Author Archives: thedoctor

With Great Data Comes Great Opportunity!

In fact, it can quadruple your ROI from a major suite.

Not long ago, Stephany Lapierre posted that your team may only be realizing <50% of the ROI from your Ariba or Coupa investment, to which, of course, my response was:

50% of value on average? WOW!

Let’s break some things down.

A suite will typically cost 4X a leaner mid-market offering which is often enough even for an enterprise just starting it’s Best in Class journey (that will take at least 8 years, as per Hackett group research in the 2000s).

Moreover, even if the enterprise can make full use of the suite it buys for 4X, at least 80% of the “opportunity” comes from just having a good process, technology, baseline capability and automation behind it. That says you’re paying 4X to squeeze an additional 20% worth of opportunity in the best case.

On average, it takes 2 to 3 years to implement a suite (on a 3 to 5 year deal). So maybe you’re seeing an average of 66% functionality over the contract duration.

As Stephany pointed out, bad data leads to

  • increased supplier discovery and management times
  • invoice processing delays and errors
  • increased risk and decreased performance insight

As well as an

  • inability to take advantage of advanced (spend) analytics
  • inability to build detailed optimization models
  • decreased accuracy in cost modelling and market prediction

This is even more problematic! Why? These are the only technologies found to deliver year-over-year 10%+ savings! (This is where the extra value a suite can offer comes from, but only with good data. Otherwise, at most half of the opportunity will be realized.)

Thus, one can argue an average organization is only getting 66% of 25% of 80% of its investment against peers (based on 2/3rd functionality, the 4X suite cost, and the baseline savings available from a basic mid-market application that instills good process and cost intelligence) and 50% of 20% (as it is able to take advantage of at most half of the advanced functionality offered by the suite due to poor and incomplete data). In other words, at the end of the day, we’d argue an average company is only realizing 23% of the potential value from an opportunity perspective!

However, as one should rightly point out, the true value of a suite is not the value you get on the base, it’s the ROI on that extra spend that allows for 20% more opportunity than a customer can get from lesser peer ProcureTech solutions.

For example, let’s say you are a company with 1B of spend with a 100M opportunity.

If tackling 20M of that opportunity requires advanced analytics, optimization, and extensive end-to-end data, it’s likely that you’ll never see that with an average mid-market solution with limited analytics, no optimization, and only baseline transactional data. If the company paid an extra 1.5M over 3 years for this enhanced functionality, then the ROI on that is 13X, which is definitely worth it.

Moreover, if the suite supports the creation of enhanced automations, you could get more throughput per employee and realize the base 80M with half or one quarter of the workforce, which would lead to a lowering of the HR budget that more than covers the baseline cost.

However, ALL of this requires great data, advanced capability, and the in-house knowledge to use both. This is only the case in the market leaders. As a result, we’d argue that the majority of clients are only realizing about 25% of the suite’s potential — when sometimes the only thing standing in their way of realizing the rest is good data.

Why Your Standard Sourcing Solution Doesn’t Work For Direct

Too many of you have been there. You sign that seven figure deal for that end-to-end Source-to-Pay suite, spend another seven figures and 18 months integrating with the ERP, PLM, AP, BI and existing Legal CR solutions, and then try to source your first NPI project natively only to … fail. Why is that?

They just weren’t built for direct.

And it’s not just something you can add in later. If the platform wasn’t designed from the ground up for direct sourcing, there’s zero chance it will ever do a decent job at it. (And, FYI, the majority of the S2P suites the big analyst firms are drooling over in their annual quadrants and waves started out as simple indirect Sourcing or Procurement tools.) People who don’t understand the nature of software don’t get this, but software has to be constructed like a building. You might hear vendors and techies throw around MVC model, which stands for Model-View-Controller, when they talk about how new and well architected their solution is, but that just means it’s built in a maintainable web-friendly way for what, and only what, it was initially designed to do.

It all comes down to the data model and the software architecture of the controller, and neither can be a black box. The data model has to be designed from the ground up to support bill of materials and direct sourcing and procurement data requirements. The controller has to provide the infrastructure to support the complexity of the application that is required. For those who don’t understand software, I like to put it this way. If you pour the foundation for a two story house, and buy wooden beams for all of your structure and supports, you can’t build a 10 story apartment building. You need a foundation for an apartment building and steel and concrete supports. (Even though you can theoretically build a 10-story structure on a two-story foundation if you have the right steel and supports, it won’t be stable. The slightest tremor on the Richter scale [which might not even be detectable by a human] or a strong wind will send it crashing down.) You need both. And just like you can’t replace the foundation under a building or replace the entire support structure in real life, you can’t do the same in code. You have to rebuild, usually from scratch.

So why weren’t they built for direct? Well, there are a number of reasons (besides they wanted to get a product to market fast and/or just weren’t smart enough to build a direct sourcing solution). They include:

  1. direct material sourcing is hard
  2. substitution is not guaranteed
  3. demand aggregation is not straight forward
  4. delivery time guarantees and on-time arrival is significantly more important

To understand these, and learn about the rest of the reasons the majority of sourcing solutions were not built for direct, dive into Standard Sourcing Technology Solutions Don’t Work for Direct – Part One and Standard Sourcing Technology Solutions Don’t Work for Direct – Part 2 over on Supply Chain Matters.

