In the current global marketplace, companies strive to ensure the highest efficiency in all operational aspects to remain competitive. Thus, businesses increasingly use artificial intelligence (AI) technologies to efficiently manage their supply chains to improve cost-effectiveness, operational efficiency and performance, and customer experience. This article discusses the importance, benefits, challenges, and applications of AI in supply chain management (SCM).
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Importance of AI in SCM
Effective SCM is crucial to optimize the flow of services and products and streamline business operations. Each point in the supply chain, including procurement of raw goods, automation of different warehouse processes, optimization of transportation routes and delivery time, and management of reliable suppliers, must function efficiently to improve the bottom line and the competitive edge of a business in the market.
Several companies are already using AI technologies to automate multiple supply chain tasks, including supplier relationship management, inventory management, quality checks, and back-office and warehouse logistics, Companies can automate time-consuming and manual tasks, implement greener warehouse processes, and improve accuracy, sustainability, and overall efficiency by leveraging AI techniques in supplier management quality control.
Moreover, AI, through predictive analytics, can predict future demand accurately by analyzing data, enabling companies to reduce the risk of overstocking or stockouts, streamline all supply chain processes, and optimize inventory levels.
Benefits of AI in SCM
Reduced Operating Costs: Businesses can reduce their operational costs by decreasing unnecessary production and purchasing expenses. Additionally, the data transparency provided by AI ensures greater cost savings through better supply chain visibility.
Better Customer Service: AI-powered chatbots can efficiently and quickly resolve customer issues and queries 24/7 without human intervention. Additionally, they can handle many customer requests, which can enhance customer satisfaction and reduce wait times. AI can also provide personalized offers and recommendations to customers based on their past preferences and purchases, which improves the overall customer experience.
Reduced Labor Costs through Automation: Extensive adoption of AI to automate different supply chain tasks that are conventionally performed by humans can significantly reduce the need for human labor, leading to a decrease in labor costs. Moreover, AI can perform manual tasks more efficiently compared to humans.
For instance, warehouse automation using AI can improve the accuracy and speed of warehouse operations. AI can optimize warehouse layouts to improve product flow and reduce the time required to locate items, while AI-powered robots can be utilized to pack and pick products more quickly than human workers.
Improved Relationship Management: Relationship management is a crucial aspect of SCM to achieve higher efficiency. Companies can avoid out-of-stock or overstocking scenarios through effective collaboration between manufacturers, suppliers, planners, and retailers.
Enhanced Safety: AI enables companies to take proactive measures to ensure a safe working environment, prevent accidents, and identify potential hazards and alert employees about such hazards by analyzing the data generated from different sources, including cameras and sensors, which increase worker and product safety and minimize downtime and related costs.
On-time Delivery and Optimized Transportation Routes: AI can facilitate on-time delivery of goods by tracking the shipment progress at every stage and optimizing the goods transportation routes.
AI can identify the most cost-effective and efficient routes to transport goods by analyzing the number of trucks required, the time it takes to reach from origin to destination, and the fuel consumed. For instance, UPS has integrated AI into its logistics operations to optimize package delivery and route planning. AI can also analyze traffic patterns and weather conditions to improve delivery accuracy and automation and reduce fuel consumption.
Improved Decision-making Capabilities: The ability of AI techniques to quickly analyze substantial amounts of data can facilitate critical decision-making. AI can provide greater data visibility and insights that can assist companies to make more accurate and faster decisions.
Precise Inventory Management: AI-powered tools enable companies to collect data on inventory levels in real time and utilize this information for supply chain process optimization. Companies can also adjust inventory levels and analyze demand patterns using AI to maintain sufficient inventory. For instance, Walmart utilizes AI to optimize inventory levels and analyze sales patterns to quickly provide fresh food to shoppers and reduce product stockouts, leading to higher customer satisfaction.
Additionally, AI can automate stock replenishment by alerting inventory managers when specific stock levels are reached, which ensures prompt replenishment of stock and prevents loss of potential sales. Thus, the speed and accuracy offered by AI-powered inventory management can assist businesses in improving order fulfillment rates and reducing carrying costs.
