It is no surprise that events in recent years have led to supply chain shortages and cost increases. Industries have been using technology more than ever to try and understand and gain visibility into their supply chains, and where possible, improve their efficiency. While the problem is evident and tangible across a wide range of industries, hurting businesses and consumers alike, great progress seems to have been made on a holistic solution that can help the decision making process when so many variables are involved. Advanced technologies, such as artificial intelligence (AI) and machine learning (ML), are becoming essential to improve efficiency and streamline operations. Could AI and ML be the silver bullet that fixes the manufacturing supply chain?
AI and ML solutions have proved useful in a range of industries to help understand the relationships between variables and the way they impact outcomes. They have even helped identify relationships that were previously unknown. Marketing and the medical industry are two great examples of where AI and ML technologies have been used to great effect, with one bringing greater insights into consumer shopping behaviour and the other into identifying disease.
Some believe AI and ML could bring countless benefits to solving supply chain issues in manufacturing and, once honed, could be the solution the industry has been desperately anticipating — not just by helping businesses grow by automating some tasks, but also by lowering costs.
Today, while many business systems create data, use it to inform users and push actions forward, they do not learn from the data. Arguably, the vast amount of data available can even slow decision making. With AI and ML the whole idea is that the data in your systems is learnt from, enabling greater automation, finding new insights at speed, and driving competitive advantage.
“Legacy data technologies do not scale while maintaining low latency and high throughput,” Lenley Hensarling, chief product officer at real-time database vendor Aerospike, explains. “AI and ML technologies function at their best with more data; they have an insatiable appetite for it. More data means better trained models. More training and more data lead to greater accuracy when the models are applied and that leads to better business outcomes.”
Moving logistics forward
Logistics requires pulling data from a variety of sources, including data centres that could be spread around the globe. Using AI tech to process this data in real time can streamline logistics and improve efficiency. For example, it can help distributors optimise delivery routes and schedules by providing up-to-date information on traffic, weather, and other factors that may impact delivery times. Machine Learning excels at descriptive and predictive analysis, taking teams from asking, “What happened?” or “Why did it happen?” to “What is happening?” or “What will happen?” These insights can lead to wiser decisions in real time and provide a greater understanding to fuel effective planning.
Visibility and traceability
Leveraging data through AI can also enhance visibility and traceability. AI-driven models can support the tracking of materials and products from the source to the end user in an instant. This can help companies identify quality issues (by pinpointing a single flawed component in one or more products) to tracking recalls and ensuring compliance with government regulations.
“Manufacturers use AI to reach across the entire length of the supply chain, gaining visibility into logistics and transportation data across the wholesalers, retailers, suppliers, and other audiences in real time,” Hensarling adds. “Gaining real-time visibility into supply chain operations by collecting and analysing data from various sources is key to helping companies identify bottlenecks, delays, and other issues, allowing them to take corrective action quickly. Waste is defined by the time spent in understanding you need to make corrections.”
AI-ding collaboration
Whilst handing over some activities to AI might seem uncomfortable, it can enable teams to focus on higher value tasks and innovation and allow companies to reduce the costs associated with human errors and time-consuming tasks. Rather than spending time manually identifying and fixing problems, AI can improve communication, ensure all parties are working toward the same goals, and help teams efficiently find the needle in the haystack based on real-time information.
Improve inventory management
Inventory management is another area where AI adds value. Goods can disappear from shelves faster than teams can manually record a shipment or replenish supplies. AI-based automation can overcome this gap and ensure goods are produced faster. Companies can manage inventory more effectively by tracking stock levels in real time to notify customers and partners of depleted or overstocked resources, as well as predict future demand, and reduce waste.
Access to AI
A recent April 2023 study from the National Bureau of Economic Research of 5,179 customer support agents found that workers who had access to an AI-based conversational assistant were 13.8 per cent more productive than employees who did not. The newest workers received the greatest benefit, reporting that the tech enabled them to work 35 per cent faster than without the AI help. Cost does not need to be a barrier. Many applications that may exist in a business could have AI features, and ChatGPT offers another route where even small businesses can use AI to perform routine tasks so that employees can focus on those that are more strategic.
AI silver bullet?
“Today, as consumers, we have all become used to seeing examples of AI at work in the real world, even taking technology such as voice recognition for granted,” Hensarling concludes. “In a business context, it can increase innovation and competitiveness and reduce costs. But companies need to ensure they have a modern storage infrastructure capable of supporting the amount of data that AI and ML workloads require, regardless of how they plan to use these technologies.”
