The main objective of AI in the Supply Chain remains to be the management of inventory, from procurement to supplying the right product at the right time in the right place. For traditional supply chain companies, it has always been a challenge to achieve it as they focus majorly on optimizing a particular segment of the supply chain, rather than optimizing the entire value chain. This limits their operational efficiency to meet the need for granularity in customers’ unique expectations. Artificial Intelligence (AI) can help supply chain companies in breaking the silos to reinvent their operational models. AI in the supply chain helps companies in procuring and processing large datasets and provides better visibility within the supply chain.
57% of companies across the world believe that supply chain automation can provide a competitive edge as applications of AI can resolve problems like inventory planning, supply network planning, production planning, etc. With advanced applications of technologies, 25% of companies can solve their bottlenecks in forecasting supply and demand through data-driven insights by adapting and implementing AI and big data.
Companies are utilizing an optimized value network system and decision support system, as traditional practices in supply chain management, to decrease supply chain cost and optimize inventory. The applications of AI can provide optimal solutions in a decision support system. By conquering problems like product tracking and tracing in the warehouse, automated inventory management, rising supply chain management system cost, quality inspection, and elimination of defective products.
Top AI Applications in Supply Chain
AI has taken up the space to fill the gap in supply chain management for smooth business operations. Applications of AI technologies in supply chain management enhance performance and increase the productivity of the business. This section describes a few applications of AI in supply chain management.
Predictive Analysis For Logistics Management
Companies are becoming more proactive as the application of predictive analytic tools can help in forecasting advanced information regarding customer trends and demand analysis. Hence a future analysis of demand and supply helps the supply chain management system in planning, the procurement of raw materials, inventory control, new product development, and supply analysis of finished goods accordingly.
Inventory assorting is the biggest task in supply chain management. It is crucial because the information regarding stock availability is recorded based on assorted inventory. Assorting is a time-consuming process and there is always a risk of human errors. Automated robots having advanced computer vision can scan images with a high accuracy rate to provide accurate information and reduce the cost of supply chain and human errors.
Chatbots provide efficient customer service assistance. The usage of AI chatbots in supply chains will have a similar output. The chatbot system can help the business entity in procurement analysis and inventory control.
Chyme is a chatbot assistant, used by salesforce and Australia’s largest beverage-selling company, Carlton united breweries. While salesforce uses chatbots for customer assistance, Carlton uses chatbots for client engagement and inventory control management. The chatbots provide instant information regarding procurement analysis, stock inputs, stock analysis, shipment status, and information regarding other queries. These help the employees in utilizing their time optimally.
Automated Quality Inspection
An automated quality inspection provides an advantage over manual methods used for inspection at logistics hubs. With automated quality inspection, the robot system programmed with computer vision, object detection, and machine learning algorithms, scans products in a three-dimensional view that detects faulty products at the ¼th time of human inspection.
Esyncronised supply chain
To facilitate and coordinate supply chain activities, supply chain firms always transfer information regarding predictive analytics reports, demand forecasting, distribution channels, and joint distribution through web-based systems or electronically. Using web mining, and data mining tools, the data can be decoded and analyzed to recognize known and unknown patterns. This data, when further processed, will be helpful for other industrialists to know customer profiles, supplier profiles, demand fluctuations, sales trends, and sourcing trends on other websites.
Zara case study: how Zara implemented applications of AI for enhancing its supply chain management system
Zara is a chain of fashion retail stores established in the year 1975 by the Inditex group. According to the 2019 report, Inditex is the worlds leading retail fashion chain. By leveraging AI, Zara was able to triple its profits by opening stores in 94 countries with a net worth of USD 68.5 billion.
What made Zara lead its competitors? Zara is in business for the last three decades. Its unique business model and usage of advanced technology made its competitors lag, Zara gets credit for the agile supply chain. The whole supply chain process could be divided into four groups- organization and design; purchase and production; product distribution; sales and feedback.
The incorporation and implementation of artificial intelligence automation and big data into its business strategy and supply chain helped Zara drive more sales. Zara changes its designs every two weeks and in a year it releases 40k new outfits, which is only possible with systematically organized production and supply chain structure, with 60% AI contribution and 40% human efforts driving its growth. It uses advanced automated systems at various levels of production processes and highly developed robot engineers for inventory assigned to product distribution. It is incorporated with intel on developing devices to measure clothing volume inboxes and Fetch Robotics on robot deployment for stock inventory, microchips from Tyco, an alarm provider are also used to track product data and locations along the supply chain.
As 40.7% of the companies in the world are automating their traditional practice in supply chain management to drive more customers and suppliers, others are lagging behind. The application of AI technologies can embrace the supply chain management systems by providing live product traceability, intelligent inventory management system, computer visioned quality inspection, etc. which not only reduce human errors but improves and smoothen the business processes.
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