Computers that manage your supply chain activities are processing the data that controls your supply chain. It’s been part and parcel of supply chain management for decades. But it wasn’t till the last decade that companies have come to understand that if they stored that data they might be able to apply some analysis to it and learn how to optimize their operations. The challenge has been to find ways to consume the huge amounts of data and find things that could make a difference in real ways, a task far beyond what us humans can handle.
The advance of smart machines is making it possible to sort through the ever-accumulating transactions in ways previously thought impossible, and in speeds unimaginable till recently. Here are just a few of the ways you can put machine learning to work on improving your supply chain functions and get ahead of the competition.
Spotting shipping damage has been the domain of dock workers who look at packages and pull out those that look damaged or broken. But in operations with large volumes of deliveries it’s common to miss small defects that mount up to expensive mistakes. One of the things that machine learning has brought to practical use is the ability to recognize patterns - visual pattern recognition. Cameras trained on inbound operations and connected to machines like IBM’s Watson can learn what a normal undamaged item looks like and spot any that differ, alerting workers to take appropriate action and initiating corrective actions.
All that data generated by EDI, inventory, and ERP transactions may be too big and complex to manually review but machine learning is bound only by the smarts of computers tasked with gathering and reviewing it. There’s lots to be learned by the historical information collected over time but machines can integrate data on conditions across a company’s supply chain from sources like weather, news, and social media. That combination can be put to use in predicting what will be needed when and where.
The products you sell are only as good as what you receive from your suppliers and assuring your suppliers comply with the guidelines you’ve established with them can either keep your customers happy or lead to declining sales and increasing expenses. Machine learning can assimilate the rules you’ve agreed upon and track how your suppliers are performing against them. In companies that have large numbers of suppliers with complex agreements it’s easy for humans to miss small issues that can build up to more serious problems. Smart machines can monitor and apply intelligence to what they detect and issue appropriate recommendations as situations demand, making small corrections continuously and avoiding large scale problems.
There are plenty of other ways that machine learning can be applied to supply chain operations. Companies should start with smaller initiatives and get accustomed to teaching their machines about their business. Smart machines can learn incrementally and make small and continuous changes over time.