Inventory and service level optimization in the spare parts area

Ensuring a high level of delivery and service for spare parts is one of the key success factors in modern service logistics. Today's customers expect even rare components to be available almost immediately—especially in industrial applications where machine downtime incurs high costs. At the same time, companies are under considerable economic pressure to reduce their inventories, capital commitment, and storage costs. The balance between service level and inventory therefore represents a classic conflict of objectives that requires data-based, systematic, and dynamic control of scheduling.

The key to resolving this conflict lies in the development of a rule-based inventory management system based on sound data analysis and simulation. The starting point is a comprehensive evaluation of the logistical behavior of all item numbers—i.e., demand trends, replenishment times, delivery reliability, safety stocks, and consumption frequencies. This analysis creates transparency regarding demand structures, seasonal patterns, outliers, and life cycles.

Based on this data, items are systematically classified according to a defined scheme, usually based on combined methods such as ABC/XYZ analyses, FSN analyses (fast, slow, non-moving), or service criticality assessments. While ABC analysis determines the economic importance of an item, XYZ analysis differentiates the predictability of demand. Criteria such as replacement value, procurement risk, or customer segment affiliation can also be included. The goal is precise segmentation that enables a differentiated disposition strategy—for example, consumption-oriented for A/X items, demand-oriented for B/Y items, or order-related for C/Z items.

The ebp's own analysis and simulation tool is used to design and optimize the scheduling parameters. This tool allows for the variation and evaluation of different scenarios with regard to delivery readiness, safety stocks, replenishment times, and capital commitment. The simulation reveals complex interactions—for example, how a 10% reduction in replenishment time affects the necessary safety stock or service level. This enables companies to make data-based decisions about which service levels are realistically achievable at what costs.

A particular added value lies in the ability to model service level indicators (e.g., fill level, cycle service level, or delivery position service level). These indicators make the efficiency boundary between inventory and service measurable and form the basis for fact-based management decisions. This creates a transparent set of rules that systematizes scheduling while leaving room for operational flexibility.

However, the successful implementation of such a system requires more than just technical analysis skills. The qualification of the planners, who must understand the methods and apply them in everyday life, is crucial. Training courses and practical training ensure an understanding of key figures, parameter relationships, and the dynamic adaptation of planning logistics. Once a system has been set up, it will only remain effective if scheduling parameters are continuously monitored, validated, and adjusted—for example, in the event of changing demand patterns, new products, or changes in supplier structures.

These processes are increasingly supported by digital solutions: modern ERP or APS (Advanced Planning & Scheduling) systems enable automatic readjustment of parameters, forecasts based on machine learning, and dashboards for real-time monitoring of service levels and inventory. This transforms a static set of rules into a dynamic, learning system.

Our project experience shows that the combination of data-based simulation, methodical classification, and targeted competence development can significantly increase service levels—often while reducing inventory by 15 to 30%. The lasting effect is not achieved through individual measures, but through a holistic understanding of the system that integrates strategy, data, systems, and people.

This makes inventory and service level optimization in the spare parts sector a decisive lever on the path to operational excellence in service logistics—with measurable advantages in availability, cost-effectiveness, and customer satisfaction.