Category Archives: Market Intelligence

Trade Extensions is Redefining Sourcing, Part I

At their recent User Conference in Stockholm, recently covered by the public defender over on Spend Matters UK, Trade Extensions unveiled the upcoming version of their optimization-backed sourcing platform. With it, Trade Extensions are redefining the sourcing platform.

In first generation sourcing platforms, you have stand-alone Spend Analysis, RFX, e-Auction, Decision Optimization, Contract Management and, maybe, Supplier Information Management (SIM) modules where each module feeds into the next in a well-defined linear order: Spend Analysis into RFX. RFX into e-Auction, Decision Optimization, Contract Management, and/or SIM. e-Auction into Decision Optimization and/or Contract Management. Decision Optimization into Contract Management. Contract Management into SIM.

In second generation sourcing platforms, which permeate the market today, these modules are better integrated and data is more fluid. Data can flow back and forth between all of the modules. However, flows are still well defined. Data can flow from any module into any other, but only using well defined, scripted, workflows. You can’t break a process, or enhance a process, with capability from another module. For example, you analyze the spend data, and issue an RFX. You can analyze the costs that come back, and when you do, you can pump additional data into the optimization module. But as it’s not common to output multiple scenario awards into spend analysis, slice and dice for cost variances, and understand how the cost models and bids affect the global supply chain, there’s no easy way to push them into the spend analysis module. As a result, a user typically has to save the scenarios in .csv or Excel format and then import them into spend analysis.

In an optimization-backed sourcing platform, like TESS, that is properly defined all data is centralized in a data store and can easily be accessed by the RFX, Auction, Analytics, Optimization, and Contract Management modules. Because the system works off of common centralized data (known as fact sheets in TESS), cost models, and scenarios and not predefined, rigid, workflows, the user can approach the sourcing event the way they want to approach the sourcing event. They can create RFIs, send them out, get data back, create cost models, send them out for completion, analyze them, optimize them, go back to suppliers for cost concession or innovation requests, jump straight to negotiation and award if they get an innovative proposal, realize that a supplier can only satisfy 75%, cut the contract, and then push the unsatisfied demand back to the RFX for a subsequent round for a secondary supplier and so on. It’s extremely flexible. But it takes a skilled user to understand how to manipulate a tool that requires an advanced understanding of models, scenarios, workflow composition, and analysis. In fact, considering the user may not only have to design the workflows, but also the fact sheets, formulas, constraint definitions, and sourcing process, the skill level required to maximize the value potential of a sourcing is often more programmatic than an average user can handle. It essentially takes a skilled software developer to maximize what the platform can deliver.

And even though Trade Extensions, with their leading, state-of-the-art, optimization-backed sourcing platform has dozens upon dozens of clients making very extensive use of their platform and getting industry leading returns, they have also realized that the best results are from the most capable users and that there is always an opportunity to do better still. And not ones to settle for anything but the best there can be (even though they have the most powerful optimization-backed sourcing platform there is), they decided to figure out how to do better still. However, realizing that they were optimization experts and not design experts, they took a step back to figure out what was needed. After much consultation, soul searching, experimentation, user canvassing, and testing, they came up with an approach that will allow not just the pros, but the beginners, to use the platform with the same ease and achieve the same results, no programming skills, or training, required. And, even though they are already industry leading, that’s how they are redefining sourcing.

So what in particular is Trade Extensions doing?

We’ll get to that. But first, a little background on what’s missing (in Part II).

Just What Is A Next Generation Supplier Network?

Just about every vendor with a supplier network these days that is doing any development at all is claiming to have a next generation supplier network, but just what is a next generation supplier network?

To answer this, we first need to define what was a first generation supplier network. But this is easier said then done.

According to the procurement dynamo, in his post on how marketplaces transformed into next generation supplier networks that was posted earlier this year, last generation supplier networks were marketplaces. And that’s more less accurate.

By definition, a marketplace is where suppliers can list their wares, buyers can search and review wares of interest, and contact the supplier to place an order. And that’s pretty much what first generation networks, which weren’t that much more powerful than craigslist or e-Bay, could do.

