Use Cases > Service Parts Management

Service Parts Management

Service Parts Management Logo

Service parts management uses sensors and analytics to predict when spare parts will be needed and to deliver required parts as needed. Solutions aim to provide real-time visibility into the install base by collecting and analyzing IoT data directly from connected assets and integrating this real time data with historical usage data. Analytics can be used to model demand cycles, forecast intermittent demand, and optimize multi-echelon inventory. They can also improve downstream demand visibility from a distribution or service network. By connecting to the dealer network or service providers, parts manufacturers can identify sell-through demand down to the daily level. By predicting demand, providers can develop stocking plans based on service level objectives, budget limitations, and forecasts, to optimize stock inventory and customer service levels. These systems usually work together with supply chain management systems to ensure that the service parts supply chain is able to meet forecasted demand.

Business Viewpoint

Customer Satisfaction: Effective service parts management ensures that businesses have the right parts available when needed for maintenance, repairs, or replacements. This leads to higher customer satisfaction levels as downtime is minimized, and equipment uptime is maximized.

Inventory Optimization: Service parts management involves optimizing inventory levels to balance the cost of carrying inventory with the risk of stockouts. Businesses aim to maintain optimal inventory levels to meet service level agreements (SLAs) while minimizing carrying costs and obsolescence risks.

Stakeholder Viewpoint

Manufacturers: Manufacturers are responsible for producing and supplying service parts to support their products throughout their lifecycle. They need to ensure timely production, efficient distribution, and accurate forecasting to meet demand while minimizing costs.

Distributors: Distributors play a crucial role in the supply chain by storing and distributing service parts to service centers, dealerships, or end customers. They need to manage inventory effectively to ensure parts availability while optimizing warehouse space and logistics costs.

Technology Viewpoint

Inventory Optimization Software: Inventory optimization software utilizes algorithms and analytics to determine optimal inventory levels, safety stock levels, and reorder points based on demand variability, lead times, and service level targets.

Supply Chain Visibility Tools: Supply chain visibility tools provide real-time visibility into the movement of service parts across the supply chain, including inventory levels, shipments, and order status. This enables proactive management of supply chain disruptions and enhances responsiveness to customer demands.


Data Viewpoint

Demand Forecasting: Data analytics and forecasting techniques are used to predict future demand for service parts based on historical usage patterns, equipment lifecycle stages, and market trends. Accurate demand forecasting helps optimize inventory levels and reduce stockouts.

Inventory Tracking: Data from inventory management systems tracks the movement of service parts within the supply chain, from manufacturing plants to distribution centers to service locations. Real-time visibility into inventory levels and locations enables timely replenishment and efficient allocation of parts.

Deployment Challenges

Inventory Management Systems: Businesses deploy inventory management systems, often integrated with enterprise resource planning (ERP) software, to track service parts inventory levels, monitor demand, and automate replenishment processes.

Supplier Collaboration: Effective service parts management requires collaboration with suppliers to ensure timely delivery of parts, manage lead times, and mitigate supply chain risks. Supplier relationship management (SRM) tools and processes facilitate communication and collaboration with suppliers.

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