Artificial intelligence (AI) empowers supply chains to predict demand and optimize across the board, from purchases to shipping. AI fuels data-driven decisions in supply chain optimization, boosting efficiency and cost-effectiveness. This article deliberates AI-based techniques' benefits, challenges, and applications in supply chain optimization.
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Importance of AI
In supply chain optimization, AI is gradually becoming essential for businesses due to increasing volatility in global markets. AI-powered supply chains unlock value through better logistics, smarter inventory, satisfied customers, and lower costs. Thus, AI-based methods assist organizations in monitoring and optimizing complex supply chain aspects, allowing them to increase operational efficiency and manage inventory levels actively.
AI-based technologies enable organizations to factor in the relationships between supply, demand, and costs while making decisions on their supply chain networks. Natural language processing and machine learning (ML) analyze large datasets to identify hidden relationships between variables for predicting future actions.
For instance, historical data has been successfully analyzed using ML algorithms to forecast customer demand. Various ML models have enabled organizations to learn the buying behavior pattern and seasonality in the data. Additionally, supplier selection and production scheduling can be optimized using AI techniques.
Moreover, timely insights about alternative routes or sources to optimize on-time delivery can be obtained using AI. The analysis of supply chain operations, such as inventory optimization, supply chain network design, and order segmentation, are performed using AI-based technologies.
AI-based visual analytics provides detailed insights into supply chain operations, improving supply chain decisions. Several AI-based solutions independently make supply chain optimization-related decisions, such as selecting an optimum supplier dynamically for a specific task or optimizing the delivery route.
This type of optimization in supply chain networks enables organizations to reduce transportation costs and improve delivery times. Nature-inspired & decision-theory methods tackle diverse optimization challenges in supply chains. Popular methods are genetic algorithm, ant colony optimization, and Bayesian approaches.
Benefits of AI
Higher Efficiency: The optimization of supply chain processes and reduction of lead times using AI-based technologies enable organizations to respond promptly to customer demands, resulting in greater satisfaction to the customers, reduced expenses, and faster delivery times.
Better Demand Planning: Accurate demand forecasting by analyzing historical data is achievable using AI, enabling supply chain organizations to improve profitability, minimize waste, and avoid understocking or overstocking.
Greater Visibility: Organizations gain real-time insights into shipping routes, production status, and inventory levels. These insights assist them in quickly responding to unexpected supply chain disruptions and making more informed decisions.
Cost Savings: AI identifies savings opportunities by reducing the cost of transportation, minimizing waste, and optimizing production schedules, which results in significant overall cost savings.
Enhanced Collaboration: AI-based technologies ensure improved collaboration between customers, suppliers, and organizations by coordinating activities and sharing information. Improved collaboration leads to robust relationships, enhanced communications, and better outcomes for all involved parties.
Higher Agility: Insights and data are obtained in real-time using AI techniques, enabling organizations to respond effectively and quickly to changes/disruptions in the supply chain. Thus, AI improves organizational agility and fine-tunes responses to new challenges and opportunities.
Applications of AI
Several organizations across different sectors have adopted AI to optimize their supply chain networks. For instance, Flipkart, a leading e-commerce player in India, has implemented AI-based algorithms to optimize its supply chain processes. Flipkart's AI forecast slashed delivery times and inventory costs.
Similarly, BMW, a leading automotive company based in Germany, has adopted an AI-based logistics planning system for supply chain optimization. The AI-powered system utilizes ML algorithms to optimize the routing of shipments, reducing transportation costs and facilitating faster delivery.
Another leading company has developed a supply chain predictive model capable of automatically creating the most suitable routing options for an enormous raw materials supply chain that includes transformers almost equal to the size of a large room and circuit breakers small enough to fit on a store shelf.
ML models were used to feed enterprise supply chain data from multiple business units, such as product shipping route-related data and transportation rates and policies. Initially, data engineers developed a tool for data extraction to collect the enterprise data, then verified the collected data, and finally processed the data.
The model analyzed 130,000 flow and routing constraints, 200,000 transportation policy data points, and over 150 initial scenarios and identified $9.32 million in annual savings that can be realized by altering product flow in the supply chain. Warehouse execution systems (WESs) use AI to increase the efficiency of existing logistical systems over time. An apparel retail manufacturer implemented WES to support its retail store fulfillment/replacement of items in stores.
