Procurement Glossary
Inventory management: Strategic control of stock levels in Procurement
November 19, 2025
Inventory management comprises the systematic planning, management and control of stock levels to optimize availability at minimum cost. It forms the core of an efficient procurement strategy and has a significant impact on a company's liquidity and competitiveness. Find out below what inventory management is, which methods are used and how you can successfully minimize risks.
Key Facts
- Inventory management reduces capital commitment by an average of 15-25% while maintaining availability
- Modern systems use AI-based forecasts for more precise order quantities and times
- ABC-XYZ analysis enables differentiated control strategies depending on item value and consumption behavior
- Just-in-time concepts can reduce storage costs by up to 50%, but increase delivery risks
- Digital dashboards provide real-time transparency on inventory ranges and service levels
Contents
Definition: Inventory management
Inventory management refers to the systematic planning, control and monitoring of material stocks to ensure optimum availability at minimum cost.
Core elements of inventory management
The main components include inventory analysis, scheduling and continuous optimization. Central tasks are
- Determination of optimal order quantities and times
- Definition of safety stocks for risk hedging
- Monitoring of storage ranges and turnover rates
- Control of material planning according to consumption patterns
Inventory management vs. warehouse management
While warehouse management primarily controls operational processes such as storage and retrieval, inventory management focuses on strategic decisions for inventory optimization. It integrates procurement planning, consumption forecasts and cost considerations in a holistic approach.
Importance in Procurement
Effective inventory management enables purchasing organizations to strike a balance between ensuring availability and minimizing capital commitment. It supports strategic supplier partnerships and helps to minimize risk in the supply chain.
Methods and procedures
Modern inventory management methods combine proven control concepts with data-driven approaches for precise demand planning.
ABC-XYZ classification
The ABC-XYZ analysis categorizes articles according to value and consumption regularity. A-items with a high value receive intensive control, while C-items are planned in a simplified manner. The classification cycle ensures up-to-date categorization.
Order point and min-max control
The reorder point procedure triggers automatic orders when stock levels fall below defined reorder points. Min-max control defines ranges for optimum stock levels, taking delivery times and fluctuations in consumption into account.
Prognostic methods
Modern systems use statistical models and machine learning for precise demand forecasts. Forecast errors are continuously analyzed and algorithms are adapted accordingly to improve the forecast quality.
Tacto Intelligence
Combines deep procurement knowledge with the most powerful AI agents for strong Procurement.
Key figures for controlling inventory management
Meaningful key figures enable the objective evaluation of inventory management performance and identify optimization potential.
Inventory range and turnover rate
The stock range shows how long current stocks will last at normal consumption. The average stock level in relation to annual consumption gives the inventory turnover rate as an efficiency indicator.
Service level key figures
The delivery service level measures the availability for customer inquiries. Service level targets per Category enable differentiated management according to criticality. Stockout frequencies and durations quantify supply risks.
Cost-oriented metrics
Inventory cost rates, capital commitment costs and obsolescence rates evaluate financial performance. Inventory indicators such as inventory turnover and range variance show optimization potential. The ratio of inventory to procurement costs indicates the efficiency of the inventory strategy.
Risk factors and controls in inventory management
Inadequate inventory management can lead to considerable financial losses and supply bottlenecks, which can be avoided through systematic risk identification.
Excess stock and capital commitment
Excessive safety stocks tie up capital unnecessarily and increase storage costs. Slow-mover analyses identify critical items at an early stage. Regular stock range checks and planned/actual comparisons uncover deviations.
Delivery bottlenecks and service level risks
Low stock levels jeopardize the ability to deliver and can lead to production downtime. Delivery service level monitoring and backorder analyses monitor critical developments. Dynamic safety stock calculations adapt to changing risk situations.
Forecast inaccuracies
Incorrect demand forecasts lead to suboptimal ordering decisions. Continuous forecast quality measurements and parameter adjustments minimize systematic deviations. MRP parameter maintenance ensures up-to-date control logic.
.avif)
Practical example
An automotive supplier optimizes its inventory management for 15,000 C-parts by implementing an ABC-XYZ analysis. After classification, A-items are planned on a weekly basis, while C-items are switched to Kanban control. An inventory health dashboard monitors critical key figures in real time.
- Reduction in capital commitment by 22% with the same level of service
- Automation of 80% of disposition decisions for C-items
- Improvement of forecast accuracy by 15% through AI integration
Current developments and effects
Digitalization and artificial intelligence are revolutionizing inventory management through more precise forecasts and automated decision-making processes.
AI-supported inventory optimization
Artificial intelligence analyzes complex consumption patterns and external influencing factors for more precise forecasts. Machine learning algorithms continuously optimize safety stocks and order parameters based on historical data and market developments.
Real-time monitoring and dashboards
Modern inventory health dashboards offer real-time transparency about stock situations. Predictive analytics provides early warning of critical developments and enables proactive control measures to avoid stock shortages.
Sustainability integration
Environmental aspects are becoming increasingly important in inventory decisions. Companies are integrating CO2 footprints from transport and warehousing into their optimization models. Obsolete stocks are minimized through improved forecasting and lifecycle management.
Conclusion
Inventory management forms the strategic foundation for efficient procurement organizations and enables the optimal balance between ensuring availability and minimizing costs. Modern AI-supported approaches are revolutionizing traditional control concepts through more precise forecasts and automated decision-making processes. Companies that implement systematic inventory management significantly reduce capital commitment while improving their ability to deliver. The continuous development of digital tools and analysis methods will further increase the strategic importance of inventory management.
FAQ
What is the difference between inventory management and warehouse management?
Inventory management focuses on strategic decisions to optimize stock levels, order quantities and service levels. Warehouse management, on the other hand, controls operational processes such as storage and retrieval, picking and physical inventory management. Both areas complement each other for holistic inventory management.
How do you determine optimal safety stocks?
Optimal safety stocks result from the balance between delivery service level targets and capital commitment costs. Statistical methods take into account fluctuations in consumption, delivery time scatter and desired service levels. Modern systems use dynamic calculation methods for continuous adjustment.
What role does AI play in modern inventory management?
Artificial intelligence improves forecasting accuracy by analyzing complex consumption patterns and external influencing factors. Machine learning continuously optimizes scheduling parameters and identifies anomalies at an early stage. Predictive analytics enables proactive control measures to avoid bottlenecks or excess stock.
How do you measure the success of inventory management measures?
Success is measured by key figures such as inventory coverage, turnover rate and delivery service level. A reduction in capital commitment with a constant or improved service level indicates successful optimization. Regular benchmarks and trend analyses reveal potential for improvement.
.png)
.png)
.png)
