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.