Category Archives: Sourcing Innovation

How Do You Identify The Day After Tomorrow’s Supply Chain Paupers?

Well, assuming the day after tomorrow comes and they are still around the day after tomorrow, they will be easy to spot. Not only will they still be trying to use Excel, but they will still be using Excel and will only recently have started exchanging documents using XML, using last decade’s e-Procurement technology.

They will not have advanced to modern e-Procurement applications, yet alone modern sourcing or supply chain visibility solutions. They will be in the process of simply moving from paper to e-Paper, trying to still conduct RFIs through e-mail with Excel (and just uploading the results to the first generation decade(s)-old e-Procurement solution), and generally trying to keep their outdated procurement processes in tact.

However, as we now know, first generation procurement and sourcing, focused primarily on e-document exchange, simple RFXs, the odd auction, and basic reporting is not enough. You need modern e-catalog management for procurement spot buys, analytics for opportunity identification, optimization for at least TCO management (if not TVM), and SRM for supplier information, relationship, and performance management.

But this is not enough. These day’s, there’s never enough time to sift through all the data to identify the opportunities, never enough time to collect enough market data to qualify even the ones you have identified, and certainly never enough time to construct category specific models on even a fraction of those to determine if they opportunities will be realized with an appropriate sourcing event — which can take years of experience to properly identify.

You need a next generation solution that can automatically collect, maintain over time, and trend market pricing data; run all your data through multiple types of automatic analysis and compare your spend against historical spend and market data (and look for variances); pull out the categories with opportunities; run trending algorithms to project your demand against expected contract prices based upon projected market demand, supply / demand (im)balance, and economic factors; calculate the potential savings if nothing was done; use historical data and automated reasoning (enriched with context) to (probabilistically) identify the best sourcing or procurement strategy; and then use appropriate workflow automation to automate as much of the event as possible (and if it is a spot-buy under a threshold, automatically procure from a catalog, an approved supplier under contract, or a three-bids-and-a-buy RFQ against approved suppliers).

In modern terms, the next generation solutions will be Cognitive Sourcing or Cognitive Procurement solutions. While they are not true artificial intelligence, with enough data and great models, you don’t need true AI to automate acquisitions where there is no strategic value and no significant value to investing human time. Good examples are office suppliers, janitorial services, and sometimes even laptops. Yes, replacing laptops across a large office can be in the millions, but laptops against a standard config are commodity. Just do an automated auction [with ceilings and floors] against a set of approved suppliers and let the most aggressive supplier win.

Introducing LevaData. Possibly the first Cognitive Sourcing Solution for Direct Procurement.

Who is LevaData? LevaData is a new player in the new optimization-backed direct material prescriptive analytics space, and, to be honest, probably the only player in the optimization-backed direct material prescriptive analytics space. While Jaggaer has ASO and Pool4Tool, it’s direct material sourcing is optimization backed and while it has VMI, it does not have advanced prescriptive analytics for selecting vendors who will ultimately manage that inventory.

LevaData was formed back in 2014 to close the gaps that the founders saw in each of the other sourcing and supply management platforms that they have been a part of over the last two decades. They saw the need for a platform that provided visibility, analytics, insight, direction, optimization, and assistant — and that is what they sent out to do.

So what is the LevaData platform? It is sourcing platform for direct materials that integrates RFX, analytics, optimization, (should) cost modelling, and prescriptive advice into a cohesive whole that helps a buyer buy better when they use and which, to date, has reduced costs (considerably) for every single client.

For example, the first year realized savings for a 5B server and network company who deployed the LevaData platform was 24M; for a 2.4B consumer electronics company, it was 18M; and for a 0.6B network customer, it was 8M. To date, they’ve delivered over 100M of savings across 50B of spend to their customer base, and they are just getting started. This is due to the combination of efficiency, responsiveness, and savings their platform generates. Specifically, about 60% of the value is direct material cost reduction and incremental savings, 30% is responsiveness and being able to take advantage of market conditions in real time, and 10% is improved operational efficiency.

