Through its ability to transform production and distribution, AI promises to ease the complexity in the supply chain. According to head of product management and strategy at IT firm Infosys, Sudhir Jha, the application of AI in the supply chain will have a larger economic impact than any other use of the technology.
Companies are already making use of machine learning robots to improve the running of their factories and warehouses. But, as explored in The Economist, AI has the potential to impact other areas of the supply chain.
Managing finances and paying suppliers are essential but time-consuming parts of the supply chain. Though, as BP’s chief digital-innovation officer Morag Watson pointed out, AI can drive back-office efficiencies, in much the same way as Microsoft Excel helped finance departments in the late eighties.
Early adopters are already using AI tools to scan invoices, predict payments and even forecast which clients will pay late.
When it comes to inspecting products for flaws on assembly lines, computer-vision systems are far more accurate than humans. Used in this way, AI can boost manufacturing at the production level. Chipmaker Nvidia is already using this technology to quality-check their assembly line products.
Unexpected machine breakdowns can cost businesses significant time and money. By combining data on past performance using smart sensors, AI can predict when equipment may fail, enabling businesses to carry out maintenance before faults occur. To determine the impact of weather and other external influences on machinery through simulations, some companies have created virtual representations, or ‘digital twins’ of their assets.
According to IHL Group, in 2015 overstocking cost companies around $470bn, while under-stocking costs totalled $630bn. Using predictive AI, organisations – particularly retailers – will be able to save money and storage space by improving inventory management and demand forecasting
Amazon implements algorithms to predict demand for its products, as much as 18 months in advance. The online retailer’s director of machine learning, Ralf Herbrich, explained that clothes purchases were the most difficult to predict. To do this the company has to anticipate which colours and sizes should be stocked at specific warehouses based on customers’ body shapes and preferences.
For businesses handling cold or frozen items, knowing the sequence in which items will arrive and leave a warehouse is crucial to ensuring that pallets are in the right position. Here, predictive AI can help too – Lineage, a company that keeps food cold for its grocery and restaurant clients, has boosted efficiency by 20% using AI for smart placement
With so many moving parts in a global supply chain, AI is key to helping companies provide better routes, track goods and predict the arrival of packages. By introducing sensors and GPS into everything from lorries to container ships, AI can ensure businesses are able to provide customers with the most up-to-date information about their purchases.
At Adare, we’re as serious about supply chains as we are about emerging technologies. Read our recent post on setting up supply chain AI, or find out how we can help your business by contacting the team today: firstname.lastname@example.org.