machine learning for supply chain

Global supply chains are becoming so complex that cutting-edge computing is quickly finding a home in this industry. Specifically, machine learning in the supply chain is carving out a place as a must-have technology of the future. It’s already used for forecasting, probability mapping, route planning, quality control, and much more.

As more technologies enter the new age, machine learning for supply chain optimization will likely be the all-governing tool used to adapt for efficient business operations.

A Brief Look: Machine Learning in Supply Chain

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, but it’s also 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. nVision transportation management system provides the ability to track real-time data and manage all your supply chain operations in one place.

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.

Advantages of a Supply Chain Driven by Machine Learning

There are several uses for machine learning in supply chain management and there advantages are clear. These are:

  • Decreased Costs: Predictive maintenance, effective transportation routes, and optimal inventory levels all result in considerable cost savings.
  • Increased Customer Satisfaction: Happy consumers are the result of prompt deliveries, better product quality, and quicker response times.
  • Enhanced Resilience: A more seamless flow of commodities is ensured and downtime is reduced when disruptions are anticipated and mitigated.
  • Data-Driven Decision Making: Machine learning gives businesses the ability to make well-informed decisions throughout the whole supply chain by substituting data-driven insights for conjecture.

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.

Is your supply chain benefiting from advancements in machine learning? As the modern supply chain goes more digital, machine learning becomes the driving force behind it. At nVision Global, we recognize the demand for this technology and are putting it to work to help our clients build robust supply chains that give them the edge. Visit our website at nvisionglobal.com.