Mastering Tomorrow’s Supply Chains: A Deep Dive into Four Generative AI Applications
In the ever-changing environment of supply chain management, technological innovations continue to reshape traditional approaches, and among these groundbreaking advancements stands Generative Artificial Intelligence (AI). The potential of Generative Artificial Intelligence in the supply chain domain lies in its transformative capability to optimize operational processes. Throughout the deployment of generative models, organizations can reach increased precision in demand forecasting, improve inventory management strategies, and develop adaptive capabilities in response to unexpected disruptions.
According to a survey conducted by McKinsey, in Generative AI, the top areas are Customer Service Operations, Operations Process Optimization, Production Planning/Scheduling, Quality Control/Inspections, and Inventory Management, ranked by executives on the field regarding where to apply Generative AI in operations.* When considering the challenges posed by a dynamic supply chain environment, it is not surprising to see operations involved in the supply chain in the top areas list. After the launches of many successful large language models such as GPT3, GPT4, Llama, Claude, Bard, etc., Generative AI is applied in many cases in the supply chain as anticipated. Here are four ways in which Generative AI can be utilized in the supply chain:
1. Analytics and Business Intelligence
In the realm of supply chain, business intelligence (BI) plays a profound role, offering organizations insights and data-driven tools needed to facilitate decision-making processes. By harnessing these tools, organizations make informed strategic choices and proactively respond to today’s market dynamics. Generative AI can be employed in business intelligence tools to provide further analysis, interpretation, and recommendations regarding data. Many business intelligence tools provide services or add-in applications of Generative AI. Microsoft PowerBI Copilot, Tableau AI, and Qlik Sense can be given as examples of BI tools having embedded generative AI services. Below is an example of Microsoft PowerBI Copilot that generates a summary in response to questions about tables and visuals.
2. Demand Forecasting and Demand Planning
In the pursuit of forecasting demand accurately, the application of Generative AI proves to be instrumental in comprehensively discerning external and internal impacts. Leveraging Generative AI, businesses extract the effects of demand drivers such as seasons, promotions, campaigns, holiday seasons and special events, etc. Based on these inferences, generative AI makes recommendations about demand plans thus enabling businesses to adapt swiftly to changing landscapes. Various products within the market offer recommendations about demand planning, thereby illustrating a diversity of solutions available for businesses aiming to optimize their forecasting strategies. Pluto7, Nextatlas, and OMP are companies providing generative AI solutions.
3. Inventory management
In the intricacies of supply chain management, the significance of inventory optimization shines through as a main strategy, streamlining operations, decreasing costs, and offering adaptability to the ever-changing demands of the market. The utilization of Generative AI in this context serves as a critical analytical tool, contributing to the optimization of inventories. Generative AI can make inventory recommendations harnessing historical sales data and other external data that may affect demand. Based on inventory data, reorder point recommendations can be generated utilizing Generative AI. LeewayHertz provides an inventory optimization service embedded with OpenAI. It recommends a detailed inventory plan by generating reports.
4. Route optimization
The strategic implementation of route optimization stands as a pivotal initiative, improving operational efficiency, minimizing costs, and enhancing logistical processes in the continuous movement of goods from origin to destination. Integration of Generative AI methodologies offers a sophisticated and adaptive solution for the optimization of complex transportation networks. Generative AI can provide an optimized route plan by minimizing cost and time and maximizing customer satisfaction. It can provide insights regarding routes and recommendations. LeewayHertz provides a route optimization service embedded with OpenAI which shortens the needed time for inventory management processes. GEP also integrates OpenAI to address potential disruptions, thus optimizing logistics.
Conclusion
Generative AI holds profound significance in supply chain management. Through the strategic deployment of generative AI models, businesses can improve their demand forecasting accuracy, augment the data effectively, manage their inventories proactively, and extract business insights. This innovative technology facilitates a proactive approach to inventory management, allowing businesses to adapt swiftly to today’s dynamic landscapes. The overarching impact of Generative AI extends beyond mere operational improvements, empowering businesses with increased agility and the capability to navigate the complexity of today’s dynamic environment with adept responsiveness.
İrem Taş
Analytics Consultant
References
- KPMG, 2023. Generative AI in Supply Chain: A Path to Better Returns
- https://learn.microsoft.com/tr-tr/power-bi/create-reports/copilot-introduction
