MedTech case study

Intelligent pricing predictability for 13 quarters in advance.

Challenge

A leading manufacturer in the Medical Device industry, with a global footprint, wants to gain visibility into future price trends across their catalog. Ideally, they would like a degree of pricing predictability 13 quarters into the future, to help them make strategic investments / divestments across their enterprise. This is a challenge on their side because they have around 100,000 SKUs across more than 60 countries. An additional challenge occurs when they launch new products with no history, or they introduce an existing product in a new country.

To solve this problem, we need to produce an accurate price forecast with the following characteristics:

  1. Accurately provide a forecast for the next X quarters (13 in case of our client)
  2. The solution needs to be a “white box” meaning that we can review the context of the prediction and understand why the solution proposed the price
  3. The solution should be able to “transfer” knowledge form different SKUs so that we can propose a price even if there is no history of the product or if they want to introduce the product in a new country/market
Problem
Solution

Solution

Predictive Layer gathers the following data points:

  • History of the sales of the company
  • CRM and business expert data

With this data, Genius Forecaster was able to begin the training process following the 3 characteristics mentioned above: accuracy and flexibility in the horizon forecast, explainability, knowledge transfer.

Once trained, the solution was ready to be tested by the client in parallel with benchmarking against their existing forecast (in other words: validate the PL forecast against actual forecast). Having successfully passed these tests, the Predictive Layer solution was deployed into production for the complete business. It currently covers the full business scope with global accuracy on the revenue of 98 %  Genius Forecaster benchmarks outlies a gain of 4 – 6% in precision and 75% of workload reduction for regular forecast updates.

Trusted by leading global brands

Shneider Electric
Arcelor Mittal
BRS
General Electric
ABB