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How does machine learning support supply chain optimization?

Machine learning is being used to improve a wide array of business processes, including supply chain management functions. Here's a look at three areas it's helping the most.

Machine learning is being used across industries as a way to improve business processes. In the area of supply chain optimization, machine learning is being put to use in detecting inefficiencies and adding better control to processes. To this end, supply chain managers should be looking at their own opportunities to apply the technology. Here are three areas to explore where machine learning could enable supply chain optimization.

Optimizing supply chains through data

Machine learning algorithms can address more data and more diverse data and so are able to improve demand forecasting accuracy beyond its current average of about 75%, according to analysis by demand forecast vendor Arkieva. One way machine learning enables supply chain optimization is by addressing a wider range of variables than is typically included in demand forecasting so that more factors are considered, and the observations made are then learned through ongoing iteration.

The data sets that contribute to demand planning are already robust and include factors such as promotions, media, market modeling, new products and historical performance. But machine learning adds less data-specific values, such as promotional lift, halo effect and lift attributed to a product segment, seasonality and web presence or social media. These variables are normally taken into account through human insights, but machine learning is able to apply them at scale to improve on the accuracy of forecasting and reduce error rate.

Boosts to inbound shipment capabilities

Physical inspection and maintenance of assets across the supply chain can be advanced by machine learning's visual pattern recognition ability. The software and people involved with supply chains spend considerable time and effort checking products as they arrive at the loading dock. At the most basic level, inbound shipments can be validated by recognizing the packaging and counting quantities. On a more granular scale, pattern recognition can be applied to identify wear conditions on production line equipment and vehicles for tire wear and evidence of accidents. Machine learning's pattern recognition can also assist in final production inspection before and after packaging to reduce human intervention and help deliver perfect orders.

Customer experience improvements

The combination of deep analytics, IoT sensors and real-time monitoring improves supply chain visibility, which, in turn, enables supply chain optimization. While consumers may not know the terminology, they expect whatever technology is working behind the scenes to enable on-time delivery. Supply chain professionals may have some version of visibility in place in their operations, but given the current drive toward greater logistical flexibility, most companies need major improvements in these areas. The combination of available technologies embodied in machine learning can bring real-time visibility to supply chains and enable companies to achieve their delivery commitments and transform the consumer experience.

Machine learning is helping to create supply chain optimization, and managers need to assess their supply chains for where these technologies can be most helpful.

In other words, supply chain managers need to evaluate how adding new capabilities can bring visibility and additional control to their supply chain and continue their efforts toward digital transformation for the benefit of their customers.

This was last published in September 2018

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