But that doesn’t a network make. Once these “marketplaces” began to allow buyers and sellers to transmit electronic documents, manage their offerings on a public and private basis (for current customers), and securely collaborate they became networks. And this is all many networks do.

So what is a next generation supplier network? According to the procurement dynamo, first and foremost a supplier network is a collaboration framework where both parties exchange business information in order to perform better together. And the doctor agrees. But any secure messaging portal fits this description, so we need to elaborate.

Secondly, a next generation supplier network is one that allows for seamless e-document exchange in EDI, XML, and other standard, accepted, and (government and regulatory body) approved e-format between buyer and supplier supply management systems. It must provide an open API for integration into these systems because if a buyer or supplier has to log in to send or receive a document, it’s first generation.

Thirdly, a next generation supplier network is one that allows suppliers to manage public and private catalogs, with multiple price tiers on the private side, to allow potential customers to find and browse their ways, and current customers to buy, buy, buy. Similarly, it allows buyers to announce tenders, define their typical needs, and be discovered by suppliers they might miss in their searches.

And fourthly, and this is the biggie, a next generation supplier network must support the development of custom apps that allow a supplier to access a supplier portal or capability on a buyer’s platform (to do VMI, for example) or a buyer to access a buyer portal or capability on the supplier’s (sales) platform to access shipment and status information or query factory stock levels. It must not only be the “glue” that allows people to connect, but processes and platforms to connect as well. We’re not really seeing much of this yet, but this will truly distinguish a next generation platform from a current generation one.

How Do We Accelerate the Adoption of Optimization and Analytics? Part II

In Part I, we began by noting that every organization that adopts Strategic Sourcing saves time, money, and reputation but that any organization that adopts Advanced Sourcing processes and platforms saves more. A lot more. Only advanced sourcing, based on analytics and optimization, saves an organization an average of 10%+ year after year after year, even when traditional sourcing methods fail.

We also noted that even though a tremendous saving opportunity exists, the adoption of modern analytics platforms and optimization-backed sourcing platforms is still minimal, even though modern platforms, unlike their predecessors, are easy to use (and can be used by even the most junior of buyers), suitable for all purchases, and ten times as affordable as they used to be (which means that the ROI they can deliver can be up to 100X their cost). This caused us to ask why they still aren’t being adopted.

We noted that leading minds, including the prophet, have speculated that the reason(s) may include perceived complexity, lack of visualization, lack of integration, lack of guidance, and lack of collaboration, but pointed out that modern systems addressed all of this and this means that the commonly perceived reasons for lack of adoption aren’t the real reason.

So what’s the real reason? Well, as far as the doctor can tell, either people aren’t getting the message, don’t believe the message, or still inherently fear the platform despite the message. The sad fact is all of these are true to some extent.

People aren’t getting the message.

First of all, there are only six providers that offer strategic-sourcing decision optimization (SSDO), but most of these providers still offer SSDO as a stand-alone module, with only two platforms being optimization backed sourcing platform (that makes the optimization and analytics capability available across the platform, where it can be used to score and compare RFX responses and select auction winners in real time against complex cost models laden with constraints). There is only so much education, and marketing, two providers can do.

In comparison, there are dozens of sourcing platform providers that don’t offer optimization, and don’t profit by focussing on the optimization (or even the analytics) message, and, as such, don’t preach those messages. But that’s not the problem. The providers that are threatened by optimization tend to promote the myths and do everything they can to discredit these providers.

People don’t believe the message.

Many first generation providers greatly over-promised and under-delivered with respect to their optimization platforms and, as the saying goes, once bitten twice shy. When your organization spent high six, or sometimes seven, figures and got a solution only the vendor could use, and only successfully on the categories they understood, your return was limited as you were only able to get big savings on a few categories, and could only revisit those categories every few years. As a result, given that these providers promised better usability, flexibility, and ease of use for years, and never delivered, many people don’t believe that modern platforms are years ahead and useable by even junior buyers.

People fear the message.

And different people fear it for different reasons, but they generally fall into two camps. The camp that fears they can’t handle it, or the camp that fears the organization can’t handle the results it will deliver.

They can’t handle it.

