Data-Driven AI through the SCOR model
The narrative around AI often leans toward futuristic visions, leaving out the, here-and-now applications. However, a recent study based on real-world case studies pulls back the curtain to reveal how AI is solving concrete supply chain challenges today.
A shining example: food companies are using machine learning (ML) algorithms like LSTM and SVR to dramatically reduce forecast errors, improve inventory turnover, and minimize out-of-stock days for consumers.
However, the impact of AI isn't limited to the food sector. As this research shows, diverse industries are benefiting. Let's dive into these data-driven insights and explore how specific AI tools reshape OSCM across various sectors.
AI in Action: Revolutionizing Core Supply Chain Processes
The study, published in the International Journal of Production Research (Cannas et al., 2024), analyzes 17 AI implementation cases across six different companies. Researchers used the SCOR (Supply Chain Operations Reference) model to assess AI's efficiency and impact across the following core processes: plan, source, make, deliver, and return. This framework provides a structured way to understand how AI impacts the entire supply chain ecosystem.
The Sectors Studied:
Food Industry: Retail and grocery supply chains, focusing on perishable goods.
Energy Industry: Electricity consumption forecasting.
Construction Industry: Spare parts and inventory management.
General Manufacturing: Diverse manufacturing contexts, highlighting predictive maintenance and quality control.
1. Plan
Predictive Precision for Optimal Inventory Management
At the heart of supply chain efficiency lies planning, the process of accurately balancing supply and demand. The study confirms that AI is excelling in planning processes related to demand forecasting and inventory management by analyzing complex and ambiguous datasets to achieve more accurate forecasting.
Food Industry Breakthroughs: Organizations are experiencing improvements in inventory turnover while reducing the number of out-of-stock days through ML implementation, while also mitigating the bullwhip effect.
Specific Tools: The study highlights the use of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) models for demand forecasting at the retail stage for selected vegetables. These models have proven effective in analyzing nonlinear, complex datasets to predict demand with greater accuracy.
Hybrid Models Enhance Accuracy: Hybrid AI models combining different methods into one solution offer greater accuracy and resilience in dynamic environments. Specifically, adaptive neural-based fuzzy inference systems (ANFIS) combine statistical, metaheuristic, and ML approaches. These models improve forecasting accuracy and are more resilient in the face of uncertainty. ANFIS models excel at capturing complex relationships between variables by integrating the reasoning capabilities of fuzzy logic with the learning power of neural networks. This makes them particularly well-suited for forecasting in environments where data is incomplete or uncertain.
AI-Driven Inventory Management: Optimizing inventory policies for spare parts is another area where AI excels. According to the case studies examined in the research, machine learning resulted in an average forecasting accuracy of 88% in determining the best inventory policy!
Why Spare Parts? The management of spare parts inventories is notoriously challenging due to intermittent demand, long lead times, and the risk of obsolescence. AI can help overcome these challenges by accurately predicting demand and optimizing inventory levels. The study highlights the use of artificial neural networks and support vector regression for demand and inventory management of spare parts in the construction industry.
2. Source
Smarter Supplier Selection Processes
AI transforms the traditionally manual supplier selection process through the use of fuzzy logic and ML, evaluating potential suppliers based on factors like cost, quality, and delivery reliability. Automated systems can verify incoming order quality, ensuring sourcing precision.
Beyond Traditional Metrics: AI allows companies to go beyond traditional metrics like price and delivery time, and incorporate factors such as supplier risk, sustainability practices, and innovation capabilities into the selection process. Fuzzy logic enables the system to handle imprecise or incomplete data, while ML algorithms learn from past performance to identify the best suppliers.
Benefits: Improved supplier selection leads to reduced costs, higher quality products, and more reliable supply chains.
3. Make
Redefining Production Efficiency
In the manufacturing domain, AI enhances production efficiency with predictive maintenance and real-time quality control. By deploying smart sensors that detect anomalies, organizations can minimize downtime and ensure consistent product quality with minimal human intervention.
Predictive Maintenance: AI algorithms analyze data from sensors on equipment to predict when maintenance is needed, preventing costly breakdowns and extending the lifespan of assets.
Real-Time Quality Control: AI-powered vision systems can inspect products in real-time, identifying defects with greater accuracy and speed than human inspectors.
Benefits: Reduced downtime, improved product quality, and increased throughput lead to significant cost savings and enhanced customer satisfaction.
4. Deliver
Logistics Optimization
AI-powered delivery systems leverage dynamic scheduling and route optimization to minimize transportation costs and enhance efficiency. These systems analyze factors like traffic, weather, and fleet availability.
Dynamic Route Optimization: AI algorithms continuously monitor traffic conditions, weather patterns, and delivery schedules to optimize routes in real-time, reducing fuel consumption and delivery times.
Predictive Delivery Times: AI can accurately predict delivery times based on historical data and real-time conditions, allowing companies to provide customers with more accurate delivery estimates.
Benefits: Reduced transportation costs, faster delivery times, and improved customer satisfaction.
5. Return
Streamlined Reverse Logistics
The management of returned goods also receives an AI boost, with the automation of sorting decisions based on product conditions. This has a strong impact on logistics sustainability.
Automated Sorting: AI-powered vision systems can assess the condition of returned products and automatically sort them for resale, repair, or recycling.
Improved Efficiency: Automating the sorting process reduces the time and labor required to manage returns, improving efficiency and reducing costs.
Sustainability Benefits: AI can help companies maximize the value of returned products, reducing waste and promoting a circular economy.
Tackling the Challenges of AI Adoption
While AI offers numerous advantages, it's crucial to acknowledge the challenges associated with its adoption:
Data Quality: AI models are only as good as the data they're trained on, meaning poor data quality will significantly limit their effectiveness.
Skill Gaps: A lack of internal expertise to implement and manage AI technologies can hinder progress.
High Costs: The initial investment in infrastructure, software, and training can be a significant barrier.
Strategic Alignment: Organizations often struggle to integrate AI projects with broader business goals, resulting in a fragmented approach.
Lack of Clarity on Economic Benefits: Organizations find it difficult to fully comprehend how to measure and quantify the economic benefits of AI implementation in supply chain applications.
Lack of Experience in Cost Analysis: Even if benefits can be envisioned, the tools to measure cost and perform analysis are lacking.
SCOR: A Strategic Roadmap for AI Integration
The SCOR model provides a valuable framework for AI integration:
Prioritization: Focus on areas where AI can have the greatest impact (e.g., demand forecasting, supplier selection).
Data Investment: Invest in robust data management systems to ensure the quality and accessibility of data for AI models.
Cross-Functional Teams: Establish cross-functional teams with a mix of technical and operational expertise.
Start Small and Scale: Begin with pilot projects to test and refine AI solutions before scaling them across the entire organization.
Measure and Track Results: Continuously monitor the performance of AI solutions to ensure they are delivering the expected benefits.
Foster a Culture of Innovation: Encourage experimentation and learning to drive continuous improvement in AI capabilities.
A Final Word on AI in Supply Chains: Data-Driven Transformation
The future of supply chain management is inextricably linked to AI. By taking a strategic, data-driven approach and addressing the challenges proactively, organizations can unlock new levels of efficiency, resilience, and competitive advantage. The real-world success stories highlighted in this study (Cannas et al., 2024) provide invaluable insights and practical guidance for those looking to leverage the transformative power of AI in their own supply chains. As AI continues to evolve, the companies that embrace its potential will be best positioned to thrive in an increasingly complex and dynamic global marketplace.
Source:
Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2024). Artificial intelligence in supply chain and operations management: a multiple case study research. International Journal of Production Research, 62(9), 3333-3360.
