Service Parts Allocation Using Machine-Specific Usage Data
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Solution Overview
Problem
Conventional service parts planning systems lack visibility into service parts inventory, particularly in transit and at repair facilities, and rely on external forecasts that do not account for individual part histories, leading to inefficient allocation and potential obsolescence.
Innovation Solution
A system and method for service parts planning that uses machine-specific information and unique identifiers to optimize the allocation of service parts across a network, enhancing inventory visibility and forecasting by integrating data from sensors and advanced shipping notifications to strategically position parts based on actual usage and failure indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional service parts planning systems use external information for demand forecasting, then the forecasting process is simplified, but the accuracy of demand forecasting deteriorates because individual part histories are not considered
Solution Approach 1:
The system implements feedback by continuously monitoring actual service part usage, failure events, and inventory movements, then using this feedback to refine and update demand forecasts. The system learns from historical data and adjusts predictions based on actual performance, improving accuracy over time while maintaining automated operation.
Solution Approach 2:
The system performs self-service by automatically collecting data from sensors, shipping notifications, and inventory systems, then autonomously generating demand forecasts without requiring manual external inputs. The system serves itself by maintaining and updating its own forecasting models using its accumulated historical data.
2Reliability
If service parts are stocked in ample supply to maintain system availability, then system availability is improved, but the capital invested in service parts increases and parts may become obsolete
Solution Approach 1:
The system performs preliminary action by proactively identifying service parts that are likely to be needed based on predicted failures or maintenance schedules. It positions these parts at appropriate locations before they are actually required, ensuring system availability while avoiding the need to stock excessive quantities of all possible parts.
Solution Approach 2:
The system dynamically adjusts inventory parameters such as stock levels, reorder points, and safety stock quantities based on changing demand patterns, part criticality, and lead times. This allows optimization of inventory levels to maintain availability while minimizing capital tied up in stock.
3Device complexity
If conventional service parts planning systems lack visibility into inventory in transit and at repair facilities, then the system complexity is reduced, but the inventory position accuracy deteriorates
Solution Approach 1:
The system implements a universal tracking mechanism that handles multiple functions: monitoring parts in storage, in transit, at repair facilities, and in use. This unified approach provides comprehensive inventory visibility across the entire supply chain without requiring separate complex systems for each location or state.
Solution Approach 2:
The system uses intermediaries such as sensors, RFID tags, barcodes, and communication protocols to track and report inventory position automatically. These intermediaries bridge the gap between physical inventory locations and the central planning system, providing real-time visibility without direct human intervention at each location.
Data Source
AI summary
Embodiments of the invention are generally directed to a system and method for service parts planning in a network having one or more service parts. For at least a subset of the one or more service parts, a calculation is performed to determine a location within the network at which allocating the service part provides the greatest gain in system availability per item cost. In an embodiment, the calculations are based, at least in part on machine-specific information. In one embodiment, inventory position is determined based, at least in part, on a unique identifier associated with a service part.