Clean Data, Lean Logistics

Modern logistics demands more than smarter routes:it demands clean, consistent data that reflects what happens on the street. This handbook explains how “data sanity” grounds planning and execution in reality so ETAs hold up, billing reconciles cleanly, and costs trend down. You’ll learn how to replace averages and assumptions with execution-time capture, close the loop between plan and actuals, and turn trustworthy data into daily operational advantage.

Key Takeaways from the Handbook

• Data sanity, defined: Accurate, consistent data across the order journey, from customer and order details to vehicles, vendors, and costs, so decisions, promises, and ETAs are grounded in truth.
• Execution-time capture: Replace spreadsheet estimates with in-moment capture of service, waiting, and billing times via driver or client apps; verify locations as they’re used.
• Location accuracy: Correct and lock customer locations; quantify drift and prevent wrong-door waste with advanced geocoding and continuous learning.
• Plan-vs-actual learning: Track and store mismatch as drift; feed insights back into the next plan so tomorrow’s routes inherit a better truth.
• Governance that scales: Surface exceptions in real time with control-tower alerts (email/WhatsApp/Slack), and roll up into living dashboards for weekly and quarterly reviews.
• Proven Results:
  • 12.5% higher driver productivity from accurate service-time capture.
  • 25% higher on-time delivery with precise customer windows.
  • 72% reduction in average geocode drift (2.25 km → 0.62 km), driving ~15% lower fuel spend and 10% fewer non-compliance fines.
  • 20% shorter delivery times fleet-wide and 25% higher utilisation; typical payback in 6-12 months (median: 9).

Transform Your Logistics with Locus

Locus implements a five-layer data framework: Identify, Capture, Learn, Standardise, Govern, so ERP, TMS, and analytics work as a single nervous system instead of loosely coupled limbs. Execution-time capture grounds masters in reality; uniform schemas keep data flowing without custom patching; and control towers convert exceptions into action. Most enterprises see measurable gains quickly, with implementation typically recovering costs within six to twelve months.

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Get the full handbook to diagnose data debt, implement a clean-data foundation, and convert accuracy into profit