By making it possible for machines to learn from experience, adjust to new inputs, and perform humanlike tasks, artificial intelligence (AI) is making its way into everything from chess-playing computers to self-driving cars to customer service chatbots.
Dig down deeper and you’ll see that AI is also impacting the way companies manage their global supply chains. These applications of the advanced technology may not make the headlines as often as, say, Amazon’s transactional AI platform does, but they are plowing ahead in a sector that’s historically been highly manual and human-centric in nature.
“Behind the scenes, companies have been applying AI in supply chain management in ways less obvious to the end consumer. From the automotive industry, to the pharmaceutical and consumer electronics industries, businesses are dealing with increasingly complex and globalized supply chains,” Karen Kim points out in “Artificial Intelligence in Supply Chain Management.”
“In modern multi-tier supply chains, hundreds or thousands of suppliers may contribute to a single product,” she continues, noting that the procurement of raw materials, managing trading partners, and sequentially planning and executing tasks with huge volumes of data often requires “heavy-duty” data analytics. “Unnecessary delays, caused by mismanagement, could result in supply-demand mismatch, shortages, overstocking, and poor customer experience.”
Here are four ways that companies are using AI to overcome these challenges and improve their end-to-end supply chain management strategies:
To improve demand forecasting. Good demand forecasting provides critical information about a company’s potential in the market and also helps them make informed decisions about pricing, growth, and customer acquisition. Last year, P&G announced that it would globally adopt an AI-based demand planning tool. “P&G, along with Amazon, UPS, Walgreens Boots Alliance, and other Fortune 500 companies,” Kim writes, “are using advanced machine learning algorithms to optimize demand plans for product launches, adjust stocking strategies, and/or find optimal delivery routes.”
To respond quickly to supply chain shifts. With the help of smart data, the Internet of Things (IoT), AI, and sensors along the supply chain, retailers and manufacturers are more effectively responding to changes in the supply chain. “With the advancement of AI, businesses are beginning to get a taste of what the technology can do,” James Dempsey writes in “Use IoT Tech to Respond to Changes in the Supply Chain.” “For example, we’re already seeing AI enhance supply-chain operations by automatically shifting shipments or orders to a different port of entry if there happens to be a weather event in a certain location, like a typhoon, that could negatively impact distribution.”
For better process engineering. According to Kim, supply chain managers are also using robotic process automation (RPA)—software bots that mimic human actions—to automate repetitive, rule-based operational tasks. “While RPA has traditionally involved less AI, this is changing,” she points out. “Business are coupling automation with predictive analytics to better manage operational processes.”
Eliminating supply chain delays and complexities (before they become real problems). Quickly identifying and addressing potential problems in the supply chain—aka “exception management”—can be somewhat of a hit-or-miss exercise. With AI and machine learning, that multifaceted and time-consuming process is removed because they provide algorithms that work in real-time to handle the disruption. “These innovations create an environment where as soon as a supply chain exception is identified it takes only a few seconds or less for the system to decide on the actions needed to be taken,” Sean Riley writes in “Leveraging AI and Machine Learning to Make the Supply Chain More Efficient and Intelligent.” “The solutions are then immediately enacted to resolve that particular supply chain issue in real-time.”