An ant scaled up to the size of an elephant could carry up to 300,000 kg, and run faster than Mach 1.5 speed – faster than the speed of sound. This would allow it to carry twice the maximum payload of the Boeing 747-8F, while also doubling its speed. With the highest power-to-weight ratio of any creature on Earth, ants are engineering marvels of efficiency and strength.
However, if you were to scale that ant up to elephant size, its exoskeleton would collapse under its own weight due to the square-cube law. What works brilliantly at small scale becomes a liability when magnified.
How does this have anything to do with the supply chain? It holds a mirror to a fundamental challenge plaguing modern logistics: the assumption that what got you here will get you where you want to be. According to Peter Jones, Managing Director of leading Australian supply chain consultancy Prological, this “ant to elephant” problem is crippling businesses across the sector.
“We see it everywhere,” Jones explains. “Successful businesses that have grown organically but never gone back to re-engineer the skeleton that the whole thing sits on. Success becomes the enemy.”
This pressure to keep pace can drive businesses toward the most visible solution: automation. But without understanding where you currently stand, how can you chart an effective course forward? It’s like trying to navigate without knowing your starting point.
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The Mirror Test
When businesses rush toward automation, they create a dangerous blindness in supply chain management. Businesses are investing millions in sophisticated technology without first understanding what they’re automating – or whether their existing operations can support the transformation.
“Whatever automation you introduce will reflect the business it’s implemented into,” Jones warns. “If you automate chaos, you get faster chaos.”
This reflection problem is particularly acute as businesses pivot rapidly between operating models. For instance, companies that spent decades perfecting store network distribution suddenly find themselves managing direct-to-consumer fulfilment. The skills, processes, and cultural norms that made them successful in one model can become liabilities in another.
“We’ve seen various media reports over the last year of big-name brands making expensive mistakes,” he notes. “The fundamental question businesses need to ask themselves is: can you confidently say you know what the next 8-10 years will look like at an operational level?”
Most can’t. Yet they’re making automation decisions based on current pain points rather than future operational realities.
The allure of a silver bullet is powerful. Vendors present compelling case studies showing dramatic efficiency gains and cost reductions. But these success stories often gloss over a crucial detail: the baseline against which improvements are measured. A business operating at 60% efficiency will see different returns from automation than one already running at 85% efficiency.
“Businesses need to benchmark automation against their own business case, not vendor presentations,” Jones emphasises. “The real baseline should be an optimised version of your existing capability, not your current chaotic state.”
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When Success Becomes the Problem
The ant-to-elephant analogy is evident in supply chains around the country. One such example is M3 Logistics’ recent transformation of its warehouse operations. The third-party logistics company had built a thriving business across three warehouses, but as volumes grew and customer demands became more complex, they needed to evolve their operations.
What had worked brilliantly at smaller scale – multiple facilities, distributed inventory, flexible but informal processes – was showing strain under increased complexity. The business had simply evolved organically, adding space and processes wherever they could accommodate growth.
Working with M3 to help them support larger volumes, Prological began with a comprehensive maturity assessment, which revealed that the biggest problems weren’t technology or automation-focused, but structural ones that could be solved through intelligent re-engineering.
The assessment uncovered opportunities to dramatically improve space utilisation and operational flow by consolidating operations into a single facility. When Jones suggested this approach, the initial reaction was scepticism. As Mal Stanton, Owner of later admitted: “When you first told me you could get all three warehouses into one, I thought you were joking.”
By re-engineering the operational skeleton rather than simply scaling up existing processes, M3 eliminated daily product transfers between sites, reduced supervision layers, and created a foundation for future growth.
“If we go outside this office and look out the window at the warehouse, everything’s in there, and I’ve still got space for growth,” Mal said.
The Hidden Economics of Optimisation
M3’s dramatic improvements came not from cutting-edge technology but from intelligent restructuring of existing operations, which points to a gap in how other businesses evaluate their modernisation options.
“We often find ways for clients to invest significantly less but deliver the same outcome,” Jones observes. “A small investment combined with optimisation might reduce operating costs by 30%, while a large automation project might yield 40%. The question is whether that extra 10% justifies the additional investment and complexity.”
The mathematics matter enormously when building business cases. Unconsidered automation proposals attribute 100% of projected benefits to new technology, when careful analysis might reveal that only 75% comes from low-cost optimisation and only 25% from the technology itself. This misattribution drives over-specification and inflated expectations.
Rethinking Business Architecture
Incremental changes will fail if forced at scale. M3’s success required more than warehouse consolidation; it demanded rethinking inventory management, customer communication protocols, and operational workflows. The company emerged stronger and more resilient, particularly valuable during post-COVID disruptions when storage demands increased while throughput volumes fluctuated.
Jones sees this pattern repeatedly.
“Most businesses that need consulting help aren’t basket cases. They’re good businesses that want to become great. But getting from good to great can require redesign, rather than iteration,” he says.
Rather than asking which technology to implement, businesses should first examine whether their operational foundations can support added complexity. Many companies would benefit more from structural improvements than sophisticated systems.
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Sequencing Success
Smart businesses are discovering the importance of investment sequencing. Establish solid operational foundations first, then layer on appropriate technology to avoid the common trap of amplifying the same inefficiencies through automation.
M3’s journey demonstrates that stepping back to examine underlying structures often reveals more sustainable paths forward than chasing the latest technological solutions. The courage to rebuild rather than simply scale can be the difference between success and failure.
“You can’t just evolve from ant to elephant,” Jones concludes. “You’ve got to go back and re-engineer the whole structure.”
For businesses facing their own scaling challenges, the question becomes not whether change is necessary, but whether leadership has the conviction to address fundamental structural issues before pursuing automation solutions.
Peter is Managing Director and Founder of Prological Australia, Prological NZ and Cological. In January 2018, he also founded Prological Consulting UK.
While Prological has been successfully operating for close to 10 years, Peter has been an innovative supply chain consultant for over two decades. Before establishing Prological, he was also a founding partner of Logiworx consulting practice from 2002 to 2010.
