Challenge

Rebalancing aerospace spares inventory to new demand patterns

Proponent is the largest independent aircraft parts distributor in the world. Just like many other businesses, the aerospace industry has endured a substantial blow by the COVID-19 virus. Due to a dramatic decrease in flying hours, Proponent faced a significant reduction in spare part demand with large variations per product line. In cooperation with Gordian, a segmented and tailored approach was developed. The model provides a better fit for future demand, while heavily optimising investments of stock in combination with a minimal loss of service level.

Result

Supporting the journey to the new normal

The model developed is not a static model, supporting a once-only intervention. The COVID-19 change ratios are dynamic, in turn leading to a dynamic classification. This means that we can provide accurate stocking parameters now but also in our journey to the new normal – whatever that may be. Using that dynamic character, cashflow will be limited throughout the complete journey while still providing the best possible service to all aerospace clients.

With the differentiated approach as advised by Gordian, we met both our goals, with only limited amendments to our Inventory controls models.

René Lagendijk

Forecasting & Inventory Manager

Approach

When demand drops down that suddenly, even the best possible forecast methods are not effective anymore. The methods will always react too late to the demand change, resulting in overstocking and spending precious budget on unnecessary stock. To respond to the demand disruption, we introduced a COVID-change-ratio to ‘impose’ this disruption on the forecasting models.

Based on the COVID-change-ratio, the parts were roughly classified in 4 groups. Note that on a more detailed level, we make an additional classification for longer and shorter lead times. Every group has a different approach to react on the demand disruption:

  • No significant negative demand change: slight reduction of service level to lower purchase requisitions.
  • High demand change, but not to zero: historical demand adjustments to improve forecasting.
  • Demand disappeared: no replenishment & trigger when demand pops up.
  • Slow movers: minimising order size.

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Rogier Zoun