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Case Study:

Dynamic Forecasting / Global Logistics Powerhouse

costly-sub-optimal-logistics-scheduling-teranalytics

Data analytics optimizes operation to minimize impact of market fluctuations

Global ground logistics company automates complex global scheduling with AI to drive maximum efficiency

Challenge:

Customer Demand Fluctuations Made Driver Scheduling Difficult and Costly

Main US distributor manages a vast fleet of trucks and thousands of drivers whom they schedule on a monthly basis to deliver its customers’ freight. Within a fast moving economy, the company needed a systematic methodology to create and evaluate static schedules, the manual scheduling process being too slow and mostly reactive. Specifically:

  1. Customer demand is seasonal: need to be able to identify shipment patterns that change by customer, by hub, by month, or by quarter. The extensive customer base made this task onerous.
  2. Manual scheduling impacts profitability: The fluctuation of demand throughout the year resulted in a mismatch of trucks and drivers versus shipping needs. The risk was to incur heavy costs either because of suboptimal truck loads or because of the necessity of hiring expensive contract drivers to fill gaps
  3. Errors propagate and amplify throughout the network: a sub-optimum schedule on one leg of the network has rippling effects throughout the entire network and can generate significant cost increases.

Solution:

Teranalytics scheduling algorithm guided by industry experts

  1. Initial Optimization: Teranalytics adapted its network optimization algorithms to account for key the company’s constraints: varied hours of operations at different stations; variable truck load capacities by route; shipment restrictions and carry-over conditions; service level implications; route balancing; truck repositioning; and more.
  2. Conversion from Dynamic to Static Schedules: Teranalytics created a tool that continually runs iterative statistical analysis of shipment patterns, replacing manual, ad-hoc schedules with data-driven predictable and repeatable static schedules that move the same freight volume at lower costs.
  3. Extensive and Interactive Solution Validation: The company validated Teranalytics’ solution by evaluating an extensive matrix of metrics, including the total loaded and empty miles traveled through the network, proportion of remaining ad-hoc drivers, service failure percentage, truck load factors, and more. Teranalytics created real-time interactive dashboards, enabling the management team to evaluate high level views or drill down for leg-by-leg examinations.

Results:

Significant cost savings gained from proactive planning, instead of reactive scheduling

With the help of Teranalytics, the company is now able to:

  1. Quantify and optimize scheduling throughout their global ground transport network, resulting in better utilization of their own drivers while minimizing the need for more expensive contract drivers.
  2. Automatically and regularly create data-driven static schedules that adapts to customer demand and capacity variables.
  3. Seamlessly balance and reposition transport legs to drive efficiency. Significantly reduce costs associated with hiring expensive third party contractors by better utilizing its own drivers.

Client Feedback:

“Teranalytics has cured our ongoing scheduling headache. We trust our new scheduling system to replace a labor intensive and not scalable manual work with an effective approach that keeps our customers delighted and our company increasingly profitable.”

— Chief Operating Officer