With massive demands and fierce competition challenging supply chains worldwide, the use of AI and machine learning in supply chain management is integral to solving key issues and improving global performance. In a market where businesses are striving to excel, AI-enhanced tools can be utilized to identify potential vulnerabilities and help drive transformational changes such as enhancing end-to-end visibility and supply chain forecasting, increase cost-saving, and overall risk mitigation, to name a few benefits. The result is a highly efficient, cost-effective business that places a strong focus on customer service. Listed below are the top six best AI use cases in supply chain management that can allow businesses to optimize performance and focus on more strategic and impactful business activities:
Using AI and machine learning tools, companies can harness the benefits of data-driven predictive analytics – demand forecasting is pivotal in decreasing holding costs and maintaining optimal inventory levels. Unlike conventional methods such as spreadsheets or statistical models, AI allows for a greater degree of accuracy, especially when considering the large number of parameters that influence the supply chain. Having a strong supply chain forecasting system indicates that your business is equipped with the resources to respond to new issues and threats in the supply chain before they disrupt the business.
1. Predictive Analytics for Demand Forecasting
Warehouse and inventory-based management are intrinsic to efficient supply chain planning and management. AI forecasting can make this easier and more systematic by preventing under and overstocking, allowing the company to meet the highest level of customer service at the lowest cost. The integration of automated robots in logistics and supply chain management will streamline product picking, and bypass manual labor practices like unloading pallets and packing items, thus saving operating costs. Additionally, these AI machines and machine learning programs in the supply chain can minimize human errors and provide businesses with data-based decision-making to optimize purchasing patterns, and predict out-of-stock items, orders, and returns.
2. Warehouse and Inventory Management
Ever-changing forecasts, raw material costs, and labor constraints can make it difficult to reliably plan production. Here, AI and machine learning models and techniques can play an instrumental role in optimizing the complexity of production plans. If not properly anticipated to meet customer demand and inventory needs, changes in production on the manufacturing line can be time-consuming and expensive. Sophisticated machine learning algorithms can utilize already available production data and identify possible areas of inefficiency and excess, thus creating an adaptable and effective system. This eliminates errors that occur between human production planners and manufacturing teams, allowing for a thriving business that is well prepared to deal with any sort of disruption in its supply chain.
3. Streamlining Production Planning
AI and machine learning can identify and simplify cases of fraud within the supply chain, from fraudulent transactions and credential abuse, to duplicate purchase orders and invoices. With the ability to process huge amounts of data, machine learning algorithms can sift through supply chain purchases and detect deviations from standard patterns by automating inspections and auditing processes, helping businesses protect themselves against unexplained disappearing annual revenue loss.
4. Increased Security and Fraud Detection
According to Forbes, the manufacturing industry is one of the most prolific generators of data, out of which “the most valuable data of all comes from product inspections that can immediately find exceptionally strong or weak suppliers, quality management and compliance practices in a factory.” No one wants to buy damaged goods, which is where computer vision technology, a machine learning-enabled technology, comes into play. Computer vision uses image recognition to automate quality inspections in the supply chain lifecycle, allowing for faster, more efficient inspections of containers or packages, as well as analyzing defects in industrial equipment. The benefit of these powerful and intelligent automated quality inspections means the chances of defective goods being delivered are greatly reduced. Whether your business is B2C or B2B, AI and machine learning can bring a greater level of transparency to supply chain management and quality inspection.
5. Automated Quality Inspection
AI is integral to understanding where a package is in the entire logistics cycle by allowing supply chain professionals to track the location of goods during transportation. Last-mile delivery is a critical aspect of the supply chain as it has the power to impact multiple verticles, like customer satisfaction and final product quality. AI and machine learning allow for insight into package transportation conditions – sensors, for example, can consistently monitor parameters like humidity, temperature, and vibrations, notifying businesses when packages might be compromised. Furthermore, real-time route optimization can track weather and road conditions and helps supply chain managers provide their delivery fleets with faster, highly optimized routes – think reduced driving time and enhanced productivity. This overall increase in tracking and transportation visibility can achieve faster and more reliable delivery commitments, allowing customers more accurate information on the shipment status.
6. Real-Time Cargo Tracking
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