Case Study - Improving workforce agility for a logistics company
Al Cranswick
July 7, 2025
Improving workforce agility so a logistics company can say “yes” more often to new customers
Discover how our expert reduced staff training delays by 40% for a logistics company.
Executive Summary
A logistics company was experiencing delays in staff training and re-certification, which was affecting their ability to take on new customers and maintain compliance. Working together over 9 months, we helped identify the underlying causes and implemented practical solutions that reduced training time by 40% and decreased declined orders by 27%.
The Challenge
Company Profile: A logistics company with 50 locations struggling with inconsistent training timelines across their network.
The Problem: The company had solid training infrastructure but couldn't understand why some locations consistently took longer to get staff certified than others. This created operational challenges including staff shortages and missed opportunities to serve new customers. In their competitive industry, maintaining excess staff at each location wasn't financially viable.
Key Issues:
Inconsistent training completion times across locations
Management had over 40 theories about root causes. but it would have taken them 12 weeks to test each of these theories, as their only performance metric was training duration.
Difficulty accepting new customer orders due to staffing constraints
Our Approach
We used a structured problem-solving methodology adapted from social impact work, focusing on identifying root causes and testing solutions incrementally. The 9-month engagement followed this process:
Day 1: Problem Definition We started by reframing the challenge from "delays in certification" to "delays in competence acquisition" - a subtle but important shift that recognised management's commitment to safety.
Month 1: Problem Decomposition
Broke down on-boarding delays into component parts
Conducted interviews across all organisational levels
Collected data on training metrics and outcomes
Month 2: Root Cause Analysis
Used machine learning to identify patterns in the data and potential causes
Prioritised the potential causes based on the quantity of associated missed opportunities to accelerate trainee competence
Developed performance metrics that allowed tracking of underlying performance improvements monthly instead of quarterly
Months 3-6: Monitoring, Evaluation and Learning
Dissected performance data monthly with the management team and designed solutions by talking to front line staff from the teams with the top and bottom performance.
Piloted different solutions across locations.
Measured results and scaled successful approaches.
Months 6-9: Implementation
Rolled out the most effective initiatives across the organisation
Techniques Used: Monitoring Evaluation & Learning (MEL), Behavioural Economics, Machine Learning (ML), Natural Language Processing (NLP), Statistical Process Control, Systems Mapping, Interactive Dashboards.
Results
Customer Orders: 27% reduction in orders declined due to staff shortages
Delivery Performance: 8% improvement in on-time delivery rates
Competency Acquisition Time: Reduced from 12 weeks to 7.2 weeks on average (40% improvement)
Safety: No negative impact on safety performance
Return on Investment: 18-month payback period
Key Takeaways
When This Approach Works Well:
Complex operational problems with multiple potential causes
Situations where existing data doesn't clearly point to solutions
Organisations juggling competing priorities from different stakeholders
What Made the Difference:
Systematic approach to identifying and testing root causes
Regular engagement with leadership team on performance data
Focus on practical, measurable improvements rather than theoretical solutions
Next Steps
If you're facing similar operational challenges, a structured problem-solving approach might help. We'd be happy to discuss:
Schedule a consultation to discuss:
Initial assessment of your specific situation
Tailoring the approach to your organisational context
Realistic timeline and resource requirements
About the Consultant: 8+ years of strategy consulting experience specialising in operations improvement, with expertise in data science, monitoring evaluation and learning (MEL), behavioural economics, and systematic problem-solving approaches.
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