Global supply chains are becoming so complex that cutting-edge computing is quickly finding a home in this industry. Specifically, machine learning is carving out a place as must-have technology for supply chains of the future. It’s already used for forecasting, probability mapping, route planning, quality control, and much more.
And as more technologies join the new-age supply chain, machine learning will likely be the all-governing tool used to adapt and optimize them.
A brief look at basic machine learning
Even in its earliest stages of implementation, machine learning is making waves in supply chains. Advancements in how we manage logistics have taken shape at every level. Here are a few simple examples:
- In warehousing operations, machine learning unlocks new levels of stock predictability and automation. Not only can software identify low stock and complete reorder actions, it’s learning trends to facilitate warehousing around those patterns.
- Instead of siloed technologies for different areas of the supply chain, machine learning ties them together. Machine learning is the thread connecting entire logistical networks, including production workflows, inventory, freight logistics, and beyond.
- Machine learning evolves with new information. It’s the solution to accelerated logistics and scaling supply chains. Using predictive analytics, businesses can project growth, identify weaknesses, trim costs, and improve communication across supply chains.
Innovations such as these general examples have contributed to a supply chain that’s revolutionizing global trade. And with consumer demand only growing, machine learning is becoming an essential piece of tomorrow’s supply chains. It’s the only thing to keep up with trends like real-time inventorying, digital payments, and very real logistical headwinds.
The key to a future of precision logistics
A recent article by Forbes details the many ways in which we’ll see machine learning shaping tomorrow’s supply chains. From analyzing gargantuan data sets to save pennies, to solving multi-faceted problems in minutes, machines do what humans cannot. The more variables dictating global supply chains, the more we’ll rely on machines to make sense of them.
Not even the most skilled logistics manager can account for every variable in a supply chain. Feeding these variables into a machine learning algorithm yields insights never before possible. Some mind-boggling examples of the potential include:
- Given trucking data, weather predictions, freight specifications, and more, an algorithm can predict what time freight will arrive at a destination thousands of miles away — in real-time, almost to the minute.
- Given the various rates, fees, duties, and other cost data, an algorithm can calculate exact costs for freight moving around the world, regardless of delays, stops, warehousing costs, and other variables.
- Using real-time data from an eCommerce site, an algorithm can execute actions to adjust inventory levels, order stock, combine shipments, and adjust logistics schedules.
The power of machine learning to react to variables and automate smart responses takes human effort (and error) out of the equation. As supply chains grow more complex and require more involvement, machine learning will continue to do what humans increasingly cannot.
Bountiful opportunities for efficiency
Machine learning touches every part of the supply chain, but supply chain management benefits most fruitfully from it. Algorithms may give way to action, but the data gleaned from smart programming funnels back to supply chain managers. Tighter logistics, lower costs, increased profitability, smart procurement, streamlined distribution, and fewer errors will quickly cement machine learning as must-have technology for third-party and other logistics providers.
More examples of machine learning’s prowess come to light each day. The supply chain is stretching continually outward and becoming more complicated. Now’s the time to investigate machine learning in preparation for the future.