The challenge
Across many supply chains, having the right parts available at the right time remains a major challenge. This applies across industries, from beverage can manufacturing to aircraft and drone maintenance. In the latter case especially, predicting demand for spare parts is highly complex.
Maintenance needs depend on a range of factors, including usage intensity, weather conditions, operational load and even geopolitical developments. For newer machines or systems, there is often limited historical data available, making traditional forecasting methods less effective.
At the same time, suppliers typically rely on their own models to estimate component lifespans. These models do not always reflect how equipment is actually used in practice. For example, when aircraft operate intensively in extreme conditions, wear and tear can occur faster than expected. This has a direct impact on safety, availability and operational efficiency.
Organisations therefore need flexible and accurate forecasting models that take both technical data and real-world operating conditions into account.
The approach
Artificial Intelligence enables a more proactive and data-driven approach to spare parts management. By combining data from multiple sources, organisations gain deeper insight into wear patterns and maintenance needs.
This includes analysing sensor data, maintenance records and historical usage to identify patterns in degradation and failure. As a result, maintenance can be planned more effectively and spare parts can be made available at the right time.
Rather than relying on a single model, KPMG recommends combining multiple AI techniques. Logistic regression can be used to assess the likelihood of a component failing within a given timeframe. Poisson models help estimate how frequently failures may occur, while survival analysis provides insight into expected component lifespans.
Combining these models creates a more complete picture of when parts are likely to require replacement and which factors influence their longevity.
Data quality is a critical factor throughout. While many organisations hold valuable data, it is often fragmented or incomplete. By establishing clear data governance and processes, and using AI to identify or enrich missing information, the quality of underlying datasets can be significantly improved.
The result
With AI, organisations can significantly improve both inventory management and maintenance planning.
More accurate predictions ensure that spare parts are available when needed, while reducing excess inventory and waste. At the same time, predictive maintenance enables organisations to act proactively, extending the usable life of equipment.
The result is a more reliable supply chain, with less downtime, more efficient inventory management and improved operational performance.
The team that made the difference
A multidisciplinary team from KPMG Netherlands supported organisations in developing AI-driven solutions for spare parts management. By combining expertise in AI and data with knowledge of supply chain processes and Digital Process Excellence, they created an approach that optimises both predictive maintenance and inventory management.
