Author Archives: Michael Lamoureux



AI in Supplier Management: The Day After Tomorrow

digital business transformation

In Spend Matters’ last pair of articles for the PRO series AI in Supplier Management, we reviewed some of the exciting capabilities that you will be able to expect in tomorrow's supplier management platforms, where we define AI, for the purposes of this article, as “augmented intelligence” because, as we've stated in our AI series, there is no true AI in any enterprise technology today.

In our initial entries of the series, we discussed how the advancements in usability and computing power have made it possible for platforms to implement better and more powerful guided on-boarding mechanisms that can allow a supplier to on-board from existing profiles more quickly and efficiently than ever before. We also discussed how embedded community intelligence will help you make better supplier selections, better performance monitoring will help you keep on top of performance problems before they lead to disruptions, KPI monitoring will identify a range of issues, risk monitoring will identify risks as soon as they come to pass, and resource assignment will be automated for common project tasks.

In our follow-up entries, we indicated that each of these capabilities would be improved with automated reasoning and machine learning technologies. Profiles would be automatically maintained. Community supplier intelligence will be augmented with supplier intelligence. Relationship status will be monitored in real time across all purchases and projects. When issues arise, corrective action plans will be automatically created. When risks are identified, mitigation plans will be automatically created. When resources are needed for more critical projects, they will be re-assigned, and projects realigned, in real time.

But is this the best we can hope for?

When we extend our event horizon out further into the future, we can predict that, at some point, industry-leading supplier management platforms are going to support:

— Supplier future state predictions
— Category-based supplier rebalancing
— Supply chain rebalancing
— Real-time order rebalancing

AI in Supplier Management: Tomorrow (Part 2)

complex sourcing

In Part 1 of AI in Supplier Management: Tomorrow, we began our discussion of some of the AI-enabled capabilities that you can expect to find in tomorrow's supplier management platforms, where we define AI as assisted intelligence (because, as we have discussed, there is no true artificial intelligence in enterprise platforms today and there won't be tomorrow either). AI is a buzzword, not a reality. But we don't need true AI to achieve software that can radically increase our productivity. Reaching assisted intelligence will add multiples to our efficiency and effectiveness.

In our last article, we discussed how tomorrow's supplier management platforms will offer smart, automatic, supplier profile update (suggestions) — taking the headaches out of profile maintenance that results in most profiles being out of date in a supplier management system shortly after they are created; market-based supplier intelligence that is more in line and reflective with reality — and not just the experience of an anomalous customer subset; and real-time relationship monitoring that paints a relatively full picture of the relationship, not just a point-based performance picture.

So what else will tomorrow's platforms do to help you focus more on the strategic side of supplier management? Let’s look at the next three areas:

— Automated resolution plan creation, monitoring and adjustment
— Automated risk mitigation strategy identification
— Optimized real-time resource re-alignment

Tealbook: Vendor Introduction (Part 2) — Product Strengths and Weaknesses

cloud solutions

In our last Spend Matters PRO brief, we introduced you to Tealbook, a five-year-old provider based out of Toronto (with an office in New York City) that is deploying a new platform for supplier information management (SIM) and discovery. Combining machine learning to accelerate data cleansing and gathering with a social media-like user experience to encourage collaborative supplier information management, Tealbook is gaining use cases and enterprise-class procurement customers that want to:

— Consolidate and better manage their supplier master data — aka the “I” (Information and Intelligence) in SIM.
— Discover and on-board new suppliers more effectively than 1) Google searches and 2) searches within proprietary supplier networks.
— Create a system of intelligence surrounding suppliers both internally (e.g., within a spend category team or project team) and externally through fully permissioned, community-based knowledge sharing.
— Quickly bring supplier diversity programs to target levels.

Part 1 of this brief provided an overview of Tealbook’s offering and a short selection requirements checklist that outlined the typical company for which Tealbook might be a good fit.

In Part 2, we provide a breakdown of what is comparatively good (and not so good) about the solution, a high-level SWOT analysis, and some final conclusions and takeaways.

