Editor’s note: This is the tenth article since May 20, 2026 in an ongoing series by Dr. Andrew Maxwell, the Bergeron Chair in Technology Entrepreneurship in the Lassonde School of Engineering at York University. Every week – and occasionally every other week – we’ll present a new article by Maxwell, in a series whose wide-ranging and incisive themes encompass: Canada and innovation policy; productivity and industry; innovation frameworks; AI and higher education; research and intellectual property; technology adoption; entrepreneurship and commercialization; universities and higher education; entrepreneurship education; and AI and the future of work. 

This is Part 3 of a three-part article. Part 1 was published here on July 15 and Part 2 here on July 22.  

Manufacturers are not bad at innovation. They are exceptionally good at optimization.

And that is precisely the problem.

For decades, the industrial operating system rewarded efficiency, yield, cost control and predictable execution.

The organizations that survived built cultures of discipline, statistical control and operational rigor. Lean systems and Six Sigma – pioneered in firms such as Motorola and later institutionalized at General Electric – drove extraordinary performance gains.

Variance was the enemy. Control was strength.

That logic still works – if the world is stable.

But today’s manufacturing environment is shaped by electrification, digitization, geopolitical fragmentation, sustainability demands, AI integration and business model shifts that redefine entire ecosystems.

And here is the uncomfortable truth: Most manufacturing organizations are structurally designed to improve yesterday’s model – not reinvent tomorrow’s.

This is not a cultural failure. It is a design outcome.

The performance engine crowds out the innovation engine

Every serious manufacturer has a performance engine. It is measured, disciplined and relentlessly optimized. Dashboards track yield, uptime, defect rates, margin per unit.

Careers advance through operational excellence. Promotion signals are clear.

The system asks: How do we make this process better?

Innovation asks a different question: Should this process exist at all?

That second question is destabilizing. It introduces ambiguity, uncertainty and temporary inefficiency. In organizations where predictability has been equated with competence, deviation feels dangerous.

Over time, the performance engine becomes dominant. The innovation engine – if it exists –  becomes episodic, peripheral or symbolic.

Improvement flourishes. Reinvention struggles.

Failure is expensive – so experimentation becomes risky

In manufacturing, failure is visible. Scrap accumulates. Lines stop. Customers complain. Safety risks escalate. The cultural response – appropriately – is to eliminate failure wherever possible.

But here lies a critical distinction that many firms blur: Operational failure must be minimized.
Experimental failure must be designed and harvested.

When those two are treated as identical, innovation slows dramatically.

Engineers become cautious about proposing early-stage ideas. Managers hesitate to sponsor pilots that may not produce immediate return on investment. Projects are over-engineered before being tested. Ideas surface late – when they are already expensive.

In digital startups, failure is data.

In factories, failure is waste.

Unless leaders explicitly separate operational discipline from experimental learning, the organization defaults to caution. And caution, over time, becomes inertia.

The deeper constraint: the business model blind spot

Most manufacturers believe they struggle with technological innovation. They don’t.

Process improvements happen constantly. Materials improve. Automation advances. Efficiency increases year after year.

The deeper struggle is business model innovation.

Traditional manufacturing thinking focuses on improving performance within an existing commercial architecture. Better tolerances. Faster throughput. Lower cost per unit. Incremental enhancements.

But true market disruption rarely comes from performance improvement alone.

It comes from redesigning how value is created, delivered and captured.

Consider Dell Technologies. Dell did not win because it invented radically new computer components. It reconfigured the supply chain and revenue model – direct-to-customer sales, build-to-order manufacturing, negative working capital dynamics. The technological components were available to competitors. The business model was not.

Or take Tesla, Inc. Electric drivetrains matter. But Tesla’s advantage lies in vertical integration, over-the-air software updates, charging infrastructure, direct-to-consumer sales, and data integration. The factory is embedded in a redesigned ecosystem.

Even Magna International demonstrates how manufacturing capability becomes strategic when positioned correctly within a broader automotive architecture. Its modular systems, contract manufacturing capability and ecosystem positioning allowed it to influence flexibility and integration beyond pure production efficiency.

In each case, technology enabled the shift. But business model redesign created the advantage.

And this is where traditional manufacturers face structural difficulty.

Plant leaders are measured on cost, uptime and quality. Sales teams manage accounts. Strategy functions create plans. Very few roles are accountable for integrated redesign of the commercial architecture.

So technological improvements are embedded inside existing revenue logic.

Rarely does the organization ask: Should we serve different customers? Should we bundle services? Should we shift from product to platform? Should we reconfigure our ecosystem partners?

Without structural ownership of business model innovation, incrementalism dominates.

Distance from the market limits co-creation

Many manufacturers operate several layers removed from end users. They sell to original equipment manufacturers, Tier 1 suppliers, distributors or integrators. Feedback arrives filtered through specifications and procurement processes.

Innovation becomes specification-driven rather than need-driven.

The organization becomes excellent at fulfilling demand – less capable of shaping it.

Business model innovation, however, requires deep engagement with unmet needs, contextual frustrations and evolving use cases. When engineers and plant leaders rarely interact directly with end users, the emotional and strategic richness required for reinvention is lost.

Co-creation demands proximity. Distance reinforces incrementalism.

 Authority and incentives reinforce the status quo

Disruptive innovation often shifts performance metrics. It may prioritize flexibility over utilization, ecosystem position over margin per unit, speed over efficiency.

But if leaders are evaluated primarily on operational metrics, their behavior aligns accordingly.

This is not a failure of imagination. It is rational adaptation to the incentive structure.

Over time, the organization becomes brilliantly calibrated to deliver continuity – and structurally resistant to discontinuity.

So what would it really take?

If innovation is to move beyond rhetoric inside manufacturing organizations, three structural realities must be confronted: space, slack and signals.

First, business model innovation requires protected space. It cannot thrive inside governance structures designed for quarterly optimization. It must have authority, executive sponsorship and the ability to question commercial assumptions without being immediately evaluated through operational metrics. This creates internal tension – but without it, architectural redesign never happens.

Second, innovation requires slack. Traditional manufacturing systems are engineered for maximum utilization and minimal redundancy. That discipline creates efficiency. It also eliminates cognitive and resource bandwidth for exploration.

Slack in time, capital and talent is not waste – it is optionality. Without it, every experiment competes with today’s production targets, and production will always win. Leaders must explicitly legitimize slack as strategic investment in adaptability.

Third, incentives and recognition must evolve. If experimentation carries career risk, it will be avoided. If failed pilots quietly disappear, learning is lost.

Organizations that take innovation seriously distinguish between disciplined experimental failure and operational breakdown. They surface lessons publicly. They reward teams who invalidate flawed assumptions early. They align promotion signals with long-term strategic positioning, not just short-term throughput.

None of this requires abandoning operational excellence.

In fact, the winners will be those who can run tight factories and loose imagination simultaneously.

The lesson from firms like Dell Technologies, Tesla, Inc., and Magna International is not that manufacturing discipline is irrelevant.

It is that discipline must be embedded inside a broader architectural vision.

Technology without business model innovation refines the present.

Technology with business model innovation reshapes the future.

 The final diagnosis

Traditional manufacturers struggle with innovation not because they lack intelligence or capability.

They struggle because they were brilliantly engineered for stability.

Efficiency under stability creates strength.
Efficiency under volatility creates rigidity.

If leaders want innovation, they must consciously redesign parts of the system to tolerate ambiguity, introduce slack, reward learning and legitimize business model experimentation.

That is uncomfortable. But so is irrelevance.

The future of manufacturing belongs to those who can optimize today – while redesigning tomorrow.

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