Dynamic Pricing for a leading Aircraft Parts Distributor in the USA

Spare Parts
We helped one of the biggest Aircraft parts distributors in the US with predictive pricing analytics. This improved sales volume as well as the profitability of the business.

Business Objective

The aerospace parts distribution is a highly fragmented industry, with each part having substitutes and applications across a variety of products and manufacturers. Our client is a US-based leader in Aircraft Parts distribution which maintains an inventory of 40,000+ precision parts. Manual quotation resulted in suboptimal prices, leading to either loss of opportunities or loss of profit.. our client in this scenario wanted to:

  • Implement a dynamic pricing system to help the quotation process.
  • Monitor the pricing performance with a dash-boarding solution.

Challenge

  1. There was a mismatch between quoted and actual sales volume, sometimes up to a factor of 8x. This made  it difficult to forecast the price from quotation data.
  2. Each part had many substitute parts at different prices which makes it difficult to devise an effective pricing strategy.
  3. Customers were inconsistent in their buying behavior, which made it difficult to uncover and explain sales trends.

Solution Methodology

We developed a dynamic price prediction model using statistical modeling. 

  1. We first put forward a new product ontology which allowed the grouping of alternate parts.
  2. We iterated over the  pricing model until the in- and output met client specifications. In particular, the automatic pricing was set to meet margin and revenue targets.
  3. The solution was finally deployed on a production API that returned a price for a given product and volume. 
  4. Business performance such as revenue and profit was monitored weekly on a data dashboard.

Impact

  1. Within three months of deployment, the model achieved an average 7% improvement in weekly sales volume.
  2. The automated pricing model generated an increase of 11% in gross sales (GMV)  within three months.

Impact in Numbers

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