Plan Better Truck Routes

It’s not so long ago that route planning, fleet optimization, and driver allocation relied almost exclusively on the guile, instincts, and experience of operations managers and dispatch personnel.


Long-range forecasting was theoretical at best, and the tools used static data and couldn’t handle the inevitable adjustments required by the dynamics of day-to-day operations. This is changing fast. While operations and dispatch personnel’s skill and knowledge will always be vital, the tools available today—long-range trend analysis and planning—make for better, faster, more cost-effective decision-making. This is changing the competitive landscape.


Analytics and data management address the value of integrating data across the enterprise using internal structured and unstructured data and external unstructured data. It also lets you convert Big Data into practical, contextual facts and figures, by unlocking the value in your organization’s data repositories. This connected intelligence helps you proactively manage information-related business risk, enhance customer experiences, and optimize business performance to create competitive advantages and discover new market opportunities.


Data is helpful only if it’s usable to make better decisions. The challenge all logistics companies have historically faced is the relevancy of long-range trend data to short-term operational decision making. With the tremendous evolution in analytical tools and ability to integrate decision rules and processes into tool sets, the foundation to create far more meaningful links between long-range planning and real-world agility is changing the paradigm.


HP was asked to look at ways to build route plans that would more efficiently use driving resources within a very large service area. There were a number of required business rules, including company and DOT driving constraints. It was very clear from the beginning that longer planning, using seasonal, volume, and trend analytics coupled with real-time data captured from multiple sources—sales, operations, maintenance, weather forecast, road conditions—could satisfy the demand for capacity. And direct cost and resources needed to meet those demands could be optimized.


Like most logistic issues, the problem has three dimensions:
· Who—or a combination of several who’s in the case of team loads or tag, relay loads—should carry a given load?

· Whatorder should they schedule loads?

· When—in this case—is the optimal sequence of geography, load balancing, customer delivery, and pick up windows?


To meet this challenge, alternative itineraries and resource maps were generated for each driver or load, derived through a rules-based decision support engine—and the optimal one was selected for each.


To continue this discussion and hear from experts at HP make sure to come along to eft's Logistics CIO Forum happening in Austin, April 20-21. 


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