The platform was built by supply chain pros for supply chain buyers. It comes with a suite of f analytics reports, but unlike the majority of analytics platforms, the reports are fine tuned to bill of materials, component, and commodity intelligence. The reports can provide deep insight to not only costs by product, but costs by component and/or raw material and roll up and down bill of materials and raw materials to create insights that go beyond simple product or supplier reports. Moreover, on top of these reports, the platform can create costs forecasts and amortization schedules, track rebates owed, and calculate KPIs.

In order to provide the buyer with market intelligence, the application imports data from multiple market fees, creates benchmarks, compares those benchmarks to internal market data, automatically creates competitive reports, and calculates the foundation costs for should cost models.

And it makes all the relevant data available within the RFX. When a user selects an RFX, it can identify suppliers, identify current market costs, use forecasts and anonymized community intelligence to calculate target costs, and then use optimization to determine what the award split would be, subject to business constraints, and identify the suppliers to negotiate with, the volumes to offer, and the target costs to strive for.

It’s a first of its kind application, and while some components are still basic (as there is no lane or logistics support in the optimization model), missing (as there is no ad-hoc report builder, or incomplete (such as collaboration support between stakeholders or a strong supplier portal for collaboration), it appears to meet the minimal requirements we laid out yesterday and could just be the first real cognitive sourcing application on the market in the direct material space.

Cognitive is the New Buzzword. But what does it mean?

It seems that everyone is talking about Procurement these days. A Google search for cognitive procurement returns about 650,000 results that include news sites, analyst firms, and vendors ranging in size from Old St. Labs to SAP Ariba to IBM.

Definitions are varied as well. Quora defines cognitive procurement as the application of self-learning systems that use data mining, pattern recognition and natural language process (NLP) to mimic the human brain to around the processes of acquiring, buying goods, services or works from an external source. IBM’s Vice President of Global Procurement defines cognitive procurement as the use of systems and approaches that are able to learn behaviour, manage structured and unstructured data, and unlock new insights to enable optimized outcomes. Vodafone defines cognitive procurement as augmented intelligence capabilities that allow a category manager to make faster and smarter data driven decisions that deliver competitive advantage.

But what does this all mean? First of all, the only commonality is using systems to do a task better. Which systems? Which tasks? Who gets the benefits? And what precisely are the benefits?

To figure this out, we have to go back and define what makes for better Procurement. The first step is good Sourcing. What are the keys to good Sourcing?

There are a number of keys to good Sourcing. Some of the most important include:

Visibility. Who are your potential suppliers? What do they provide? Where are they? What do you know about quality, reliability, delivery, etc? What are the risk factors with dealing with them? What data can you get on finances and sustainability? You need good information.

Analytics. Once you get the information, you need to make sense of it. Roll up component and material costs across bill of materials. Amalgamate risk ratings into meaningful scorecards. Aggregate demand across categories. Determine what you need, when, in what quantities, and how much it should cost before you start a negotiation.

Modelling. The ability to define detailed should cost models based on components or materials, production costs that include energy and labour and overhead, and other relevant cost factors. To define how those costs change with market data or production volumes. And so on.

Optimization. Once you get the data, you need to figure out the baseline costs and what the optimal awards are assuming nothing changes. Then how those change as costs change as bids change. Also, what are the optimal logistics strategies and costs. How does logistics impact the award decision? How should the logistics supply chain be designed?

Negotiation Support. At some point, the analysis needs to turn to negotiation, because the goal of sourcing is to acquire the products and services the organization needs to support its operations and satisfy its customers. All of this capability needs to be brought to bear in a cohesive, assistive, fashion that can help a buyer make the right decision.

That’s what cognitive procurement is — presenting a user with the information they need when they need it to make the right decision. Not automated buying. Not artificial intelligence which doesn’t exist. Not trying to mimic the human brain, as we don’t even fully understand how that works now.

So, does any application meet these requirements?

There are 4 Modes of Innovation, But Only Two Types!

A recent article over on HBR.org on the 4 types of innovation and they problems they solve didn’t really discuss the types of innovation, but rather the modes. The author, who broke innovation down into the age-old 2*2 matrix, with domain definition on one axis and problem definition on the other, indicated that their was basic research — typically carried out by or with academia, breakthrough innovation — typically accomplished by skunk work projects, sustaining innovation — typically done by R&D labs, and disruptive innovation — that often comes out of VC-funded innovation labs.

