Key AI Applications for Supply Chain Optimization
Improving end-to-end visibility and response time
With AI solutions, you can collect and analyze real-time and historical data from multiple connected devices and systems (including SCM, ERP, and CRM systems) to gain broader and deeper insights into work that are very useful to decision-makers. Using these solutions, the procurement team can gain insight into the supply chain, anticipate problems (whether within the organization, such as due to disruptions, or externally, such as delays in deliveries), and take alternative measures to minimize the impact on the supply chain. Delay in the actual response negatively affects supply chains and, consequently, net profit.
AI solutions enable organizations to collect information from multiple different contractors, customers, and their own functions (including suppliers, customers, inventory, and products) in real-time and use it to make accurate predictions. Traditionally, forecasting does not include real-time details and is based solely on historical data. However, with the use of AI, forecasting accuracy has improved significantly, which allows leaders not only to plan better but also to increase efficiency. In addition, leveraging AI to automate lower-level decision-making can free up bandwidth for managers to focus on high-level strategy and decision-making.
Effective supply chain and production planning
AI tools and solutions help you analyze huge datasets in real-time, balance demand and supply gaps, plan production efficiently, efficiently plan production activities, and develop error-free SCM plans and strategies. AI can help to properly assess market needs and manage production accordingly to avoid overproduction or product shortages, which can lead to losses.
Supplier selection and supplier relationship management
AI solutions can be used to analyze different sets of data (such as delivery performance, audits, ratings, and credit scores) and provide customized advice on supplier relationship management. Up-to-date and regular information about potential or existing suppliers can be used to build mutually beneficial relationships.
Optimization of the logistics route
AI solutions enable decision-makers to analyze existing routes, identify bottlenecks and focus on the best route; this reduces both time and overall warehousing and shipping costs. AI and ML data processing tools help capture the details of moving goods in real-time and correctly estimate delivery times.
Warehouse management (WMS)
By using AI solutions, the amount of both excess and insufficient stocks can be reduced. AI analyzes large datasets much faster and eliminates errors that can appear when an analysis is done manually. Automating day-to-day tasks such as forklift management, sorting, and inventory management using drones or autonomous ground vehicles is transforming warehouse management. Despite the benefits it offers, AI has yet to delve deeper into manufacturing. Conceptually strong algorithms, as well as innovations in big data, will not only increase computational power but also help overcome the challenges associated with data integration, helping to expand the use of AI in SCM.
Which companies and startups apply machine learning to their workflow and how they do it: https://acropolium.com/blog/use-cases-of-ai-in-transportation-logistics-are-they-relevant-for-your-business/
