Can the same machine learning tools work for both retail and manufacturing businesses?

When it comes to tailoring machine learning tools for both retail and manufacturing businesses, there are some significant similarities, but there are also fundamental differences.

In retail, the vast majority of machine learning tools and processes are oriented toward sales and customer-facing initiatives. Companies utilize the immense power of machine learning to dig through data that allows them to sell, that boosts conversion and thus, profits. One excellent example that straddles the line between machine learning and artificial intelligence is pursuing customer outreach around shopping cart abandonment. The sets of tools that actively reach out to customers who have abandoned items in a shopping cart are often classed as artificial intelligence tools, but other tools that simply aggregate and analyze data to evolve human-driven systems are examples of machine learning applied to retail.

In manufacturing, the machine learning landscape looks quite a bit different. Machine learning applies to manufacturing and the production of physical goods in quite a few unique ways. Much of the value of machine learning in manufacturing is applied to the handling of supply chains. Machine learning will inform maintenance, repair and overhaul (MRO) processes, and other aspects of building, packaging or assembling discrete or mass production items. In other words, many of the most valuable machine learning tools in manufacturing are oriented toward the shop floor, aimed not at customers, but at building the perfect “smart factory” and improving physical processes. (This Forbes article is just one example outlining ten of the ways that machine learning is changing manufacturing quickly, and in fundamental ways.) By contrast, retail machine learning tools are mostly aimed at the “smart sales floor” and the bulk of commerce that now takes place online or through digital platforms.

With that being said, retail businesses can also use machine learning tools to handle physical processes, for example, inventory. In inventory handling, machine learning predictors can help retail companies to save enormous amounts of money by keeping only the inventory that they need available at a given time, and making warehouse and storage operations much more efficient. However, a major value of machine learning in retail is still focused on decision support for sales, on learning more about the customer based on deep data aggregation and analysis practices, on examining demographics and personal information and getting extremely valuable sales intelligence.

The bottom line is that, as a harbinger of coming strong AI, machine learning and deep learning tools are simply “smart.” They aggregate data and provide a holistic picture of some defined concept, whether it’s in a geographical, physical space or a digital environment. So different industries utilize the power of machine learning in different ways. The difference between machine learning in retail and machine learning in manufacturing is an evident example of how businesses pinpoint their needs and adopt machine learning technologies accordingly.