They fear that they will have to be highly technical, capable of understanding advanced models, and, most importantly, have a solid command of advanced mathematics. When you consider that, with declining education, only a small percentage of American adults are “proficient” at math (as the US ranked 21st out of of 23 first world countries in numeracy in an OECD survey in 2013), and optimization is based on some of the most complex math there is, the fear is understandable, even though its unfounded as modern platforms hide the complex math and all users have to do is make sure all of the costs, constraints, and associated data elements are entered.

The organization can’t handle the results.

Many fear that if the solutions deliver and they identify savings of 10% or more on a category they are supposed to be experts in, their competency will be called into question and the whole existence of the Procurement organization as it now stands reviewed, because if they were good at their jobs, why were they overspending by so much. This is a valid fear if the executives are idiots, but a smart executive should realize that no human can analyze millions of scenarios in a few hours; that while costs rise in some categories, they fall in others; and that when entire categories can be put out to bid at once, economies of scale in both production and transportation materialize and their interplay can achieve savings that would not otherwise be realizable.

So what do you we do?

Attack the bullshit.

First of all, since the education isn’t working, let’s counter the false claims made by the providers without optimization-backed sourcing platforms. Demonstrate that their claims are baseless. Even though the laggards will be swayed, the leaders and followers will come around.

Make it personal.

Stop selling and start enabling. Thanks to the lies, damn lies, and false promises of early vendors, the buyers that were burned no longer believe vendors. Providers, even direct competitors, of these platforms have to create communities where their customers can engage with their peers and discuss the truths of the platforms they have adopted, warts and all. A dedicated buyer will gladly look past a few warts if it means better job performance (and a bigger bonus) at the end of the day.

Educate the C-Suite.

Help potential buyers educate the C-Suite that the system will deliver better results ONLY in the hand of a true professional, that a significant part of the savings come from the economies of scale and new supply chain models enabled by the software, that optimization can only save so much when applied tactically to the status quo (or, more importantly, like an auction will only generate savings once if not properly applied). Make sure they understand that the current situation is not the result of poor performance by the buyer, but the organization providing the buyer with poor tools.

Any differing thoughts?

How Do We Accelerate the Adoption of Optimization and Analytics? Part I

Every organization that adopts Strategic Sourcing saves time, money, and reputation (that would result from poor sourcing that typically results from tactical buying), but any organization that adopts Advanced Sourcing processes and platforms saves more. A lot more. Only advanced sourcing, based on analytics and optimization, saves an organization an average of 10%+ year after year after year, even when traditional sourcing methods fail.

But despite this, the adoption of modern analytics platforms and optimization-backed sourcing platforms is still minimal, and considering second generation platforms have been in existence for about ten years, and third generation platforms have been hitting the scene for the last couple of years (which can do more than first generation systems ever imagined) that can now be used by even the most junior buyer. There’s no reason that these systems are not in every leading Supply Management organization and every organization that wants to be a leading Supply Management organization.

Why aren’t these systems, which can deliver an ROI not only many times their cost but many times that of every other system, not being adopted?

Well, there are still the rampant myths that they are hard to use, require a PhD, and are only applicable for complex strategic categories, but anyone who does even a bit of research will realize that these myths only had (a shred of) validity with respect to first generation systems. There is also the belief that they are unaffordable (as first generation systems required high six figures, if not seven figures), but again research will illustrate that powerful systems are available in the five figure range and best in class systems, which support the organization end to end, can be obtained on an enterprise basis in the low six figures (and often deliver eight figures of value year after year, a 100X return). But what’s the real reason, and how do we overcome it?

If we want to really accelerate adoption, we have to figure out the critical roadblock. Last year, the prophet, in his post on “brainstorming how to accelerate the adoption of sourcing optimization” suggested the answer resided in:

  • simplifying the non-power user experience,
  • providing dynamic global/geo analysis from a visibility and risk perspective,
  • including greater API-based connectivity to back-end systems,
  • providing decision guidance as to the best models to use and scenarios to run, and
  • allowing for the sharing of models, scenarios, and best practice guidance between users

suggesting that the real reasons were

  • perceived complexity,
  • lack of visualization beyond cost tables,
  • lack of integration,
  • lack of guidance, and
  • lack of collaboration.

