Industrial Supplier Responds to Route Variance in Real Time

The Challenge

An industrial parts supplier operated 350+ routes daily across 15 carrier partners, serving manufacturing plants and repair shops. On-time delivery was 89%, which seemed acceptable but masked significant variance. Some carriers and routes performed consistently; others varied wildly.

Planners had no visibility into what drove variance. Was it the carrier? The load profile? Weather? Traffic patterns? Distance? When a route fell behind, the response was reactive—expedite calls, customer negotiations—rather than predictive.

The company was making carrier and routing decisions with incomplete information, spending time and money on expedites that might have been preventable, and missing opportunities to shift loads to better-performing partners.

The Approach

We integrated their TMS with historical performance data and external data sources (weather, traffic patterns, facility performance). We built models that correlated route performance with carrier, load profile, geography, seasonality, and other variables.

The result was a clear picture of which factors actually predicted on-time performance and which ones didn't. We then built:

• Route Performance Dashboard: Visibility into variance drivers for every route segment and carrier partner
• Predictive Analytics: Real-time probability of on-time delivery at dispatch time, accounting for current conditions
• Optimization Recommendations: Alternative carriers and routes with predicted performance impact
• Carrier Scorecards: Detailed performance metrics by carrier, load type, lane, and time-of-week

We went live with analytics-only access for 30 days while planners validated the data and learned the system, then enabled actionable recommendations.

The Results

Within 6 weeks, planners were using route intelligence to make dispatch decisions. They shifted loads to better-performing carriers on high-variance lanes, reducing expedites by 31%. On-time delivery improved from 89% to 93.8%—and more importantly, variance dropped significantly.

The analytics revealed that three of their 15 carrier partners were significantly underperforming on specific geographies. The company renegotiated terms with two and found alternative partners for the third. That single decision improved performance on 14% of their volume.

Planners spent 2+ fewer hours per day managing exceptions and making reactive expedites. Instead, they used that time for proactive load consolidation and capacity planning.

The Quote

"We were making carrier decisions based on cost and gut feel. Waymark gave us the actual performance story—which carriers really deliver on time, which lanes are risky, where our expedites are coming from. We've already renegotiated two partnerships based on that data."

— VP of Logistics, Industrial Parts Supplier

Key Takeaways

• ✓ Data reveals hidden performance patterns. What looks like acceptable average performance often masks significant variance. Visibility into drivers enables targeted fixes.
• ✓ Predictive analytics shift decisions from reactive to proactive. Planners went from responding to delays to anticipating them and preventing them through better dispatch.
• ✓ Carrier performance intelligence drives strategic moves. Better data led to renegotiations and partner changes that compounded the operational benefit.
• ✓ Real-time variance detection cascades into faster decision-making. Early visibility into problems enables faster, more confident responses by everyone in the operation.