In over a decade of building optimisers for the transport and supply chain sectors, we at Opturion have noticed a recurring, somewhat baffling paradox:
We consistently demonstrate massive potential for cost savings and asset utilisation, yet the “status quo” often remains the winner.
If the math proves you could save millions, why is the industry so slow to change? Does anyone actually care about underutilised assets?
The Victoria Case Study: A Reality Check
To illustrate, let’s look at a project we completed for a major retailer’s distribution network in Victoria. They managed a regional distribution centre, a network of stores and a fleet of self-employed drivers.
We ran the data. We looked at capacity, operating costs, delivery windows, and store accessibility. Then, we compared their “Manual Plan” with our “Optimised Plan” across three seasons (peak, low, and average).
The Results
Without changing a single physical asset—simply by re-allocating deliveries to trucks and refining routes—we identified:
- 20% reduction in the number of trucks required.
- 10% reduction in total operating costs.
How was this possible? The manual plan was failing to balance driver hours. Some drivers were finishing in under 8 hours (but getting paid for 8), while others were hitting 11+ hours and racking up expensive overtime. In a market plagued by driver shortages, this inefficiency is a double-edged sword: you lose money and your best drivers, who prefer the consistency of a 10-hour workday.
Why Human Planners Hit a Wall
The root cause isn’t a lack of talent; it’s a lack of “mental bandwidth.” A human planner simply cannot hold all these variables in their head simultaneously:
- Real-time routing and traffic
- Complex loading and access constraints
- Dynamic delivery windows
- Driver workload balancing and reloading
When the problem has ten dimensions, a human can usually only solve for three. Mathematical modelling solves for all ten at once.
The “Invisible” Inefficiencies
During this study, we uncovered two specific “status quo” habits that were burning cash:
The Habit |
|---|
The “Reasoning”
The Reality
Breaking Pallets
“We can fit more in the truck if we hand-stack.”
It took 90 minutes to load. If they loaded the pallets whole (taking 5 mins), the time saved would often outweigh the extra capacity gained.
Fixed Mon-Fri Delivery
“It’s simpler and avoids weekend overtime rates.”
No one had actually calculated the trade-off. Adding weekend deliveries might cost more in labour, but the savings in fleet size and store stock-outs often justify the premium.
The Framework for Change
Optimisation (or the “Digital Twin”) allows you to categorise your operations into three buckets of change. This is how we helped this retailer formulate a smarter, more profitable contract:
- Fixed Constraints (Hard to change): Warehouse and store locations, back-of-store capacity.
- Tactical Wins (Easily changed): Delivery frequency, loading practices, and daily routing.
- Strategic Shifts (Long-term): Fleet composition and driver contract structures.
The Bottom Line: If our analysis of this one retailer is any indication, a holistic approach to these trade-offs delivers far more than a 10% cost reduction. It delivers a resilient, scalable business model.
