Category Archives: Direct Sourcing

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?