1. Introduction: The Academic Question and its Relevance for Modern Manufacturing in 2026
Inventory management is a fundamental activity in industrial manufacturing. In a rapidly evolving production environment characterized by demand volatility, supply chain disruption, and pressure for operational efficiency, optimizing inventory levels becomes a critical challenge. Excess inventory ties up capital and generates storage costs, while a shortage can cause production stops and lost sales. The central academic question addresses how simulation models can provide a predictive and analytical tool to optimize inventory management in complex supply chains. In 2026, this relevance is accentuated by the need for agility, resilience and capacity to respond to unforeseen scenarios.
2. Academic Foundations: Exploring Original Research
Academic research, such as the thesis "Development of a simulation model for a supply chain with Powersim" from the University of Basilicata, has laid the foundation for understanding inventory dynamics through simulation. This work explored how a simulation model can represent the interactions within a supply chain, from the supply of raw materials to the distribution of the final product. Key parameters that influence inventory levels were identified, such as supplier delivery times, demand variability, and reordering policies. The simulation allowed the impact of different strategies to be evaluated without altering real operations, demonstrating the potential to reduce operating costs and improve service capacity through improved inventory policies. UNITEC-D actively collaborates with leading European universities in this line of research, contributing to the advancement of knowledge in MRO and modern supply chains. You can consult the complete collection of theses at UNITEC-D Theses.
3. Evolution of the Industry since Then
Since the publication of this initial research, the industrial landscape has undergone a significant transformation. Originally, companies relied on static models and historical analysis for inventory planning. However, global events in recent years have highlighted the fragility of these strategies. The industry has migrated towards more dynamic and predictive approaches, driven by digitalization. The global supply chain management software market, which will reach approximately €25 billion by 2025 according to market estimates, reflects the investment in these solutions. Companies that have adopted advanced systems report improvements in supply chain efficiency ranging between 15% and 25% in terms of inventory cost reduction and flow optimization. Resilience has gone from being a desirable objective to a critical need, prioritizing the ability to adapt over mere cost minimization. This has led to increased adoption of adaptive safety inventories and multi-provisioning strategies.
4. Current Best Practices: What Leading Manufacturers Do Today
Leading manufacturers in 2026 implement a set of advanced practices for inventory management:
- AI/ML Driven Demand Forecasting: They use artificial intelligence and machine learning algorithms that not only analyze historical data, but also incorporate external factors such as market trends, seasonality, promotions and economic events to generate highly accurate demand forecasts.
- Dynamic Safety Stock Calculation: Safety stock is adjusted in real time based on observed variability in demand and delivery time, rather than using fixed values. This minimizes the risk of stockouts by keeping inventory as tight as possible.
- Collaborative Planning, Forecasting and Replenishment (CPFR): Close collaboration with suppliers and customers through shared data platforms enables comprehensive visibility and coordinated planning, reducing uncertainty and optimizing inventory flows across the network.
- Lean Inventory Principles with Risk Mitigation: Although Just-In-Time (JIT) principles are maintained to reduce inventory, strategic buffers and contingency plans are incorporated to mitigate the risks associated with unexpected interruptions.
- Maintenance Inventory Optimization (MRO): For MRO parts, specific strategies such as risk-based inventory management and predictive maintenance are applied to ensure the availability of critical components without incurring excessive costs for slow-moving parts.
5. Technological Enablers: Driving Transformation
The implementation of these best practices would be infeasible without the support of advanced technologies:
- Artificial Intelligence (AI) and Machine Learning (ML): AI not only improves the accuracy of demand forecasting, but also optimizes transportation routes, identifies consumption patterns and automates reordering decisions.
- Internet of Things (IoT): Sensors embedded in warehouses, production lines, and transportation vehicles provide real-time data on inventory status, location, and environmental conditions. This allows for granular visibility and proactive response.
- Digital Twins: A virtual replica of the entire supply chain allows managers to simulate complex "what-if" scenarios, test new inventory strategies, and evaluate the impact of potential disruptions before implementing them in the real world.
- Cloud Platforms (Cloud Computing): Provide the scalable infrastructure necessary to run complex simulation models and store large volumes of data generated by IoT and AI, making these tools accessible to companies of all sizes.