The Pundits Agree. Winter is Coming!

In a recent LinkedIn Post, THE PROPHET, a year late to the party (see the SI and Procurement Insights archives, and our Marketplace Madness post in particular), finally announced that Winter is Coming: The Great Procurement (and Broader) Legacy SaaS Rationalization and that it is going to be a very cold winter that will be Swift. Brutal. And very, very final.

There’s too many companies that took too much funding at too high a valuation with nothing to show. PE firms will be dropping these companies faster than a hot potato into boiling lava pits to focus on the companies in their portfolio with current year-over-year growth in hopes of making some of their losses back. Too many companies started without doing any research and literally built the 10th AP clone, SXM clone, RFX clone etc. of a dozen already existing solutions. (Just check the mega map if you don’t believe me.) In a race to the bottom (which helps no one), they’ll lose due to their lack of a bank account as they slash prices too deep in hopes of getting customers. Too many applications took a silo focus, didn’t build Open API centric, and the hurdles of plugging them in will be too much or too costly, and no matter how good the tech is, they won’t get bought.

And then Agentric AI will thin what remains (but not lead the cull as THE PROPHET predicts, because these AI solutions still cost money, and sometimes a lot of it, and for what they cost you can hire a real expert and not a fake one, license a few augmented intelligence tools for a quarter of what these over hyped Agentric AI platforms are charging (because they raised, and wasted, too much cash and have to recoup that before they become the next hot potato dropped into the lava pit by their PE investors), and have a super-human employee who does the work of an entire team, error free (with little to no risk of that skilled employee getting you sued as a result of a conversation, installing a back door for hackers on your systems, shutting down your systems for days, costing you 10X on a purchase due to a dot transcription error, or increasing your internal fraud [link]).

And for some of these companies, it’s already too late to pivot. For others, there are actions they can do. THE PROPHET offered some:

  • risk over-cutting
    (if done smartly)
  • consumption-based models
    (which will be more attractive to some potential customers)
  • challenge the team to earn their existence
    (but that doesn’t necessarily mean prompting GPT like a pro: you don’t want junkies)
  • redefine the sales org
    (a better playbook is key, and it needs to be differentiated)
  • Skip the Fairy Dust and Buzzwords
    (Hear! Hear! I’ve been shouting that for years! Unfortunately, not sure most of the companies out there know how to do that though! I know for a fact only the squirrels have been listening to me, and they are getting very tired of that rant. They like variety. Basically, it’s been a long, long time since marketing focussed on education and value and startups priced based on that.)

And that’s just the tip of the iceberg. SI has posted entire series on:

Failures from those who raised too much and offer too little will be coming fast and furious. This needs to be repeated. You need to be very careful which vendor you buy from and what protections you have in place if they don’t make it. (In particular, there must be a “we own our data” clause which gives you the right to all of your data and the right to export all of it into a standard file format at any time, and that specifies your data includes rules and workflow configurations and the right to export those too — for example, in spend analysis, it’s not just the data, it’s the rules that create the cubes; in invoice processing, it’s the workflow and approval rules … you won’t be able to migrate to another system quickly if all you have is the transaction data, the data that defines the processes and rules is just as important. And if you can’t export all of your data, rules, templates, etc., at any time, then don’t buy the system!)

However, while the app consolidation will be brutal, as will the renegotiations if you want to be one of the apps that make it (now that organizations are realizing they don’t need 17 apps for S&M and probably shouldn’t have 17 apps in any function, including Procurement), Agentric AI (especially at 20K a month when you can hire a REAL person for 1/4 of that) will not replace people en masse (but AI-enabled technology will). Teams will be cut and replaced by two individuals who can use next generation augmented intelligence solutions that can truncate months of research and analysis to a few days and allow strategic decisions to be made in hours, not weeks, and shifts to be made seemingly overnight while eventually allowing 99% of all tactical data processing to be automated through evolving rules and workflows under expert guidance.

Moreover, at the end of the day, relationships are not built on 1s and 0s, and they are needed now more than ever. So not only will we have skilled technologists, but skilled relationship managers. (While everyone else who does nothing but push e-paper 90% of the time or code spreadsheets will slowly be eliminated.) Of course, this means if you don’t understand optimization, analytics, statistics, game theory, economics, and logic, or you’re not an expert in relationship management, you’re screwed, but everyone had a chance to study STEM in University (and skip the woke liberal arts) and learn the technical skills for the first set of jobs.

So this also means if the platform is not enabling this next generation of employees to become more and more productive over time, its lifespan is probably short.

So get focussed in your diligence efforts on solution acquisition if you don’t want your platforms disappearing out from under your virtual feet, and if you need help, call an expert!

It’s Not Just Public Procurement Offices That Should Avoid Tech Fads

A recent article over on State Tech Magazine boldly stated that State Procurement Offices Should Carefully Avoid Tech Fads. And the headline, and author, was right. But it’s not just the public procurement offices that should be avoiding tech fads, the private sector offices should be avoiding them too. But more on this later.