As you can say, these are not really “types” but methods of innovation which can each lead to innovations that might be classified as basic, sustaining, breakthrough, or even disruptive innovations (so the names are quite confusing), and this leaves the question, what are the real types of innovation and how does innovation happen. (An academic might come up with a disruptive way to create new communications technology and the best-funded VC lab might, after years of research, just come up with a way to make a fabrication process more efficient, saving 20% of time and 10% of cost, and not discover a single revolution.)

So how is innovation accomplished? These days, it’s fundamentally accomplished in one of two ways — either using the tried and true method of good old fashioned human ingenuity or the new method of deep learning that can discover patterns, formulas, or correlations that humans can miss. But is this the kind of innovation we need? Or even want?

As per our last article where we asked if the end of the digital west was in sight, while these deep learning systems can, with enough data, make predictions that are much more accurate than the best human experts, the fact that they cannot explain their reasoning is very disturbing. Very disturbing indeed. Do we really want to trust them with a new drug formula that, while having the potential to save thousands, also has the potential to kill hundreds, with no knowledge of which individuals are at risk of instant death? the doctor hopes not!

While it’s okay to use these systems to identify the most likely directions of success, it’s not okay to use these systems to blindly choose those directions without independent verification and confirmation with rationale, deterministic explanations. In other words, while we should use every tool at our disposal, we should never replace human intelligence and ingenuity with dumb systems. Because, while there are two types of innovation in use these days, there’s only one real type of innovation — human innovation. the doctor hopes that we never forget it and return to the glory days where all innovation was human innovation.

The UX One Should Expect from Best-in-Class Spend Analysis … Part V

In this post we wrap up our deep dive into spend analysis and what is required for a great user experience. We take our vertical torpedo as far as it can go and wrap the series up with insights beyond what you’re likely to find anywhere else. We’ve described necessary capabilities that go well beyond the capabilities of many of the vendors on the market, and more will fall by the wayside today. But that’s okay. The best will get up, brush off the dirt, and keep moving forward. (And the rest will be eaten by the vultures.)

And forward momentum is absolutely necessary. One of the keys to Procurement’s survival (unless it really wants to meet it’s end in the Procurement Wasteland we described in bitter detail last week) is an ability to continually identify value in excess of 10% year-over-year. Regardless of what eventually comes to pass, the individuals who are capable of always identifying value will survive in the organizations of the future.

But if this level of value is to be identified, buyers are going to need powerful, usable, analytics — much more powerful and usable then what the average buyer has today. Much more.

As per our series to date, this requires over a dozen key useablity features, many of which are not found in your average first, and even second generation, “reporting” and “business intelligence” analytics tool. In our brief overview series to date here on SI (on The UX One Should Expect from Best-in-Class Spend Analysis … Part I, Part II, Part III, and Part IV) we’ve covered four key features:

  • real, true dynamic dashboards,
  • simultaneous support for multiple cubes,
  • real-time idiot-proof data categorization, and
  • descriptive, predictive, and prescriptive analytics

And deep details on each were provided in the linked posts. But even prescriptive analytics, which, for many vendors, is really pushing the envelope, is not enough. Great solutions really push the envelope. For example, the most advanced solutions will also offer permissive analytics. As the doctor has recently explained in his two-part series (Are We About to Enter the Age of Permissive Analytics and When Selecting Your Prescriptive, and Future, Permissive, Analytics System), a great spend analysis system goes beyond prescriptive and uses AR and a rules-engine to enable a permissive system that will not only prescribe opportunities to find value but initiate action on those opportunities.

For example, if the opportunity is a tail-spend opportunity that could best be captured by a spot-auction, approved products that meet the bill, and approved suppliers that can automatically be invited to an auction to provide them, the system will automatically set up the auction and invite the suppliers, and if the total spend is within an acceptable amount, automatically offer an award (subject to pre-defined standard terms and conditions).

And that’s just the tip of the iceberg. For more insight onto just how much a permissive analytics platform can offer, check out the doctor and the prophet‘s fifth and final instalment on “What To Expect from Best-in-Class Spend Analysis Technology and User Design” (Part V) over on Spend Matters Pro (membership required). It’s worth it. And maybe, just maybe, when you identify, and adopt, the right solution, you won’t end up wandering the Procurement Wasteland.