In Q2 2025, manufacturing labor productivity grew 2.5% (the fastest pace in 4 years). And yet, that number could be higher as unplanned downtime still costs Fortune Global 500 companies roughly $1.4 trillion annually. These disruptions impact more than direct output — they drive workforce turnover and growing skill gaps, according to Deloitte.

Historically, manufacturers have tried to close the productivity gap with incremental fixes. But the limitations of aging equipment, data silos, and fragile supply chains can’t be solved with yesterday’s methods.

The path forward is a digital-first model of manufacturing process optimization — one that builds on Lean, Six Sigma, and Total Productive Maintenance (TPM) while using digital twins and real-time data to cut downtime, boost resilience, and accelerate innovation.

What is manufacturing process optimization?

Manufacturing process optimization is the discipline of improving efficiency, quality, and resilience by systematically refining how a factory operates. Traditionally, that’s meant applying frameworks like Lean to eliminate waste, Six Sigma to reduce variation, and TPM to maximize equipment uptime.

But in 2025, optimization goes further. Digital technologies now provide a level of visibility that stopwatches and spreadsheets never could. Digital twins replicate production environments in 3D, IoT sensors feed live performance data, and AI-powered analytics surface hidden bottlenecks. Together, these tools let manufacturers not only document and streamline workflows but also simulate changes before making them, measure results in real time, and scale improvements across sites.

Optimization still centers on reducing downtime, cutting costs, and increasing output. The toolkit has changed: digital-first models make these goals achievable with speed and precision, closing the productivity gap while building resilient operations.

Why manufacturing process optimization matters in 2025

The pressure on manufacturers isn’t just about keeping lines moving — it’s about keeping pace with a faster, more complex market. Customers expect quicker product rollouts, requiring shorter innovation cycles without compromising quality. Global supply chains remain fragile, making accurate forecasting and inventory control essential to avoid costly shortages.

At the same time, sustainability and compliance targets demand tighter control over waste, energy use, and emissions. And with skilled labor increasingly hard to find, manufacturers need better ways to onboard new employees quickly and extend the reach of their existing expertise through remote training and collaboration.

Process optimization isn't a nice-to-have anymore. It’s the foundation for resilience, helping manufacturers cut downtime, boost output, and adapt to challenges that traditional methods alone can’t solve. Beyond these common benefits, process optimization offers deeper value in 2025.

Reduced error rate

Because manufacturing process improvement involves rethinking proper equipment use and ideal process and worksite setups, you can naturally find a reduced error rate and less need for rework.

When practicing manufacturing process optimization, you have the right materials at the right stations, properly maintained equipment, and processes that efficiently use employee time. This combination can keep employees on task while lowering the risk of someone grabbing the wrong material or a piece of equipment malfunctioning.

Elevated product quality

Along with a reduced error rate comes more consistent quality and a boost in customer satisfaction. Beyond consistent quality, you can achieve elevated product quality through continuous process refinement.

Time is manufacturing's biggest constraint, limiting daily output potential. Optimizing processes frees up time for further improvements. Ongoing improvements enable elevated capacity — whether meeting increased demand or delivering higher quality products.

Reduced downtime for manufacturing equipment

Unplanned maintenance and equipment downtime grind production to a halt while costing Fortune Global 500 companies 11% of yearly revenue.

By optimizing equipment use and maintenance schedules, along with ensuring staff are trained on the latest equipment protocols, you can reduce the likelihood of an avoidable equipment failure. This helps avoid costly equipment downtime and enables more accurate production forecasting with fewer disruptions.

Decreased manufacturing and production costs

Time is money, especially in manufacturing. By streamlining existing processes to reduce time and material waste, and reducing the likelihood of equipment downtime, you’re able to ultimately decrease manufacturing and production costs. Like quality improvements, the right optimization methods drive continuous profitability gains.

Increased productivity

Naturally, as you streamline workstations and current processes, you will increase productivity. Fewer incidences of equipment downtime and maintenance will also help with productivity, as your team won’t be on hold while something is fixed.

As an added benefit, employee morale often improves with smoother production and fewer preventable issues.

Streamlined supply chain management

Supply chain management becomes difficult when you can't accurately forecast material usage or when materials are lost to inefficient processes and mistakes.