The author noted that Artificial Intelligence is everywhere these days, and that news, advertising [and marketing] may leave you feeling [more than a bit] pressured to join the crowd and be an early adopter. But, as the article points out, and as THE REVELATOR would also be quick to point out, successful IT procurement inolves engaging with a comprehensive list of stakeholders, conducting thorough research and careful implementation planning, and, as THE REVELATOR reminds us on a regular basis, understanding what you need in the first place.

As the author notes, emerging technologies often present unforeseen challenges and novel issues that procurement offices must be aware of and prepared for. Failure to do due diligence can lead to embarassing or costly results. Not only do you have the extremely high failure rates with advanced tech projects, exceeding 85% according to Gartner, but, as the author points out, in the public sector technology breakdowns can have much more consequential impacts. The Air Canada lawsuit is just the first example of what is to come from the inevitable failures of AI not ready for prime time.

It’s not about the hype, it’s about the value the solution will provide, which includes, as the author notes, the total cost of ownership and longevity. The solution must fulfill the organizational need, not the hype. Otherwise, the total cost of ownership is high as no value is delivered while the longevity will be very limited.

But this should be just as true in the private sector. After all, a solution that could get you sued if it fails, that doesn’t solve the problem, that is worthless from the minute it is implemented, and that will paralyze you until a replacement is found and implemented is not something you should ever, ever want in private industry either. So don’t fall for the hype, and stay on the course that’s right — real solutions that solve real problems.

To Manage Innovation, Governments Must Fix Procurement … And Take Care Where AI is Concerned!

A recent article on Civil Service World noted two things that attracted my attention:

  1. To manage innovation, governments must fix procurement
  2. Too often, contracts in AI do not give governments powers to investigate algorithms or the data they are trained on. As a result, they risk taking the blame when things go wrong without the means to find out why.

Public Procurement is expensive. Very expensive. Given that it represents 12% of the annual GDP of an average developed economy, that is a huge amount of spend. Given that the overspend in most departments of most jurisdictions is likely as bad as in the private sector, which means, depending on the category, is likely in the 4% to 6% range at a minimum (based on the results high performing organizations see when implementing best-in-class processes and technology), that means a minimum of 1/2% of GDP is being wasted annually, but based on the fact that most public sector projects exceed initial budgets and timelines, we’d bet that the overspend is double that and at least 1% of the annual GDP. That’s a lot of waste — 770 Billion on the top 10 economies. Furthermore, that assumes that all of the spend is necessary and well planned. (There is likely considerably more savings with better demand planning, more operational efficiency, better project planning, etc. We’re just stating that the savings on committed spend alone is likely 10%.)

The article notes that despite the strategic importance of Procurement, it’s rarely seen as a priority and is more often treated as a standardized compliance function, rather than a tool for strategic investment and, in some cases, has become synonaomous with absurdity, due to an accumulation of rules so complex that even those administering them cannot interpret them creates the perverse incentive of doing the least risky thing to avoid individual liability. As a result, governments end up buying obsolete technologies that make them vulnerable, because innovation evolves so rapidly, and forces them to buy more. The cycle repeats, budgets balloon, and public capabilities diminish.

And, unfortunately, public procurement is a brick-and-mortar process, still more suited to bulk-buying precisely describable goods, accounting for them, and moving onto the next purchase. Innovation is different: you do not know today what is going to be possible tomorrow, even when you are the one inventing the tech. While governments work in one-off projects, innovation is made of ever-changing, always-fleeting products.

Furthermore, those in charge of procuring these technologies are not technologists. Public procurement is professionalized in only 38% of OECD countries, so even if officials had the incentive to experiment, they would not have the expertise.

To combat this, the authors of the article propose that Procurement systems should be like good software, fluid, flexible, and constantly evolving. However, as they note, this will take more than changing rules. As they note, it will take talent that are experts in what they are buying. It will take the treatment of Procurement as a strategic function, with clear lines for advancement for all personnel (as studies have shown that even a marginal improvement in skill can yield significant reductions in costs, times, and contracting complexity). Thirdly, they will need a federated data environment to make use of modern technology. (Especially if they want to use AI.)

This is just the start of what is necessary. There needs to be regular training. There needs to be specialization to different types of functions and purposes. There needs to be a rewrite of rules to focus on the right outcomes, not just a plethora of rules designed to prevent previously undesirable outcomes. There needs to be clear paths from buyer to public organization CPO to department head, not just paths of advancement within the Procurement function. There needs to be a focus on what’s best for the public being served, not best to minimize the risk to the buyer. And a willingness to accept that their may be a few mistakes made here and there as new buyers learn the ropes, while a willingness to weed out anyone that “makes a mistake” in order to give a contract to a supplier who is not the best fit (and do so in exchange for a kickback).

But most importantly, if they acquire AI technology, they also need to acquire the right to investigate the algorithms being used, the data it is trained on, the results of prior training, and the right to inspect any changes to the algorithms, data, and training. Otherwise, you can never trust any AI technology you might want to acquire.

Because governments need to apply the most appropriate AI-enhanced technology more than the private sector, but are the least likely to be able to use them properly.