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Leading practices in spares forecasting

The utilisation of an aircraft depends on the availability of the right spare parts, in the right place and at the right time. Because aircraft are enormously expensive capital assets, spare parts planning is one of the highest value activities in the aircraft value chain. However, the process of spare parts forecasting is far from optimised and, as a result, the MRO supply chain requires high levels of working capital to compensate for its shortcomings.

 

Continuous monitoring and Big Data hold the prospect for much better forecasting, but these techniques require the collection of relevant information. While OEMs, MROs and operators all have aspects of this data, not all the information has the same relevance. Different information has been shown to have a high, medium or low correlation to spare parts forecasting, with the data being inconsistently used within the industry. The data is often used qualitatively, providing a greater opportunity for quantitative analysis. Additionally, the data is not consistently shared across the value chain, which could greatly enhance its performance.

 

PwC’s Spares forecasting industry study included a survey and analysis of spares forecasting data and processes. Following are key findings and recommendations from the study:

 

Key findings

 

  • Successful spares planning and forecasting needs more effective collaboration and sharing of information (fleet data, engineering change orders, part reliability, service bulletins, and so on) across the supply chain.
  • The adoption of leading practices for spares planning bills of material (BOM), demand aggregation, but software enabling capabilities shows a low maturity level. This is impacting both internal and external key performance indicators across the service value chain.

 

Key recommendations

 

  • Focus on high-value data and special-factor drivers to reduce data-sharing cycle time, improve the quality of the information, and remove barriers, including:
  • Aggregate fleet data
  • Macroeconomic indices
  • Engineering data drivers
  • Consider the following leading practices to improve the overall maturity of the spares forecasting value chain:
  • Use monthly Sales, Inventory, and Operations Planning (SIOP) process to integrate the overall customer demand
  • Employ forecast spares planning BOMs to improve integration and management of changes over the planning time horizon
  • Use procurement release time fences (the boundaries between different periods in the planning horizon) to improve supply chain communication and reduce risk

 

Stakeholders across the value chain should take a fresh look at their spare parts forecasting processes. We recommend that they:

 

  • Identify the most relevant data and work collaboratively across the value chain to fill data gaps
  • Take a more quantitative approach to data analysis. Consider weighting the data based on its correlation to forecasting
  • Consider software tools to automate the process

 

Companies that take these steps can improve customer service while reducing working capital investment.

 

For more information on these issues as well as examples, please see PwC’s Spare forecasting paper.

 

 

This content is for general information purposes only, and should not be used as a substitute for consultation with professional advisors.

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Author

Scott Thompson
PricewaterhouseCoopers’ US Aerospace & Defense Leader

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