Tealbook: Vendor Introduction (Part 1) — Background and Solution Overview

Procurement organizations today talk a big game about automating transactional processes so that they can focus on upstream value creation opportunities. The thinking goes like this: The biggest opportunities for procurement are not in squeezing diminishing savings out of the usual vendors year after year but in identifying and contracting with the most innovative suppliers that can enable exclusive competitive advantages. These include not only strategic sourcing efforts around major categories or products but also mutually beneficial relationship-based activities like supplier collaboration, development, innovation and risk mitigation.

Yet there are several obstacles to this shift in emphasis toward more strategic activities. One is remarkably simple: The majority of procurement organizations do not have a single, accurate record of all of their suppliers. Most of the vital information that would constitute a vendor master file is instead scattered across various silos, including ERP systems, dedicated P2P or S2P tools, homegrown tools, and proverbial three-ring binders. So before procurement can earnestly attempt to spend more time on higher-impact value creation opportunities, most organizations have a lot of work to do forming a baseline off which they can build stronger supplier management, discovery and development competencies. This baseline of supplier knowledge is not just about maintaining an accurate vendor master file to pay the bills, but also a hub for information to help build supplier intelligence and a private supplier network (albeit with some community-based elements) rather than any single commercial network/marketplace.

Helping organizations form this baseline is how Tealbook, a four-year-old provider based out of Toronto (with an office in New York City), is deploying its platform for supplier information management and discovery. Combining machine learning to accelerate data cleansing and gathering with a social media-like user experience to encourage collaborative supplier information management, Tealbook is gaining use cases with enterprise-level procurement organizations that want to consolidate their efforts in master data management (MDM), quickly bring their supplier diversity programs to target levels, and find new suppliers more effectively than a search on the open web allows, as well as expedite the supplier on-boarding process. And as it continues to bring more users and suppliers into its network, Tealbook generates insights that becomes increasingly valuable to its community (without ever sharing proprietary information between organizations).

This Spend Matters PRO Vendor Introduction offers a candid take on Tealbook and its capabilities. The first part of this brief includes an overview of Tealbook’s offering and a short selection requirements checklist that outlines the typical company for which Tealbook might be a good fit. The second part of this brief provides a breakdown of what is comparatively good (and not so good) about the solution, a high-level SWOT analysis, and some market implications and takeaways.

AI in Supplier Management: Tomorrow (Part 1)

In our last pair of Spend Matters PRO articles about AI in supplier management today, Part 1 and Part 2, we overviewed some situations where you can find AI in e-sourcing platforms today, where we define AI as “assisted intelligence” because, as we've stated in our series about AI, there is no true artificial intelligence in any enterprise technology today. In fact, there is nothing close, at least not on the open marketplace.

But when we get to the point where we have an augmented intelligence solution that can help us not only monitor supplier performance (across a community), automatically identify issues and risks, and even help us with automated resource — and asset — assignment but can also help us identify automated corrective action resolution plans, risk mitigation strategies, and real-time relationship monitoring and resource re-alignment, they start to approach augmented intelligence and become quite useful to us indeed.

In this article, we are going to discuss the AI-enabled functionality that we expect to see in the leading supply management platforms tomorrow. We will continue our pattern and start by defining what we expect to see, how it will likely work, and then give some hints of the technology platform that will underlie it.

Tomorrow, we expect that the leading supplier management platform will also have the following capabilities:

— Smart information selection and auto profile updates
— Market-based supplier intelligence
— Real-time relationship monitoring
— Automated resolution plan creation, monitoring and adjustment
— Automated risk mitigation strategy identification
— Optimized real-time resource re-alignment

AI in Supplier Management: Today (Part 2)

As we have been repeating throughout this PRO Spend Matters’ AI series, AI is the reigning buzzword of the day in sourcing and procurement software. Supplier management is no exception. Just about every vendor out there trying to get an edge in the space is claiming to have AI, even if all they have is a pinch of RPA. That's why, in Part 1, we reviewed the technology ladder from RPA to "cognitive" — and insisted that while there is no true artificial intelligence out there today, we will start to see “assisted intelligence” and, later, “augmented intelligence” as the software gets more mature and more powerful.

