Rapid adoption of artificial intelligence is occurring across all sectors of the economy by various organizations. 65% of organizations are utilizing generative artificial intelligence in at least one function, which is twice the amount from only one year ago, according to a recent poll conducted by McKinsey. Furthermore, 67% of firms anticipate elevating their investments in AI over the course of the next three years.
It should come as no surprise that a significant number of these firms have made supply chain management their primary area of investment. In fact, several of them have already reported revenue improvements of five percent or more as a direct result of their use of artificial intelligence in supply chain and inventory management.
It has been projected for many years that artificial intelligence would be a game-changer for supply chain management. Right now, it is finally starting to fulfill the promise that it made. The use of this technology is rapidly bringing about a new level of both speed and efficiency. The tightening of global rules has made it more impossible to maintain compliance and efficiency without the use of advanced digital systems. As a result, it is also becoming an essential component in the movement toward sustainability. One way that artificial intelligence can decrease waste and fuel consumption is by optimizing routes and stocks, for example.
In the press, there are several instances of some of the most well-known companies in retail adopting artificial intelligence to their supply chains. In order to manage inventory, process orders, and maximize storage space, both Walmart and Amazon are using robots driven by artificial intelligence in their fulfillment facilities. In addition to this, they are using predictive analytics in order to foresee demand. Zara is also using artificial intelligence for demand forecasting and inventory management. The company is examining sales data, trends on social media, and other data sources in order to make more accurate predictions on fashion trends and modify appropriately, hence reducing the likelihood of overproduction and stockouts.
All of this is only the kickoff. As AI continues to advance, it is prepared to take on jobs that are ever more sophisticated. There is a good chance that future applications may expand into autonomous decision-making, which means that artificial intelligence systems will not only anticipate but also make modifications to supply chains in real time without the need for anything to be done by humans. It is expected that advanced artificial intelligence will oversee the majority of the activities involved in the supply chain, beginning with the procurement of raw materials and ending with the delivery of products to customers. This more in-depth integration has the potential to convert conventional supply chain models into dynamic, predictive networks that are better able to adapt to global problems and variations in the market.
Although it is understandable that brands and retailers are eager to use artificial intelligence in their supply chain operations, the reality is that many of them have not yet developed the digital infrastructure necessary to do so. A lack of structured, consolidated, and real-time data is one of the most significant obstacles that prevents organizations from fully exploiting the promise of artificial intelligence. It is necessary for businesses to start the process of establishing a common repository of supply chain data at the purchase-order, SKU, and plant levels in order to address this challenge.
Interconnecting millions of private data points from numerous data sets throughout an organization is the basis for achieving the advantages of artificial intelligence for any firm. This capacity is essential for maximizing the potential of AI. For this purpose, it is necessary to collect all of the data, beginning with the preliminary planning stages and continuing through the formulation of product specifications, sourcing, costing, and logistics, and including comprehensive information on all of the suppliers along the supply chain, all the way up to the nth tier. It is not until organizations have developed efficient data management that they will be able to begin realizing the full promise of artificial intelligence.
The use of a multi-enterprise platform for digitization guarantees that the data is up-to-date, accurate, and easily accessible. These solutions provide insight into the supply chain in real time, which enables organizations to continually monitor their supply chains, spot possible problems before they become more severe, and make choices that are guided by information that is accurate and up to date. When it comes to providing artificial intelligence with the data it requires for predictive analytics and automated decision-making, the establishment of this digital infrastructure is essential.
Not only are these platforms already using artificial intelligence in novel ways, but their capabilities are also continuously increasing. Chain-of-custody solutions that are driven by artificial intelligence have the potential to automate verification and record the chain of custody of all items, therefore dramatically improving traceability. Using these technologies, compliance risks are proactively evaluated, and it is ensured that every link in the supply chain satisfies the company’s requirements of sustainability and conforms with global environmental, social, and governance (ESG) legislation. Compliance with global environmental, social, and governance standards, such as the Uyghur Forced Labor Prevention Act, is significantly simplified by artificial intelligence (AI) via the process of automatically scanning and validating all papers against several databases of blacklisted businesses, as well as finding gaps or missing paperwork prior to shipment.
The management of quality is likewise being rethought by AI. An intriguing new application improves quality inspections by evaluating hundreds of data points around risk variables such as product type, materials utilized, and country of origin. This allows the program to calculate the possibility of a product line failing quality inspections. With this technology, organizations are able to proactively identify and resolve high-risk PO product lines. As a result, they are able to focus quality inspections around high-risk products, which helps them to reduce inspection costs while simultaneously enhancing product quality.
At a time when the retail industry is on the verge of undergoing a digital revolution driven by artificial intelligence, the possibilities for change are enormous. Those retailers that are able to successfully incorporate artificial intelligence into their supply chains will not only be able to achieve higher operational efficiency, but they will also gain competitive advantages in terms of agility, consumer happiness, and continued sustainability. If brands and retailers want to fully benefit from the expanding potential of artificial intelligence, they need to make digitalization of their supply chain a priority right now. Otherwise, they run the risk of losing out on important advancements and falling behind industry leaders.