, this is tough as it’s essentially a variation of that analysis, except you are looking at services instead of products, roles instead of client types, and customer instead of sales rep … and then, for your problem clients, looking at which service reps are responsible … so after you do the base analysis (using dynamic view based measures), you’re creating new views with new measures and filters to group by service rep and filter to those too far beyond a threshold. In any other tool, it would be nigh impossible for even an expert analyst. In Spendata, it’s a matter of minutes. Literally.

And this is just the tip of the iceberg in terms of what Spendata can do. In a future article, we’ll dive into a few more areas of analysis that require very specialized tools in different domains, but which can be done with ease in Spendata. Stay tuned!

CF Suite for your Consumer-Friendly Source-to-Contract Needs

Founded in 2004 to help public and private sector companies save money through reverse auctions, the Curtis Fitch Solution has expanded since then to offer a source-to-contract procurement solution, which includes extensive supplier onboarding evaluation, performance management, contract lifecycle management, and spend and performance management. Curtis Fitch offers the following capabilities in its solution.

Supplier Insight

CF Supplier Insights is their supplier registration, onboarding, information, and relationship management solution. It supports the creation and delivery of customized questionnaires, which can be associated with organizational categories anywhere in the 4-level hierarchy supported, so that suppliers are only asked to provide information that the organization needs for their qualification. You can track insurance and key certification requirements, with due dates for auto-reminders, to enable suppliers to self-serve. Supplier Insights offers task-oriented dashboards to help a buyer or evaluator focus in on what needs to be done.

The supplier management module presents supplier profiles in a clear and easy to view way, showing company details, registration audit, location, and contact information, etc.. You can quickly view an audit trail of any activity that the supplier is linked to in CF Suite, including access to onboarding questionnaires, insurance and certification documents, events they were involved in, quotes they provided, contracts that were awarded, categories they are associated with, and balanced scorecards.

When insurance and certifications are requested, so is the associated metadata like coverage, award date, expiry date, and insurer/granter. This information is monitored, and both the buyer and supplier are alerted when the expiration date is approaching. The system defines default metadata for all suppliers, but buyers can add their own fields as needed.

It’s easy to search for suppliers by name, status, workflow stage, and location, or simply scan through them by name. The buyer can choose to “hide” suppliers that have not completed the registration process and they will not be available for sourcing events or contracting.

e-Sourcing

CF eSourcing is their sourcing project management and RFx platform where a user can define event and RFx templates, create multi-round sourcing projects, evaluate the responses using weighted scoring and multi-party ratings, define awards, and track procurement spend against savings. Also, all of the metadata is available for scorecards, contracting, and event creation, so if a supplier doesn’t have the necessary coverage or certification, the supplier can be filtered out of the event, or the buyer can proactively ensure they are not invited.

Events can be created from scratch but are usually created from templates to support standardization across the business. An RFx template can define stakeholders, suppliers (or categories), and any sourcing information, including important documentation. In addition, a procurement workplan can be designed to reflect any sign off gates as necessary when supporting the appropriate public sector requirements some buying organizations must adhere to.

Building RFx templates is easy to do and there’s a variety of question styles available, depending on the response required from the vendor (i.e. free text, multichoice, file upload, financial etc.) RFx’s can be built by importing question sets, linking to supplier onboarding information, or via a template. The tool offers tender evaluation with auto-weighting and scoring functionality (based on values or pre-defined option selections). Their clients’ buyers can invite stakeholders to evaluate a tender and what the evaluator scores can be pre-defined. In addition, when it comes to RFQs for gathering the quotes, it supports total cost breakdowns and arbitrary formulas. Supplier submissions and quotes can be exported to Excel, including any supplier document.

The one potential limitation is that there is not a lot of built in analysis / side-by-side comparison for price analysis in Sourcing, as most buyers prefer to either do their analysis in Excel or use custom dashboards in analytics.

In addition, e-Sourcing events can be organized into projects that can not only group related sourcing events, and provide an overarching workflow, but can also be used to track actuals against the historical baseline and forecasted actuals for a realized savings calculation.

e-Auctions

CF Suite also includes CF Auctions. There are four styles of auction available for running both forward and reverse auctions; English, Sequential, Dutch, and Japanese auctions, which can all be executed and managed in real time. Auctions are easy to define and very easy to monitor by the buying organization as they can see the current bid for each supplier and associated baseline and target information that is hidden from the suppliers, allowing them to track progress against not only starting bids, but goals and see a real-time evaluation of the benefit associated with a bid.

Suppliers get easy to use bidding views, and depending on the settings, suppliers will either see their current rank or distance from lowest bid and can easily update their submissions or ask questions. Buyers can respond to suppliers one-on-one or send messages to all suppliers during the auction.

In addition, if something goes wrong, buyers can manage the event in real time and pause it, extend it, change owners, change supplier reps, and so on to ensure a successful auction.

Contract Management

CF Contracts Contract management enables procurement to build high churn contracts with limited and / or no clause changes, for example, NDAs or Terms of Service. CF Contracts has a clause library, workflow for internal sign off, and integrated redline tracking. Procurement can negotiate with suppliers through the tool, and once a contract has been drafted in CF Suite, the platform can be used to track versions, see redlines, accept a version for signing, and manage the e-Signature process. If CF Suite was used for sourcing, then if a contract is awarded off the back of an event, the contract can be linked with the award information from the sourcing module.

Most of their clients focus on using contracts as a central contract repository database to improve visibility of key contract information, and to feed into reporting outputs to support the management of the contract pipeline, including contract spend and contract renewals.

