6 Ways Machine Learning Can Improve Supply Chain’s Bottom Line
The use of machine learning in the supply chain industry is picking up steam.
Supply chain companies are adopting machine learning at such a pace, it won’t be long before the process is commonplace. Across the industry, companies are using machine learning to integrate systems, forecast demand, provide real-time visibility on shipments and improve efficiencies in last-mile deliveries, among a host of other uses. According to a report by Gartner, the use of artificial intelligence (AI) and machine learning was one of the top 2020 trends in the supply chain industry, and one of the top technologies giving companies a leg up over the competition.
Machine learning is a complex technology. In the most basic sense, machine learning is the process of taking an immense amount of data and analyzing it to make smarter decisions. The goal is to save businesses money and generate greater profits, in part by better serving end customers. One example of machine learning in everyday life is image recognition. Facebook uses image recognition to identify and tag people in photos. In image recognition, algorithms are used to identify facial features and compare those features to facial images collected in a database. Similarly, in the supply chain, algorithms are used to measure everything from weather patterns and rail schedules to SKU profiles and stock-out alternatives. Those hundreds or thousands of data sets can be compared and cross referenced to predict various outcomes, from inventory levels to estimated delivery, with higher accuracy than ever possible.
A study published in February by Deloitte showed that “more intelligent networks enable organizations to reduce the time between collecting data and take meaningful actions.” Only 40% of survey respondents said they are actively using AI, but those who are moving toward increased digitalization will outperform their competitors. For example, the use of IoT-enabled trucks is expected to reduce transit time by 50%.
Several software companies are helping supply chain businesses become more digital. The time to dip a toe in the water to tentatively test out the efficacy of the latest technology is over. Instead, it’s time for the industry to commit to the shift, technology experts say.
“Machine learning is shifting the perspective of the supply chain,” says Manish Sharma, group chief executive at Accenture Operations. “Traditionally, supply chain models tend to approach improvements too incrementally and leaders in the supply chain industry need to think bigger.”
He points to Johnson & Johnson as one example of a supply chain success story.
“They re-tooled their supply chain to adapt to fluctuating consumer and production demand patters, with an end-to-end digital ecosystem fueled by deep data science, analytics, and automation,” Sharma says.
Gartner recently placed Johnson & Johnson at No. 3 on its Supply Chain Top 25 List for 2020 for its ability to continuously improve supply chain practices to meet demands during the COVID-19 pandemic. The company scaled up its manufacturing to meet demand for ventilators, converted manufacturing lines to produce hand sanitizer and has taken on its own vaccine efforts.
“Due to the pandemic, they’re now thinking bigger,” Sharma says. “Businesses are now looking to augment human plus machine workforces by integrating machine learning and other automation technology into supply chains. Helping drive productivity and improve employee experiences, companies are already seeing dividends and plan to scale their artificial intelligence platforms in the coming years. From the consumer perspective, businesses can now utilize their supply chains for greater visibility into sourcing and shipments to enhance the experience for them.”
During the pandemic, Accenture repurposed its SynOps platform to maintain real-time visibility into supplier inventory and automatically identify alternatives for its customers.