By streamlining worksites and processes and ultimately reducing materials waste, you’re able to more accurately forecast material needs in the coming quarters. This allows you to order in advance and avoid falling victim to shortages.

As a supplier, consistent production enables accurate delivery contracts with bulk purchasers.

Manufacturing process optimization methodologies

Manufacturers have relied on structured methodologies for decades. Frameworks like Lean, Six Sigma, and Total Productive Maintenance (TPM) provide proven ways to identify inefficiencies, reduce variation, and keep equipment running at peak performance. Each offers a different lens on improvement: cutting waste, controlling quality, or preventing downtime.

The most effective manufacturers don't rely on just one method. They combine these frameworks with digital technologies to expand their impact. Digital twins, IoT data, and AI-powered analytics transform traditional methods into faster, more precise tools, allowing teams to model workflows, test changes virtually, and monitor results in real time.

Lean manufacturing principles

Lean manufacturing is built on a simple idea: maximize value by eliminating waste. Practitioners have identified eight forms of waste that slow production and drive up costs:

• Transport: unnecessary movement of materials

• Inventory: excess stock that ties up resources

• Motion: wasted movement by workers

• Waiting: idle time between steps

• Overproduction: making more than demand requires

• Over-processing: doing more work than the customer needs

• Defects: products that require rework or scrapping

• Underutilized talent: failing to use employee skills and insights

Traditionally, spotting these wastes required walking the shop floor with a stopwatch or clipboard. Today, digital twins make the process faster and more accurate. 

By creating precise 3D facility models with real-time data layers, manufacturers can visualize value streams, identify bottlenecks, and map waste beyond what static spreadsheets allow. The result: a clearer picture of high-impact improvements and the confidence to act.

Six Sigma methodology

Six Sigma is designed to reduce variation and defects by applying a disciplined, data-driven approach to improvement. Its core framework — DMAIC (Define, Measure, Analyze, Improve, Control) — guides teams through a structured cycle:

• Define the problem and scope

• Measure current performance with quantifiable data

• Analyze to uncover the root causes of defects

• Improve by testing and implementing solutions

• Control to ensure gains are sustained over time

At its heart, Six Sigma depends on reliable measurement and rigorous analysis. Digital twins make the difference here. Dimensionally accurate facility models with live data overlays provide precise baseline measurements and visibility into process variation. In the Analyze phase, teams can dig deeper into root causes by visualizing flows, cycle times, or quality patterns within the twin.

Digital twins also transform the Improve stage. Instead of costly, high-risk trials on the shop floor, teams can run virtual “what-if” simulations to validate process changes before making them physical. This speeds up experimentation, reduces risk, and helps ensure improvements deliver.

Total Productive Maintenance (TPM)

Total Productive Maintenance is about maximizing equipment effectiveness by shifting from reactive fixes to proactive care. TPM engages operators, technicians, and managers — not just maintenance departments — in preventing breakdowns, reducing defects, and maintaining peak performance. The goal: zero unplanned downtime, zero accidents, and zero defects.

Digital twins expand the reach of TPM by providing a virtual environment for equipment monitoring and planning. 3D production floor replicas let teams inspect assets, track maintenance histories, and plan interventions without disrupting operations. When paired with IoT sensors, these twins become even more powerful — streaming data on temperature, vibration, or cycle times directly into the digital model.

This integration enables predictive maintenance. Instead of waiting for a machine to break, teams can schedule maintenance at the optimal time, preventing costly stoppages and extending asset life. Digital twins bring TPM's vision of "perfect production" closer to reality by reducing risk while boosting efficiency and reliability.

6 steps for optimizing manufacturing processes

Lean, Six Sigma, and TPM provide the structure for continuous improvement — but they weren't built for today's factory speed and complexity. A digital-first approach bridges that gap. By pairing proven methodologies with tools like digital twins, IoT data, and AI-driven insights, manufacturers can move from diagnosing problems after the fact to preventing them before they occur.

The following six steps outline a modern framework for optimization. Each builds on traditional best practices while showing how digital technologies bring greater precision, visibility, and scalability to the process.

1. Capture the current state with digital twins

Every optimization effort starts with a clear picture of how things work today. This means creating a complete, dimensionally accurate facility record — capturing actual shop floor conditions, not just floor plans. With a Matterport Pro3 camera, manufacturers can capture photorealistic, LiDAR-enhanced 3D scans that document equipment locations, workflow paths, storage zones, and utility connections in precise detail.