But, just like the myths, these reasons don’t apply to modern optimization-backed platforms, which make it easy to import and export data (for file-based integration, which is all that is needed); visualize data on a map and against constraints and identified risks; share models and scenarios; use pre-packaged cost and constraint templates (which is guidance); and walk a user through the advanced sourcing process using a wizard.

So what’s the problem? Why isn’t the adoption being accelerated? We’ll address this in Part II.

Just What Does Modern Sourcing Need?

Every year vendors, analysts, and even bloggers come up with their view of what next generation sourcing is, and what is going to get us there. This year, there is a big push towards AI (Artificial Intelligence) and not just predictive, but prescriptive analytics. Apparently, the Sourcing (and Procurement) of the future will be managed by computers, and not by experts. This is not only unnecessary, but a bit scary.

Why is it scary? Because computers run on algorithms and algorithms are not intelligence. Even though it’s theoretically possible for a sufficiently powerful computing platform to pass the Turing test*, all it does is point out the insufficiency of the Turing test for assessing the intelligence of a computer program. While computers can process significantly more data than we can, and modern predictive (trend) models, given enough data, can be more accurate than our intuition, they cannot detect when they are likely to fail or there is information or factors that need to be considered not baked into the model.

For example, if the prescriptive analytics relies on predictive analytics that relies on price trend modelling that simply takes into account price history, currency fluctuations, demand for related products or commodities, and demand for commodities that are usually used to hedge against price fluctuations in the product or commodity category, it will not detect when a natural disaster will result in a supply chain disruption that will result in less product or commodity availability in two months, which, of course, will have a drastic impact on price. As a result, the recommendation to spot-buy while the price is dropping is the wrong one, because as soon as supply drops, prices will skyrocket and it will be too late to lock in the current price.

But this isn’t the worst that can happen. If the AI that monitors multiple pricing trends, expiring contracts, and supplier performance not only ignores this blip but, instead, not only directs a resourcing for an expiring contract on an unrelated,but highly strategic, category, but encourages the inclusion of a supplier (that normally does not supply that category) that is currently in financial distress, the organization could end up blindly awarding a critical category (that is currently being served by a stable, reliable, reasonably low cost supplier) to a different supplier that is about to go bankrupt and then, seemingly without warning, stock-out on a critical product for months.

As you can probably guess, the doctor still believes that Sourcing and Procurement do not need AI and prescriptive analytics. What they really need are powerful and modifiable rules-based workflows, exception monitoring, suspicious transaction identification, and event monitoring.

The true power of a platform is to automate the tactical and streamline the strategic. Every minute of a professional’s time should be spent on strategic activities or issue resolution, not electronic paper pushing. Document matching, data collection and verification, contract monitoring, automated trend computation, etc. are all tasks that should be done automatically, but no actions with any strategic impact should be taken without intelligent human intervention.

A good rules-based workflow can allow tactical tasks, such as invoice matching and marketing monitoring, to be automated according to accepted rules and ensures that professionals only need to be involved when the parameters exceed the specified norms. Exception monitoring can insure that when something is out of expected norms, or exceeds ranges, or happens too often, it is immediately brought to the attention of the right individual. A suspicious transaction monitoring system, even if statistically based, minimizes the chances of duplicate payments, fraud, audit trail tampering, and so on. And event monitoring, even though it will produce a number of false positives, will enable a human to identify events that might impact the projected supply and cost trends for commodities and products purchased by the organization, and mark those for manual review and, possibly, re-sourcing if need be.

Modern sourcing does need better technology, but it doesn’t need artificial intelligence. It needs platforms that can help the sourcing professional focus appropriately, not guide the professional down a programmed path that will only give Sourcing and Procurement a false sense of security. The solution can track best practices for different situations, but the human still needs to determine if the system’s assessment is proper. Sourcing needs a system that empowers it with the intelligence it needs to make the right decisions, not a system that makes decisions and acts on those decisions (with automated contracts, orders, etc.) without human review and approval.

* A computer that is capable of sampling all conversations archived and currently taking place in real time, finding the one that best matches the conversation you are having, and providing that answer will provide a conversation indistinguishable from that provided from a real human, but it’s not intelligent. It merely proves the infinite monkey theorem.