AI Techniques in SCM
Several AI techniques can be used for SCM, including artificial neural networks (ANNs), agent-based systems (ABSs), multi-agent systems (MASs), genetic algorithms (GAs), case-based reasoning (CBR), support vector machine (SVM), tree-based models, natural language processing (NLP), machine translation, k-means clustering, and Bayesian networks.
For instance, ANNs can be utilized for consumption forecasting, demand management, supplier selection, production forecasting, pricing and customer segmentation, marketing DSSs, and sales forecasting. Similarly, MASs and ABSs can be employed for distributed supply chain planning, design and simulation of supply chain systems, analysis of the complex supply chain behavior, and negotiation-based collaborative modeling.
GAs can be used for multi-objective optimization of supply chain networks and partner selection in green supply chain problems, and in developing managerial decision-making processes and multi-product supply chain networks. Data mining can be utilized for improving knowledge management and marketing, enhancing supply chain innovation capabilities, and controlling and monitoring warehouses.
CBR can be used to design mechanisms for supply chains under demand uncertainties, and for supply chain negotiations, agile SCM, supplier performance evaluations, and supply chain risk management. Additionally, swarm Intelligence can be employed for product line optimization, designing of systems for pricing, supply chain cost minimization, designing of agile supply chain networks, supply chain network architecture optimization, and inventory replenishment.
SVMs can be utilized for time-series classification in supply chains, supply chain demand forecasting, designing of systems for supply chain networks, and supplier selection.
AI Applications in SCM
In SCM, supply chain network design (SCND) is a critical area of strategic decision-making that involves determining the optimum size and locations of facilities, such as warehouses and plants, and the product flow through these facilities.
Studies demonstrated that a spanning tree-based GA built upon Prüfer number representation can effectively solve the SCND problems. Similarly, a nonlinear integer program with GA was used in a study to address the multi-echelon SCND problem involving freight consolidation and product returns across geographical areas and holding time.
Another study developed steady-state GA combined with a multiple-objective programming technique (MOPT) to identify a set of Pareto-optimal solutions for the multi-product SCND problem. A combination of fuzzy sets theory and multi-criteria decision-making (MCDM) models can be employed for effective supplier selection owing to the conflicting goals of identifying reliable and low-cost suppliers and the ambiguity of supplier attribute information.
For instance, a fuzzy, mixed-integer, goal programming model can be used for solving supplier selection problems with conflicting goals of minimizing the net late deliveries, net cost, and net rejections depending on the constraints of the buyer’s demand, budget allocation to individual supplier, purchased value of items, supplier’s quota flexibility, and supplier’s capacity.
Challenges of AI in SCM
Biased Algorithms: Erroneous data or design flaws sorted into AI algorithms can limit the functionality and effectiveness of the algorithm. AI trained on biased data can replicate the societal biases for age, race, and gender, which increases economic and social inequalities. Thus, AI can deliver skewed results and make poor decisions when the data is not representative of the entire supply chain.
Cybersecurity Risks: Attackers can utilize generative AI to create complex and new types of malware and phishing schemes that can lead to financial losses, reputational risks, and data breaches for companies. Thus, a compromised enterprise computer system can result in significant disruptions to the supply chain and the loss of valuable customer relationships.
High Implementation Costs: Companies must invest in training, infrastructure, and maintenance to fully realize the potential AI benefits. However, developing AI solutions and integrating them into the existing supply chains can be very expensive and time-consuming.
References and Further Reading
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2020). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502-517. https://doi.org/10.1016/j.jbusres.2020.09.009
Sharma, R., Shishodia, A., Gunasekaran, A., Min, H., Munim, Z. H. (2022). The role of artificial intelligence in supply chain management: mapping the territory, International Journal of Production Research, 60, 24, 7527-7550. https://doi.org/10.1080/00207543.2022.2029611
Gupta, S. (2023). BENEFITS OF ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MANAGEMENT [Online] Available at https://community.nasscom.in/communities/ai-inside/benefits-artificial-intelligence-supply-chain-management (Accessed on 06 November 2023)
Patel, V. (2023). How Artificial Intelligence Is Revolutionizing Supply Chain Management [Online] Available at https://www.computer.org/publications/tech-news/trends/ai-revolutionizing-supply-chain (Accessed on 06 November 2023)