And while we may not see true AI for decades, we do need assisted and augmented intelligence to efficiently and effectively do our jobs. As with supplier discovery, sometimes there is just too much supplier data to weed through to on-board, qualify, track and manage suppliers in an efficient and effective manner. It's really hampering our productivity.

But the right platforms will change all that. As per Part 1, the best platforms of today will:

— speed up and simplify on-boarding for us and our suppliers with auto-fill from databases, networks and third-party information sources.
— offer basic community supplier intelligence to provide quick, differentiating insights between suppliers with similar profiles but greatly differentiated capabilities.
— provide real-time performance insight and alerts to issues that need, or may soon need, attention from a real person versus just automated follow-ups with a supplier.


This is great, but it is not all they can do. We really need platforms that can be all they can be in order to truly take supplier management to the next level as an organizational practice ... versus a point-based endeavor with suppliers that we think are strategic or need our help.

The best platforms on the market today can also help with:

— automated issue identification — automated risk identification — automated resource assignment

And we will discuss each of these required capabilities in the rest of this article.

AI in Supplier Management: Today (Part 1)

suppliers

With this brief we begin the next installment of our series on the application of artificial intelligence (AI) to various source-to-pay technologies. Previous entries focused on AI in procurement (Today, Part 1 and Part 2; Tomorrow, Part 1, Part 2 and Part 3; and The Day After Tomorrow), AI in sourcing (Today; Tomorrow, Part 1 and Part 2; and The Day After Tomorrow), AI in sourcing optimization (Today; Tomorrow; and The Day After Tomorrow, Part 1 and Part 2) and AI in supplier discovery (Today, Tomorrow and The Day After Tomorrow).

Following the path from supplier discovery and selection is the topic of our current series, supplier management. As with each preceding entry, the aim is to define what is available with AI(-like) technology and what will be possible tomorrow. And just as the best platforms for supplier discovery are starting to use machine learning and RPA, so too are the best supplier management platforms — but we're getting ahead of ourselves.

Tradeshift Innovation Summit in London: A Few Takeaways

Brexit

Last week, I attended Tradeshift's new “Innovation Summit” series in London. It was a short event, lasting only an afternoon, and obviously one intended to test the waters to the receptiveness of both the format and the location, but an interesting one nonetheless.

From a vision perspective, Tradeshift is almost dead-on in terms of what the platform of the future has to look like, and from a marketing perspective, it makes perfect sense. But when there are still a large number of organizations using Excel and email, and a larger number still who remain on first-generation best-of-breed procurement applications, it's hard to sell a true “Procurement 3.0” solution approach when the majority of the world hasn't even caught on to “Procurement 2.0” solutions.

AI in Supplier Discovery: The Day After Tomorrow

In our initial entry of the series, AI in Supplier Discovery: Today, we discussed how the advancements in usability and computing power have made it possible for platforms to implement better and more powerful search algorithms that can actually make searches useful across wide supplier directories and networks. Then, in our last entry, AI in Supplier Discovery: Tomorrow, we discussed how the inclusion of advanced semantic processing, high dimensional (fingerprint) similarity clustering algorithms, range and "like" search algorithms, and machine learning that can improve the algorithms over time as humans identify "good" versus "bad" matches will allow even better, smarter, more useful searches to be performed in the days to come for the identification of the right suppliers for direct categories and services.

But is that the best we can hope for?

While that is all we can hope for tomorrow, we can hope for even more the day after that. More specifically, when we extend our event horizon out just a little bit further, we can predict that at some point in the future, supplier discovery systems are going to support innovative supplier discovery (based on performance, need and soft factors) and predictive smart search (based on upcoming projects, performance profiles and real-time community feedback).

Sustainability, Environmental Stewardship and CSR: The CPO’s Outside-In Agenda (Part 2B)

sustainable supply chain

In our last article in this Spend Matters PRO series, we focused on several pressing issues that are shaping procurement from the outside in, yet chief procurement officers are primarily still concerned with issues set by an inside-out agenda — that is, cost-cutting and supply assurance targets mandated by upper management. However, our PESTLE analysis of factors shaping the modern CPO agenda identified broad outside-in trends that an organization needs to consider if it wants to truly tap and manage the opportunities (and risks) offered by external supply markets. (Read the CPO’s Conundrum: Parts 1A and 1B.)

