Are Your Supply Chains Ready for AI?: Webinar Recap

Craine began the webinar with comments on the potential of AI to improve business processes. “This is an important topic as many experts predict that supply chain automation will double between now and 2028,” Craine said, noting AI and its related technologies and tools can take over much of the difficult work of core business processes and free humans up for more strategic business activities. Sparking the fast adoption of AI, of course, is the boost to the bottom line from improved operational efficiencies and employee productivity, among other benefits. Craine outlined several research findings of the growth and benefits of AI, such as the finding by Research and Markets that AI-enabled supply chains are 67 percent more effective due to reduction in risks and costs. “AI’s ability to manage huge amounts of data, understand relationships, provide visibility and support better decision making makes AI a potential gamechanger,” Craine said. “So the questions become ‘are your supply chains ready for AI’, ‘how do you get the most out of these solutions’ and ‘what are some best practices’?” While we can’t cover all the compelling information covered in the webinar, let’s take a look at a few highlights, starting with contributions by Generix executives.

Inventory management and AI

Craine posed the question of how AI supports predictive analytics to improve inventory management, such as using forecasting models that use historical data, market trends and external factors to predict future demand more accurately.

Generix’s Langois highlighted how data is a key component of inventory management. “Clearly using AI for better use of the data, cleaning the data, and connecting the data, there’s a lot of possibilities for inventory management.”

When asked about using AI-driven forecasting, Generix’s Cyr said: “One example is for new products where you don’t have historical data and you are trying to forecast how many you need to buy, where you need to store it in the warehouse, etc.” He noted predictive AI with modeling can provide valuable links between products that are similar using historical data from similar products, which usually wasn’t attainable with the use of only databases and sales data. Cyr said: “The way to implement is to make sure the machine learning understands the similarities between the products and then it can help us predict things which we were not able to do previously.”

AI continues to improve robotics and other equipment

AI and its subset of machine learning are combining with other technologies such as IoT, language processing, and computer vision to generate value in the warehouse. Think autonomous mobile] robots (AMRs), also known as collaborative robots which work alongside people, autonomous guided vehicles (AGVs), 3D bin-picking systems, and wearables.

Both Langois and Cyr weighed in AI’s use with robotics and material handling equipment.

“You can use an AI platform to better orchestrate all those resources,” Langois said. “It’s not only about putting in robots or an intelligent conveyor, it’s about looking at all your resource to better predict, better plan, and use information in real time to make adjustments as things occur and be more proactive rather than reactive.” This means warehouse managers are left with scalable solutions both in the human workforce and equipment to meet the peaks and valleys of warehouse and distribution center workloads.

Cyr emphasized that robots are not replacing people, but they are supplementing the workforce and have gained social acceptance. He noted technical jobs are needed to manage robotic and material handling fleets. “Robots are not replacing people. We are lacking people (in the warehouse),” Cyr said.

He gave the example of the once-static paths of AGVs that have evolved in recent years to include more flexible navigation. “And AMRs, with their intelligence, are able to go around obstacles, making them more efficient. Since these robots are more and more efficient, it’s making the life of the people easier inside of the warehouse.”

Many uses of big data with AI

AI and machine learning require vast amounts of data to work effectively and these technologies leverage data to do things like create algorithms, data analysis and build prediction models which give supply chain managers greater insights and ability to make better decisions. To this end, Craine raised the topic of using AI and big data, whether to review large volumes of data such as regarding large numbers of suppliers or using AI to identify irregularities and fraud.

Bhardwaj of Deloitte emphasized that big data and AI can be used for multiple uses such as ESG initiatives to track sustainability levels of the supply chain, such as in managing company sustainability goals. He said: “When it comes to managing large numbers of suppliers, big data and AI is being looked at for sustainability and emissions. You’d rather do that proactively with your suppliers and help them track it rather than be pushed against the wall later (when they don’t meet the requirements you set).”

AI can thus be used to moderate risk frameworks. “It can identify issues with suppliers located in a different geographical region where you don’t have a lot of control on how they work or whether they are ethical or not,” Bhardwaj said, adding, “That is where AI can provide powerful insights that can help really drive decisions that might make or break the future of the company.”

Saiz de Omeñaca Monzón of the U.N. Economic Commission for Europe added: “You can use AI and blockchain and supplier information to track the goods in and out of countries, with customs clearance, and all the way to delivery with the customer.” The results are improved end-to-end visibility and control of the supply chain which reduces risk and helps to avoid fraud.

Using AI to parse and clean data

An attendee asked about whether using AI brings real efficiencies or if it’s just more work to define the outputs of AI and enrich the data. Langois responded with a Generix customer use case.

“A customer of ours is starting to use AI and machine learning to help with their order entry processes. They receive orders from thousands of customers in different formats, such as via email, EDI message and fax,” Langois said, noting AI is used to analyze inbound order information, such as reconciling the data, validating customer information and their history. “By the time the order makes it to their ERP system, it’s just better data. It’s clean.”

Langois said the company currently has over 20 people in the department, and expects that within a year the application of AI to its inbound order process will greatly improve efficiencies. “They expect that within a year, they will only need two people in that department and will have better data, less error and better customer service.”

AI is evolving

Many of the webinar guests suggested that AI is a journey and recommended to start with small AI-related projects when undertaking an AI-enabled solution. Always monitor results and gradually involve more people in development of AI initiatives. As these applications of AI suggest, AI holds great potential to transform supply chain processes, bringing the enterprise—and entire supply chains—greater efficiencies that show up in improved accuracy, responsiveness as well as satisfied vendors and customers. Generix Group views AI as a constantly evolving technology which we use and continue to develop in many of our Warehouse Management Systems, and AP Automation and Invoice Services. Watch the full SupplyChainTalk webinar “Are Your Supply Chains Ready for AI?” here: https://www.business-reporter.co.uk/supplychaintalk-on-demand/are-your-supply-chains-ready-for-ai