The cost of not having the right spare part at the right time is more than the price of a delayed repair. It’s lost uptime, disrupted customer commitments, and rising operational costs. Service parts are the backbone of maintenance and after-sales support, yet their management remains one of the most complex and undervalued elements of the supply chain.
As global supply networks grow more intricate and customer expectations evolve toward near-zero downtime, organisations are rethinking how they manage spare parts. Optimising Service Parts Management (SPM) is now a strategic priority for building resilience, responsiveness, and long-term service profitability.
This guide explores emerging service parts management best practices and how AI-powered spare parts logistics and predictive inventory optimisation are redefining operational efficiency and service excellence.
from static planning to strategic service parts management
Traditional SPM models were built for stability, not volatility. They relied on static forecasts and manual decisions to determine stocking levels and replenishment cycles. But in today’s world of fluctuating demand, extended lead times, and unpredictable supply risks, those models are no longer sufficient.
Forward-looking enterprises treat service parts management as a strategic capability, integrating planning, logistics, and field service operations into a unified, data-driven framework. This transformation allows better alignment between maintenance needs and logistics execution, enabling faster response times and leaner inventories.
A well-optimised SPM framework delivers measurable business outcomes: higher service-level compliance, lower working capital, and reduced total cost of ownership.
service parts management: best practices that drive value
Modernising service parts operations begins with a few foundational principles:
- Build end-to-end visibility: Integrate ERP, warehouse, and maintenance systems for a single source of truth to track inventory, forecast needs, and prevent bottlenecks.
- Prioritise by criticality: Use ABC/XYZ analysis to allocate safety stocks based on part importance and demand variability.
- Align maintenance and logistics: Coordinate preventive maintenance with replenishment cycles to reduce delays.
- Eliminate obsolete inventory: Audit and remove redundant SKUs to free space and capital.
- Enable collaboration: Work with suppliers and partners for faster replenishment and shared forecasting.
These service parts management best practices set the stage for intelligent automation and predictive analytics.
harnessing AI-powered spare parts logistics
The next evolution of SPM is happening through AI-powered spare parts logistics. Artificial intelligence and machine learning are transforming how organisations procure and position service parts, moving from reactive to proactive operations.
AI-driven solutions bring intelligence to every stage of the logistics lifecycle:
- Smarter demand forecasting: Machine learning models analyse historical usage, real-time sensor data, and maintenance schedules to predict which parts will be needed, and when.
- Automated replenishment: AI dynamically adjusts reorder points and safety stocks based on usage patterns, seasonality, and supplier performance.
- Optimised distribution: Intelligent routing engines recommend the most efficient warehouse-to-site delivery paths, minimising response times and logistics costs.
- Obsolescence management: Predictive analytics identify slow-moving or at-risk parts early, helping planners rationalise inventory before obsolescence drives losses.
By leveraging AI in spare parts logistics, organisations are realising tangible benefits, including up to 60% reduction in working capital spend, 30% reduction in excess inventory, and greater part availability that minimises equipment downtime.
predictive inventory optimisation: from forecasting to foresight
If AI brings intelligence, predictive inventory optimisation brings foresight. Rather than relying solely on historical data, predictive models anticipate future parts demand by integrating asset health, maintenance triggers, and external factors such as lead times or supplier disruptions.
For instance, a predictive model can assess the likelihood of component failure across a fleet of equipment and pre-position parts in strategic locations. This proactive service approach reduces emergency shipments and improves uptime, lowering operational costs.
Key enablers of predictive inventory optimisation include:
- Integration with IoT and asset health data for early fault detection and demand prediction.
- Dynamic safety-stock models that adjust based on real-time risk and consumption variability.
- Cross-site optimisation ensuring parts are distributed efficiently across warehouses and field locations.
Organisations adopting predictive approaches are achieving dual benefits:
- Reduced working capital
- Improved service reliability
Both are critical in competitive after-sales environments.
getting started: building a future-ready SPM framework
Optimising SPM requires a blend of technology, process excellence, and cultural alignment. A structured roadmap can accelerate results:
- Assess current maturity: Evaluate inventory accuracy, process standardisation, and data quality across the SPM lifecycle.
- Identify high-impact use cases: Start with a limited set of high-value parts or service lines to validate AI and predictive models.
- Strengthen data foundations: Ensure clean, structured, and accessible data from ERP, logistics, and asset systems.
- Integrate seamlessly: Connect planning, maintenance, and logistics functions through digital workflows.
- Scale continuously: Once proven, extend predictive optimisation across global networks, leveraging continuous learning for improvement.
A strong governance model supported by the right metrics, such as fill rate, mean time to repair (MTTR), and cost-to-serve, ensures sustained performance.
how can Infosys BPM turn service parts into a strategic advantage?
Infosys BPM offers end-to-end supply-chain optimisation services featuring machine-learning forecasting that boosts accuracy by 15-20% and inventory-turn improvements of 10-15%. Optimise your service-parts ecosystem by connecting asset-health data, safety stocks, and logistics. Reduce excess inventory, boost part availability, and elevate after-sales performance. Start transforming your field operations today.