- Blockchain: Improves product traceability and transparency throughout the supply chain, providing an immutable record of transactions and inventory movements. This is especially useful for high-value products or products with specific certification requirements.
6. Practical Implementation Guide for the Plant Manager or Purchasing Director
For effective implementation of inventory optimization through simulation, a structured approach is recommended:
- Phase 1: Supply Chain Diagnosis and Mapping:
- Identify bottlenecks and critical points in your supply chain.
- Map material, information and financial flows.
- Collect historical demand data, supplier delivery times, storage costs, ordering costs, and out-of-stock costs. Ensure the quality and completeness of the data.
- Phase 2: Tool Selection and Model Development:
- Evaluate available simulation software (e.g., AnyLogic, Arena) or consider open source frameworks.
- Develop a simulation model that accurately represents your supply chain, including suppliers, production centers, warehouses and demand points.
- Define key model variables: reordering policies, safety stock levels, storage capacity, etc.
- Phase 3: Scenario Simulation and Sensitivity Analysis:
- Run simulations under different scenarios: increase/decrease in demand (±15-20%), supplier interruptions (20-50% increase in delivery time), price fluctuations.
- Perform a sensitivity analysis to understand how changes in input variables affect inventory and service results.
- Phase 4: Policy Optimization and Decision Making:
- Interpret simulation results to identify inventory policies that best balance costs and service levels. For example, a policy that reduces inventory costs by 10% with an acceptable (less than 2%) decrease in service rate.
- Use clear metrics such as inventory turns, days of inventory, customer service rate, and total supply chain cost.
- Phase 5: Implementation and Continuous Monitoring:
- Implement optimized inventory policies in your ERP or WMS systems.
- Establish Key Performance Indicators (KPIs) and a dashboard to continually monitor performance.
- Review and adjust policies periodically (every 3-6 months) based on actual data and model performance.
7. ROI and Business Cases: Real Efficiency Figures
Investment in simulation and inventory optimization offers a tangible Return on Investment (ROI):
- Reduction in Inventory Maintenance Costs: A company in the automotive sector implemented a simulation system that resulted in an 18% reduction in inventory maintenance costs, which is equivalent to an annual saving of 1.2 million euros in its central warehouse.
- Reduced Obsolescence: The ability to better forecast demand and adjust stock levels can reduce product obsolescence by 5-15%, a critical aspect for MRO components with defined life cycles.
- Service Level Improvement: By optimizing safety stock, a consumer goods company increased its on-time order fulfillment rate by 12%, resulting in improved customer satisfaction and a reduction in lost sales due to out-of-stocks by 7%.
- Logistics Cost Optimization: Simulation-based inventory planning allowed an industrial machinery company to reduce its transportation costs by 8% by consolidating orders and optimizing delivery routes.
- Payback Period: Projects of this type typically have a payback period of 12 to 24 months, depending on the complexity of the supply chain and the magnitude of the initial investment in software and consulting.
8. Conclusion
Inventory optimization using simulation models has evolved from an academic issue to a critical industrial need. In 2026, the convergence of real-time data, AI, IoT and Digital Twins provides manufacturers with tools to manage supply chains with unprecedented precision. By adopting a systematic approach to simulation, companies can achieve significant efficiency, improve resilience and ensure the availability of key components, meeting quality and safety standards such as those set by EN regulations. UNITEC-D, through its collaboration with the academic community and its experience in the MRO sector, supports the industry in the adoption of these technologies. Explore our offering of industrial components and technical solutions in the UNITEC-D E-Catalog and delve into the research driving innovation in our theses collection in UNITEC-D Theses.
9. References
- [1] Thesis: "Development of a simulation model for a supply chain with Powersim", University of Basilicata. Available at: https://www.unitecd.com/thesis/
- [2] Deloitte. (2024). Supply Chain Resilience in the Age of Volatility: Strategies for 2026 and Beyond. Industry Report.
- [3] Gartner. (2025). The Impact of Artificial Intelligence on Demand Forecasting in Industrial Manufacturing. Trend Analysis.
- [4] ISO 28000:2022. Specification for security management systems for the supply chain. International Organization for Standardization.
