Autonomous supply chain planning is neither pie-in-the-sky nor purely aspirational. Rather, it is the only viable response to today’s globalized, e-commercialized, omnichannel business environment, where organizations must cope with a constantly shifting deluge of information pouring into the enterprise from all corners of the marketplace.
There is no longer any way companies can manage supply chain planning by increasing the size of their planning teams. First of all, that’s not financially and organizationally manageable. Secondly, the combination of seasoned, yet simultaneously tech-savvy, talent needed for the job is in increasingly scarce supply. Thirdly, and perhaps most crucially, people, no matter how many or how smart, don’t have sufficient brainpower to deal with the scale of inputs and outputs in the modern supply chain —only machine learning can keep up.
Today, demand is sensed and expressed everywhere, 24/7—from myriad brick-and-mortar points of sale, scraping of consumer and influencer sentiment off social media, e-orders placed across Web and mobile channels, and so on. Decision making is equally complex: where to stage inventory, how to deliver, how much to replenish and from where and when… on and on.
The only way to do this efficiently and effectively—in a way that sustains margin, and delivers goods to customers at a level of service satisfaction that meets or exceeds their expectations—is to further mechanize and “intelligize” the process in the direction of full autonomy.
Autonomous comes from the Greek word autonomos, having its own laws. These laws—algorithms that improve their ability to self-govern, through ongoing “law-making” enabled by machine learning—are what are bringing automated supply chain planning into being.
Machine learning is the key. It’s not enough to just record, sort, and make available the reams of structured and unstructured data that constitute the contemporary business enterprise. Someone—or some “thing”—has to “think through” what’s happening, in as near real time as possible, to make what are seemingly impossibly complex decisions.
Machine learning—by exploiting the real-time data vacuumed up by companies and tapping the massive computing power in the cloud—makes it possible to not only improve process automation, decision making, and optimization beyond what supply chain software has already done, but to create an entire supply chain business model powered by machine intelligence.
Machine learning’s self-learning systems unscramble more data, detect deeper patterns and simulate outcomes in more detail, all at blazing speed. They help keep your dial tuned to the current business moment, to make informed decisions and capitalize on opportunities. Going forward, they help you assess and better respond to the range of potential scenarios with which every business must contend.
The world is only getting more interconnected, and demands more omnichannel. When a customer is poised to make a purchase, delivery or pickup date often drives the final decision. Autonomous supply chain planning, with machine learning at the steering wheel, helps you calculate, lightning-fast across a multi-echelon supply network, so you make a promise you can keep, at a margin—above cost to serve—you can live with, and close the sale.
Editor: To learn more about supply chain planning and optimization check out Dassault Systèmes DELMIA portfolio.
John Martin writes about technology, business, science, and general-interest topics. A former U.S. correspondent for The Economist (Science & Technology), he writes for the private sector, universities, and media, and can be reached at jm@jmagency.com.