From route planning to carrier management, the foundational tasks involved in running smooth supply chains have existed as long as many shippers in the industry. The purposes of these processes haven’t changed over time, but the advancement of technology—automation, machine learning (ML), artificial intelligence, and beyond—has changed the game in terms of efficiency, ease, and strategy.
One of the most discussed additions to supply chain tech is predictive analytics, an innovation which uses mathematical models and statistical algorithms to make forward-looking supply chain predictions. Combining historical data with the speed and processing power of a machine, predictive analytics help shippers close visibility gaps, reduce uncertainties, and mitigate industry pain points.
Make supply chain data work harder
Predictive analytics is a category under the larger umbrella of business intelligence—the process of collecting, analyzing, and presenting data in a way that helps organizations make strategic business decisions and avoid future challenges. Like other supply chain processes, forecasting isn’t new. But when enhanced with the capabilities of machine learning, it’s even better at anticipating trends, identifying potential opportunities and risks, and enabling real-time adjustments to stop issues before they occur.
Machine learning enables a level of analytics that is often desired but difficult to achieve due to the sheer volume and complexity of supply chain data. Able to process vast amounts of information at high speeds, predictive analytics help shippers make the most of their data, highlighting patterns, correlations, and predictions about the future—an especially important feature for global companies managing multi-modal shipments.
For example, when it comes to demand and inventory management, predictive analytics could analyze historical data, market trends, and other variables to make forecasts, including anticipating low inventory levels or surges in demand. This can help reduce stockouts and improve resource allocations. These analytics could even predict maintenance needs before fleet breakdowns occur or use customer data to better anticipate behavior. The list goes on.
Enhance the power of a TMS
With a robust transportation management system (TMS) like Honeybee TMS in place, disparate supply chain activities are centralized for better visibility, easier management, real-time tracking, automation, analysis, and more. The right TMS reduces costs, saves time, improves processes, and even supports customer satisfaction.
Now, take all of that organizational power and add a computer that can turn terabytes of data into helpful future-looking information in milliseconds. That’s why predictive analytics are becoming a must-have component of TMSs—and why leading logistics providers are already adding this capability to their offerings.
A TMS with predictive analytics can analyze billions of data points related to speed, routes, weather, traffic, and beyond to provide predictive ETAs and anticipate risks like delays, disruptions, or bottlenecks before they occur. From there, the software can make quick pivots to optimize routing, carrier selections, and load allocations, avoiding delivery delays and additional costs.
Predictive analytics provide a competitive edge
One of the most beneficial aspects of a predictive analytics tool is its ability to track KPIs, providing shippers with information they can use to make strategic decisions and refinements to current processes. At CTSI-Global, integrating predictive analytics into our powerful Honeybee TMS helps our customers close even more visibility gaps and relieve the uncertainties that can come with global supply chain management. In a competitive marketplace, these insights help businesses identify subtle trends and patterns, predict outcomes, and make operational adjustments in time to avoid major costs.
It’s time to bring predictive analytics to your supply chain operations. Contact CTSI-Global to unlock vital strategic insights.