A distribution center was developed using WES to replenish products in 3,900 retail stores. The company had to change its store fulfillment operations for eight individual store brands into a single distribution center, which implied that the distribution center must possess a high storage density and ensure speedy product replenishment simultaneously.
The whole distribution center’s operational processes, including receiving orders and scheduling and shipping those received orders, were optimized using WES. The developed retail store replenishment system enabled the company to successfully accommodate almost 600,000 pieces per day restocked in their stores, which was sufficient for all replenishment of eight brands, including during peak conditions.
Moreover, the storage capacity was expanded and processing costs were reduced by the system. Imperial Logistics, based in South Africa, has deployed an AI-powered inventory management system that utilizes ML algorithms for optimizing stock levels. The company collaborated with its customers and partners to make the system more effective to improve the supply chain operations.
Maersk Line has implemented a neural network algorithm for transportation optimization to improve the supply chain performance. In the supply chain, the integration of AI substantially reduced transportation costs, resulting in increased resource utilization and operational efficiency.
Additionally, the neural network algorithm implementation improved the delivery performance, which indicated a more effective and streamlined logistics process. Retail companies in Benue State, Nigeria have optimized demand forecasting processes by integrating neural network algorithms in the supply chain.
The Aramex logistics company has employed a genetic algorithm for transportation optimization. Using a genetic algorithm significantly improved the cost-effectiveness and efficiency of Aramex’s logistics operations. Iran-based Tamin Pharmaceutical Investment Company leveraged AI to optimize inventory management. Inventory turnover improved sharply, indicating more efficient use of available stock, increasing agility, and streamlining the supply chain.
Additionally, this AI-driven strategy reduced inventory holding costs, ensuring financial efficiency and operational cost-effectiveness for the pharmaceutical company and underscoring the need to leverage AI for improved supply chain outcomes.
Challenges of AI
Data Availability and Quality: AI-based models need large volumes of data to train and improve themselves over time. However, such data collection exercises are expensive and time-consuming, specifically for businesses/organizations with complex supply chains. Similarly, the quality of data used to train AI models primarily influences the accuracy of the model outcomes. Thus, AI models cannot make accurate decisions or predictions when either incomplete or inaccurate data are used for training them.
Complexity: AI models are typically complex and cannot be understood easily, which increases the difficulty for businesses to trust the model results/outcomes and make decisions depending on them.
Regulation: The adoption of AI for supply chain optimization is a new trend. Only a few regulations have been implemented to govern AI usage, creating uncertainty for businesses about AI usage and compliance with existing regulations in this field.
In conclusion, AI supply chain optimization has delivered several operational benefits to businesses and enabled them to remain relevant in a rapidly changing global marketplace. Many organizations are actively adopting AI solutions to gain an edge in an increasingly competitive business environment. However, the existing challenges must be addressed effectively to increase AI adoption for optimizing supply chain processes.
References and Further Reading
Pournader, M., Ghaderi, H., Hassanzadegan, A., Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241, 108250. https://doi.org/10.1016/j.ijpe.2021.108250
Sakala, C. M., Bwalya, S. M. (2023). The Role of Artificial Intelligence in Optimizing Supply Chain Performance. Journal of Procurement and Supply Chain Management, 2(1), 1-14. https://gprjournals.org/journals/index.php/JPSCM/article/view/215
Kumari, N., Chaudhary, D., Kaur, H., Yadav, A. L. (2023). Artificial Intelligence in Supply Chain Optimization. 2023 International Conference on IoT, Communication and Automation Technology (ICICAT), 1-6. https://doi.org/10.1109/ICICAT57735.2023.10263631
Khadem, M., Khadem, A., Khadem, S. (2023). Application of Artificial Intelligence in Supply Chain Revolutionizing Efficiency and Optimization. International journal of industrial engineering and operational research, 5(1), 29-38. https://bgsiran.ir/journal/ojs-3.1.1-4/index.php/IJIEOR/article/view/34
Makkar, S., Devi, G.N.R., Solanki, V.K. (2020). Applications of Machine Learning Techniques in Supply Chain Optimization. ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management, 861-869. https://doi.org/10.1007/978-981-13-8461-5_98