This digital model does more than preserve a snapshot in time. The model serves as the baseline for measuring improvements. By embedding performance data into the model — such as cycle times, downtime events, or energy use — teams can compare future states against a verified starting point.

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Because the scans are stored in Matterport’s cloud platform, they’re also easy to share across teams and sites. Central access ensures everyone works from the same information, reducing miscommunication and creating a reliable foundation for subsequent steps.

2. Analyze workflows and bottlenecks

Once the current state is captured, the next step is turning that model into insight. Digital twins let manufacturers spot bottlenecks, waste, and inefficiencies that aren’t obvious on the shop floor. Heat maps of foot traffic, material flow visualizations, and cycle time comparisons reveal production bottlenecks from delays or excess motion.

Because the model is accessible in the cloud, cross-functional teams can conduct virtual walkthroughs together. Engineers, operators, and safety managers can collaborate remotely, reviewing the same environment and embedding their observations directly in the model. Matterport Tags let teams attach contextual documentation — workflow instructions, safety alerts — exactly where needed, creating a living reference for analysis and action.

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This approach also makes prioritization easier. Teams can prioritize improvements by impact versus effort, directing resources to fixes with the greatest productivity, quality, or safety gains.

As Boaz Goldschmidt, VP of Business Development at Treedis, said: “A maintenance worker can pull out his mobile device, fire up the Treedis AR app, point it at a machine and see live IoT data, SOPs, or be navigated to ‘air conditioning unit number 26’ among a line of similar units.” This contextual visibility transforms process analysis from manual, after-the-fact exercises into real-time, data-informed decisions.

3. Design and test solutions virtually

Traditionally, improving a process meant trial and error on the shop floor — an expensive and disruptive approach. With a digital twin, manufacturers can prototype changes in a risk-free virtual environment before a single piece of equipment is moved.

Within the model, teams can:

• Reconfigure layouts to test material flow or workstation design

• Simulate new workflows to spot potential delays or collisions

• Validate improvements against baseline data to ensure gains are real

Matterport’s Measurement Mode makes these tests even more precise. Teams can check clearances, validate equipment placement, and confirm fit down to the centimeter. This reduces installation errors, prevents costly rework, and ensures "first-time right" process changes.

By running these simulations up front, manufacturers shorten planning cycles, cut costs, and move into implementation with confidence that the improvements will deliver measurable results.

4. Implement changes with precision

Designing solutions virtually is only valuable if they translate accurately on the shop floor. Digital twins make that possible by serving as the bridge between planning and execution. Installers and facility teams work directly from the model, aligning equipment placement, wiring, and utility connections with validated specifications.

On a Matterport webinar on Industry 4.0, Abhishek Srivastav, Principal Solutions Architect at AWS, explained how immersive digital twins give teams the ability to train workers more efficiently and understand how predictive maintenance and automation technologies keep operations running smoothly. This context ensures virtual improvements translate consistently to real-world execution.

See the complete discussion on Industry 4.0:

Siemens put this into practice by using Matterport for a major facility relocation. By capturing a digital twin of the new site and importing 3D models of machinery, the team could virtually stage equipment, rotate and reposition assets, and confirm measurements remotely with 99% accuracy. This avoided costly clashes on-site and cut down on cycles of rework — enabling Siemens to complete the move faster and with less risk.

For building owners and factory and facility managers, remote monitoring is important. It’s even better when you can understand the context of what is physically all around the location where an industrial IoT sensor is placed. Is it near a window, a compressor, or any type of machine that could be giving off heat? That context is much more clear and insightful in a digital twin than with a simple 2D dashboard.” – Alexandra Piedade

This approach scales to any facility. Matterport Tags embedded in the model can hold installation notes or safety instructions, and cloud access ensures every stakeholder works from the same reference point. The result: fewer errors, shorter coordination time, and smoother implementation of process improvements.

5. Monitor performance and measure results

Optimization doesn’t stop once changes are in place — the next step is proving they work. Digital twins make it possible to track live performance against the baseline metrics captured earlier, creating a feedback loop that sustains improvement.

