Research-Article
Perishable Product Inventory Management Based on Fuzzy Control Algorithm
This paper proposes an intelligent decision-making method based on a fuzzy control algorithm to address the uncertainty and dynamic nature of perishable product inventory management. By constructing a fuzzy controller with inventory potential and inventory freshness as dual input variables and combining 25 expert rules, it effectively responded to demand fluctuations and product deterioration risks under different sales modes.
Executive Impact: Key Metrics & Enterprise Value
The fuzzy control strategy significantly outperforms traditional methods in managing perishable inventory, particularly in real-world scenarios, leading to substantial improvements in cost efficiency, waste reduction, and service levels. This translates directly to enhanced profitability and operational robustness for businesses.
0%
Average Cost Reduction (Freshness Preference Mode)
0%
Waste Rate Reduction (From 1.5% to 0.3%)
0%
Maintained Service Level
0
Total Downloads (Since 01 April 2026)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
-7.9%
Average cost reduction in Freshness Preference mode compared to traditional (r,Q) strategy. This significant improvement demonstrates the economic advantage of fuzzy control.
80%
Reduction in waste rate from 1.5% to 0.3% in Freshness Preference mode. This drastically reduces spoilage and associated costs.
| Metric | Traditional (r,Q) Strategy | Fuzzy Control Strategy |
|---|---|---|
| Average Cost (Freshness Preference) | 11.39 | 10.49 (-7.9% improvement) |
| Waste Rate (Freshness Preference) | 1.5% | 0.3% |
| Waste Cycle Rate (Freshness Preference) | 5.2% | 1.1% |
| Service Level (Freshness Preference) | 95.8% | 96.1% |
| Adaptability | Low (fixed parameters) | High (dynamic adjustment) |
| Robustness | Lower | Higher |
Enterprise Process Flow
Information Extraction (IP, FR)
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Fuzzy Control Processing
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Inventory System Simulation
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Performance Metrics Output
Real-world Application: Perishable Inventory Optimization
Fuzzy control addresses the inherent challenges of perishable product inventory management, such as uncertain demand, product perishability, and complex replenishment decisions. By dynamically adjusting reorder points and order quantities based on inventory potential and freshness, the system provides a robust solution for real-time inventory states. This method is particularly effective in scenarios like fresh produce or dairy, where freshness directly impacts consumer choice and waste.
- ✓ Dynamic Adjustment: Adapts to real-time inventory states and demand fluctuations.
- ✓ Freshness Integration: Incorporates product freshness alongside inventory levels for replenishment decisions.
- ✓ Waste Reduction: Minimizes spoilage and economic losses through proactive management of expiring goods.
- ✓ Enhanced Service Level: Maintains high customer satisfaction while optimizing costs.
- ✓ Robustness: Superior performance compared to traditional methods under varying demand fulfillment patterns.
Calculate Your Potential ROI
Estimate the tangible benefits of implementing an AI-powered fuzzy control system for your inventory management.
Estimated Annual Savings
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Annual Hours Reclaimed
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*This calculator provides an estimate based on industry averages and the efficiency gains observed in similar AI deployments. Actual results may vary depending on specific business operations and implementation details.
Your AI Implementation Roadmap
A structured approach to integrating fuzzy control into your perishable product inventory.
Phase 1: Discovery & Data Integration
Assess current inventory systems, data sources (demand, perishability, logistics), and integrate relevant data streams to feed the fuzzy control model.
Phase 2: Fuzzy Model Development & Training
Design and configure the fuzzy logic rules based on expert knowledge and historical data. Train and optimize the fuzzy controller with inputs like inventory potential and freshness.
Phase 3: Simulation & Validation
Conduct extensive simulations using various demand patterns (FIFO, Random, Freshness Preference) to validate the fuzzy control strategy's performance against key metrics (cost, waste, service level).
Phase 4: Pilot Deployment & Refinement
Implement the fuzzy control system in a pilot environment, monitor its real-time performance, and refine rules and parameters based on operational feedback.
Phase 5: Full-Scale Integration & Monitoring
Roll out the fuzzy control system across all relevant perishable product inventory operations, establish continuous monitoring, and set up adaptive learning mechanisms for ongoing optimization.
Ready to Transform Your Inventory Management?
Leverage advanced AI to reduce costs, minimize waste, and enhance service levels for your perishable products.
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