The contract database includes a pool of common fields (i.e. contract title, start and end dates, contract values etc.) and their clients can create custom fields to ensure the contract records align with their business data. Buyers can create automated contract renewal alerts that can be shared with the contract manager, business stakeholders or the contract management team, as one would expect from a contract management module.

Supplier Scorecards

CF Scorecards is their compliance, risk, and performance management solution that collates ongoing supplier risk management information into a central location. CF Suite uses all of this data to create a 360 degree supplier scorecard for managing risk, performance and development on an ongoing basis.

The great thing about scorecards is that you can select the questionnaires and third-party data you want to include, define the weightings, define the stakeholders who will be scoring the responses that can’t be auto-scored, and get a truly custom 360-degree scorecard on risk, compliance, and/or performance. You can attach associated documents, contracts, supplier onboarding questionnaires, third party assessments, and audits as desired to back up the scorecard, which provides a solid foundation for supplier performance, risk, and compliance management and development plan creation.

Data Analytics

Powered by Qlik, CF Analytics provides out-of-the-box dashboards and reports to help analyze spend, manage contract pipelines and lifecycles, track supplier onboarding workflow and status, and manage ongoing supplier risk . Client organizations can also create their own dashboards and reports as required, or Curtis Fitch can create additional dashboards and reports for the client on implementation. Curtis Fitch has API integrations available as standard for those clients that wish to analyse data in their preferred business tool, like Power BI, or Tableau.

The out-of-the-box dashboards and reports are well designed and take full advantage of the Qlik tool. The process management, contract/supplier status dashboard, and performance management dashboards are especially well thought out and designed. For example, the project management dashboard will show you the status of each sourcing project by stage and task, how many tasks are coming due and overdue, the total value of projects in each stage, and so on. Other process-oriented dashboards for contracts and supplier management are equally well done. For example, the contract management dashboard allows you to filter in by supplier category, or contract grouping and see upcoming milestones in the next 30 days, 60 days, and 90 days as well as overdue milestones.

The spend dashboards include all the standard dashboards you’d expect in a suite, and they are very easy to use with built-in filtering capability to quickly drill down to the precise spend you are interested in. The only down-side is they are OLAP based, and updates are daily. However, they are considering adding support for one or more BoB spend analysis platforms for those that want more advanced analytics capability.

Overall

It’s clear that the Curtis Fitch platform is a mature, well thought out, fleshed out platform for source to contract for indirect and direct services in both the public and private sector and a great solution not only for the global FTSE 100 companies they support, but the mid-market and enterprise market. It’s also very likely to be adopted, a key factor for success, because, as we pointed out in our headline, it’s very consumer friendly. While the UI design might look a bit dated (just like the design of Sourcing Innovation), it was designed that way because it’s extremely usable and, thus, very consumer friendly.

Curtis Fitch have an active roadmap, following development best practices, alongside scoping workshops, where they partner with their clients to ensure new features and benefits are based on user requirements. Many modern applications with flashy UIs, modern hieroglyphs, and text-based conversational interfaces might look cool, but at the end of the day sourcing professionals want to get the job done and don’t want to be blinded by vast swathes of functionality when looking for a specific feature. Procurement professionals want a well-designed, intuitive, guided workflow, a ‘3-clicks and I’m there’ style application that will get the job done efficiently and effectively. This is what CF Suite offers.

Conclusion

While there are some limitations in award analysis (as most users prefer to do that in Excel) and analytics (as it’s built on QlikSense), and not a lot of functionality that is truly unique if you compare it to functionality in the market overall, it is one of the broadest and deepest mid-market+ suites out there and can provide a lot of value to a lot of organizations. In addition, Curtis Fitch also offers consulting and managed auction/RFX services which can be very helpful to an understaffed organization as they can get some staff augmentation / event support while also having full visibility into the process and the ability to take over fully when they are ready. If you’re looking for a tightly integrated, highly useable, easily adopted Source-to-Contract platform with more contract and supplier management ability than you might expect, include CF Suite in the RFP. It’s certainly worth an investigation.

The Sourcing Innovation Source-to-Pay+ Mega Map!

Now slightly less useless than every other logo map that clogs your feeds!

1. Every vendor verified to still be operating as of 4 days ago!
Compare that to the maps that often have vendors / solutions that haven’t been in business / operating as a standalone entity in months on the day of release! (Or “best-of” lists that sometimes have vendors that haven’t existed in 4 years! the doctor has seen both — this year!)

2. Every vendor logo is clickable!
the doctor doesn’t know about you, but he finds it incredibly useless when all you get is a strange symbol with no explanation or a font so small that you would need an electron microscope to read it. So, to fix that, every logo is clickable so you can go to the site and at least figure out who the vendor is.

3. Every vendor is mapped to the closest standard category/categories!
Furthermore, every category has the standard definitions used by Sourcing Innovation and Spend Matters!
the doctor can’t make sense of random categories like “specialists” or “collaborative” or “innovative“, despises when maps follow this new age analyst/consultancy award trend and give you labels you just can’t use, and gets red in the face when two very distinct categories (like e-Sourcing and Marketplaces or Expenses and AP are merged into one). Now, the doctor will also readily admit that this means that not all vendors in a category are necessarily comparable on an apples-to-apples basis, but that was never the case anyway as most solutions in a category break down into subcategories and, for example, in Supplier Management (SXM) alone, you have a CORNED QUIP mash of solutions that could be focused on just a small subset of the (at least) ten different (primary) capabilities. (See the link on the sidebar that takes you to a post that indexes 90+ Supplier Management vendors across 10 key capabilities.)

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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.)

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!