Nowhere is this more readily apparent than with the topic of sustainability and environmental stewardship, the focus of today’s brief. The environment is an inseparable component of any business. It forms the platform layer off which all goods and services are produced, and cannot be ignored. And the difference between effective and sustainable management and ineffective and unsustainable management, as pointed out in yesterday’s article, is shocking. Not only would investments in environmental sustainability focussed companies over the past two decades doubled an average rate of return, but millennials will pay a (small) premium for sustainably (and ethically) sourced products and you are ensuring that you will have raw material supply for years (and decades to come).

Sustainability, Environmental Stewardship and CSR: The CPO’s Outside-In Agenda (Part 2A)

leading cross-functional teams

In this first installment of this Spend Matters PRO series (see Part 1A and Part 1B), we noted that a number of pressing issues are shaping procurement from the outside in, yet chief procurement officers (CPOs) are primarily still concerned with issues set by an inside-out agenda — that is, cost cutting and supply assurance targets mandated by upper management. Our PESTLE analysis of factors shaping the modern CPO agenda identified broad trends like economic instability, globalization, changing digital business strategies and the need to address corporate social responsibility (CSR) as areas that procurement organizations need to consider if they want to truly tap and manage the opportunities (and risks) offered by external supply markets.

Perhaps nowhere is this more readily apparent than with the topic of sustainability and environmental stewardship, the focus of today’s brief. The environment is an inseparable component of any business. It forms the platform layer off which all goods and services are produced, and, more fundamentally, the resulting ecosystem services from which humans benefit create the foundations for our species’ survival and quality of life. Due to multiple ongoing trends, however, the environment is changing, as are the ways that consumers, investors and governments think about our relationship to the environment.

Accordingly, Part 2 of this series on the CPO’s Conundrum examines the outside-in drivers pushing sustainability and environmental stewardship higher on the procurement agenda. It also explores recent examples of how businesses are integrating these issues into their supply management strategies, while simultaneously addressing them in balance with traditional procurement objectives, such as category management, supply base alignment and demand shaping.

AI in Supplier Discovery: Tomorrow

interest rates

In Spend Matters’ last PRO article for the AI in Supplier Discovery series, we overviewed some situations where you can find it today, or at least functionality that looked like it was enabled by artificial intelligence (even if it was not), and set ourselves up for a discussion of true AI that is going to creep into supplier discovery platforms tomorrow.

However, when we say true AI, we mean the definition of AI as “assisted intelligence,” because there is no true artificial intelligence out there and probably won't be for a very long time (with some futurists conjecturing it will be 2060 before machines are as smart as the dumbest of us). Note that we don't even mean “augmented intelligence,” as even though the platforms will augment your knowledge, it will still be up to you to make the right, intelligent, decisions tomorrow. (And maybe the day after that, but that is a subject for our next article.)

In our last article, we reviewed the capabilities of the leading discovery platforms today, which mainly revolved around:

  • Smart search
  • Community intelligence

...and the intersection of both.

We discussed how the improvements in computing power and web-usability made it possible for platforms to implement better and more powerful search algorithms that actually made searches useful across wide supplier directories and networks; how community intelligence allowed an organization to quickly narrow potential supplier pools down to reasonable sizes; and how the intersection allowed for the definition of "like" searches that could not be done before now.

But as of today, those "like" searches are still pretty high level. And they are best at finding suppliers that provide finished products and services that can be well-defined and compared to other suppliers that provide similar finished products and services. In fact, most systems with "like" searches are for the identification of suppliers for indirect. Not direct. (And not services either.)

But that is going to change tomorrow. Tomorrow, supplier discovery systems are going to support:

  • deep capability match that uses bill of materials, production requirements and other deep factors to support supplier search for direct suppliers
  • resource capability match that can identify needed skill sets, knowledge and related attributes for services suppliers

And we'll finally have smart supplier search for all. But how will it happen? And what will it look like? Let's explore.