Modern supply chain management is fully dependent on technology. Like any other field, supply chain management has been included in adopting modern, cutting-edge technologies. For instance, the Internet of Things, Robotics, Blockchain, machine learning, and artificial intelligence have enabled the automation of supply chain systems, improved efficiency, and enhanced competitiveness. Artificial intelligence has recently emerged as a revolutionary technology that has taken up supply chain operations. The essay provides an overview of how artificial intelligence has become a disruptor in supply chain management practices and is poised to change the level and scale of supply chain operations.
Artificial intelligence is the ability of machines to learn and simulate information from previous human input information to replicate and improve output and decisions. AI is thriving because organizations are required to remain abreast of the everchanging consumer expectations, global competitiveness, and the need to remain relevant in the market. Ideally, AI has the potential and is already revolutionizing supply chain operations. Particularly, AI is relevant and applicable to the production process, inventory management, and product distribution channels. Helo and Hao (2022) state that AI-based supply chain management has four critical aspects: “optimisation, prediction, modeling and simulation, and decision support.” In supply chain management, AI technologies through machine learning and big data integration provide data processors that collect and integrate different data points to provide valuable insights for organizational decision-making. Essentially, this is because supply chain management relies heavily on data and insights that affect production, distribution, and inventory management decisions.
Artificial intelligence is well suited for performing complex tasks that require manipulating large data sets to identify event patterns and correlations. One important aspect of supply chain management is demand forecasting. Demand forecasting involves predicting customer demand for products to ensure that delivery is timely, efficient, and of the right quantities to satisfy customer needs without exceeding. AI is leveraged in SCM operations to analyze historical sales, demand, and market trends data because it can identify and model non-linear relationships among data variables (Soori et al., 2023). Ideally, when AI is integrated into operating systems that document vital data on sales of a particular product, the technology can provide real-time and continuous insights into trends and product sales performance in different markets. Coupled with historical data, artificial intelligence technologies provide future demand predictions that can be used for production planning (Sharma et al., 2022). For instance, AI technologies such as artificial neural networks allow for demand segmentation, sorting data on customers and products based on their demand patterns (Soori et al., 2023). Accordingly, AI’s adaptability to changing patterns ensures that organizations are aware of subtle yet critical changes in demand to provide reliable and up-to-date forecasts for decision-making. As such, AI is instrumental in demand forecasting, which helps organizations plan procurement activities, production schedules, and inventory optimization.
Artificial intelligence is vital in inventory management. Inventory optimization in supply chain operations involves efficiently managing stock to meet customer needs without accruing extra costs. For instance, Soori et al. (2023) note that artificial neural networks are integral in inventory management and optimization of supply chain operations. The ANNs analyze large data sets to identify patterns, make predictions, and select the most suitable outcomes based on the inputs given. As such, organizations with real-time data, historical data, and value parameters on inventory can leverage these artificial intelligence technologies to
Accurate demand prediction and forecasts are important in inventory management. The data collected and analyzed by AI technologies such as artificial neural networks provide insights into customer demands, stock levels, and production capacity, which help reduce the incidence of stockouts or excess inventory. For instance, AI can be trained on lead times, changes in demand, and required service levels (Helo & Hao, 2022). Given such historical data, the technologies provide relationships between the variables and recommend the optimal inventory management decision that meets customer needs and reduces associated costs in the organization (Soori et al., 2023). Based on insights and predictions from AI, supply chain managers can initiate inventory control policies on order quantities and reorder points to optimize decision-making and operational efficiency. Accordingly, well-harnessed AI technology improves efficiency in inventory management and reduces costs associated with understocking or overstocking in organizations (Sharma et al., 2022). Therefore, AI is vital in inventory management because it leverages data to provide reliable estimates for managing customer orders, stock levels, and delivery to reduce costs linked to poor inventory management.
Artificial intelligence is also relevant in risk analysis and mitigation. Supply chain operations are bereft with numerous risks that could jeopardize and constrain production, delivery, and access to vital products. According to Wong et al. (2022), a firm’s preparedness to access up-to-date and reliable information guarantees SC competitiveness, agility, and responsiveness to economic dynamics. As such, organizations with adaptable integrated machine learning systems leverage data on operations and sales to identify possible sources of supply constraints, demand volatility, macroeconomic changes, and production interruptions that could affect operations. In essence, AI-supported systems provide insights into possible risk points and their likelihood of occurrence, which gives companies time to mitigate them to avoid excessive losses or disruptions. For instance, AI technologies can predict production delays, disruptions in operations, and even product quality issues such as product anomalies and defects (Soori et al., 2023). As such, AI technologies should be equipped with the capacity to access the most recent data and historical records to generate up-to-date insights on market changes.
During periods of uncertainty, organizations must identify suitable suppliers for products. AI technologies provide an adaptable mechanism for analyzing existing trade partners to establish the most viable, efficient, and less costly to the organization. Essentially, supplier selection and evaluation are vital in the supply chain because only suppliers meet the required organizational criteria and performance (Soori et al., 2023). As such, AI technology is used to evaluate the performance and metrics of different suppliers to determine who meets the organizational needs, such as quality and timely delivery. Such insights are shared among supply chain stakeholders such as producers, distributors, retailers, and consumers to ensure the ecosystem survives anticipated disruptions with minimal losses. In essence, AI technology provides a mechanism for information processing that improves the preparedness and resilience of supply chains by ensuring optimum inventory planning for unforeseen events (Modgil et al., 2022). Therefore, adaptable AI systems are appropriate in supply chain management because they ensure organizations are flexible and responsive to dynamic market trends to avoid heavy losses.
In conclusion, technology is part and parcel of supply chain operations and management. The entry and proliferation of AI in every sector of the economy is a promising and refreshing technology. In supply chain management, AI technology is critical in decision-making support, which is required in every facet of SCM operations. AI technologies leverage machine learning and big data to analyze and evaluate past and present organizational data to provide valuable insights on different operational aspects. AI is valuable for demand forecasting, inventory optimization, operational planning, and supply chain risk mitigation. Therefore, the future of SCM operations is hinged on the careful adoption of AI technologies to improve operational efficiency, reduce costs, and optimize processes to match the competitive landscape of supply chain management.
References
Helo, P., & Hao, Y. (2022). Artificial intelligence in operations management and supply chain management: An exploratory case study. Production Planning & Control, 33(16), 1573–1590.
Modgil, S., Singh, R. K., & Hannibal, C. (2022). Artificial intelligence for supply chain resilience: learning from Covid-19. The International Journal of Logistics Management, 33(4), 1246–1268.
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.
Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial neural networks in supply chain management, a review. Journal of Economy and Technology, 1, 179-196. https://doi.org/10.1016/j.ject.2023.11.002
Wong, L. W., Tan, G. W. H., Ooi, K. B., Lin, B., & Dwivedi, Y. K. (2022). Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis. International Journal of Production Research, 1–21.
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