By integrating IoT data directly into the model, manufacturers can monitor metrics such as:

• Machine utilization to spot underperformance

• Temperature and vibration to anticipate equipment issues

• Cycle times and throughput to verify efficiency gains

Instead of reviewing abstract dashboards, teams see data points in physical context — at each machine, workstation, or utility location. Siemens has used this approach to overlay real-time IoT feeds in its digital twins, giving managers a clearer picture of how conditions on the shop floor affect performance.

Matterport supports this kind of integration through platforms like AWS IoT TwinMaker, which enables real-time factory twins that combine immersive 3D visualization with dynamic operational data. The result: continuous monitoring that verifies improvements and flags optimization opportunities before they become costly problems.

6. Standardize, sustain, and scale improvements

Lasting optimization depends on turning one-off gains into repeatable practices. Digital twins help lock in those improvements by becoming a single source of truth for process knowledge.

• With Matterport Tags, manufacturers can embed standard work procedures, maintenance manuals, and training modules directly in the twin at the point of use, whether that’s a workstation, machine, or utility panel.

• Teams can build training content into the model, so new employees learn best practices in the exact environment where they’ll be applied.

• Ongoing monitoring systems, supported by IoT data integrations, ensure processes stay within target ranges and flag deviations before they erode performance.

This approach not only prevents backsliding but also makes it easier to replicate success across sites. Once captured and documented in one facility, optimized processes can be replicated across sites accurately and consistently. By sustaining improvements in this way, manufacturers build a culture of continuous optimization — one that scales as the business grows.

Challenges in adopting new manufacturing processes

Even the best-designed optimization initiative can stumble without the right foundation. Manufacturers often run into familiar roadblocks — cultural resistance to change, technical complexity in connecting old and new systems, and the difficulty of sustaining improvements once the initial momentum fades.

Acknowledging these challenges upfront is key. By pairing digital tools with strong change management and clear communication, organizations can overcome these barriers and capture lasting value from their investments. The following sections outline the most common hurdles and practical strategies for addressing them.

Resistance to change from facility management teams

New processes or technologies often feel disruptive to facility managers and operators. Longstanding routines, fears of job displacement, or concerns about downtime often lead to passive resistance. In fact, McKinsey reports that approximately 70% of large-scale transformations don’t achieve their intended outcomes.

Digital twins offer a powerful way to bridge this gap in trust and understanding. By creating a realistic 3D model of their facility, teams can:

• Visually confirm inefficiencies and bottlenecks, making problems tangible rather than abstract.

• Preview proposed improvements—such as workflow changes or layout shifts—before committing to change.

• Embed operational aids like safety reminders, step-by-step procedures, and SOPs at the precise point of use, signaling that the technology supports rather than replaces their work.

This clarity helps turn apprehension into engagement.

Technology integration hurdles

Even when the value of optimization is clear, connecting new tools to existing systems can be daunting. Many manufacturing environments run on legacy PLCs, SCADA, MES, or ERP systems that weren’t designed to communicate with cloud-based platforms or IoT devices. Critical data sits in silos — paper logs, outdated databases, proprietary software — making unified operational views difficult to build.

Digital twins help bridge these gaps, but integration takes planning. The challenge extends beyond capturing 3D scans to ensuring reliable, secure data flow from sensors, production software, and enterprise systems into the model. Without this connection, the twin becomes a static snapshot rather than a live operational tool.

Solutions include deploying IoT gateways and middleware, using APIs to connect manufacturing software with digital platforms, and leveraging integration-ready services like AWS IoT TwinMaker, which Matterport supports. These approaches make it possible to overlay real-time data onto the twin, providing context for machine performance, energy use, and workflow efficiency. When done right, integration transforms the digital twin into a single source of truth that enhances the systems already in place.

Incorporating feedback loops to maintain momentum

Many optimization efforts start strong but fade quickly. After addressing visible problems, daily production pressures often sideline continuous improvement. Without clear tracking and reinforcement, teams revert to old habits, and gains gradually erode.

Digital twins help sustain momentum by making improvements visible and ongoing. Because they preserve a real-world baseline, teams can return to the same model to:

• Compare current performance to past states and verify that improvements are holding.

• Visualize new bottlenecks as they emerge, preventing small issues from compounding.

• Keep knowledge accessible by embedding updated work procedures, maintenance logs, and training content directly in the model.

This creates a built-in feedback loop where managers and operators can see. Instead of treating optimization as a one-time project, digital twins turn it into a continuous, collaborative process.