Depending on what industry you work in, Artificial Intelligence (AI) might already be a part of your day-to-day process or it might seem like a scary sign of things to come, a predecessor to jobs being lost to computers as technology streamlines some of us right out of our own professions.
Cast aside your fears if you are part of the latter opinion. The coming of AI to the supply-chain industry is the potential for improvements across the board that can reduce stress on professionals, largely eliminate a huge amount of unnecessary errors, and tighten up scheduling to a level never before seen.
Sound like a magic bullet? It’s not quite that. Human involvement is still crucial and poor planning and execution can still send your supply chain crashing to a halt. But before we delve into how to use this new tool, let’s talk about what it is and what it actually does.
Mastering Machine Learning
AI is a broad field with many sub-categories. From the outside it might conjure up images of C3PO from “Star Wars” or the chilling HAL 3000 from “2001”, the real strides that have applications in supply-chain management come from a subset of AI known as Machine Learning (ML). ML refers to the process by which computers can “learn” massive amounts of information in a fairly short amount of time and using complex algorithms, begin to pick out patterns in that information. These patterns are the type of thing that data analysts used to look for, but even with their combined brain power, it could take them months and years to recognize what a computer can see in minutes and hours. Now, the ML isn’t designed to tell you what to do with that data, although there are available resources that can, but it delivers it in a format that is foolproof and fast, with insights that might have otherwise taken substantial time to arrive at.
Supply-Chain Applications of AI
Without a doubt the most valuable possibility for ML in supply chain is to predict the future demands for production, easily one of the most challenging parts of the process. Given enough data points, it is possible for ML to take into account factors that previously employed methods could not track or quantify. That’s just scratching the surface, however. ML can also improve supplier delivery performance, minimized supplier risk, and reduce freight costs by really narrowing down exact amounts that need to be moved. Identifying synergies between multiple shipper networks in an environment approaching real-time can be a game-changer for keeping everyone’s costs at a minimum. Another great asset that ML delivers is visual pattern recognition, which can be transferred to applications in terms of warehouse maintenance and physical inspection across your entire supply-chain network. ML can help automate quality inspection and isolate products that are notably damaged or own. The number of applications that can be harnessed is not set in stone, but rather growing with every passing week as more and more companies integrate AI into their day to day.