Leaders in continuous improvement often find that traditional methods, like manual observation, miss critical inefficiencies that limit productivity. Even with SMED methodologies and lean training, achieving substantial changeover time reductions can be difficult without full visibility into process execution. The primary obstacle is capturing and analyzing what occurs during every changeover across all shifts.

Key terms to know

• SMED (Single-Minute Exchange of Dies): A lean manufacturing method aimed at reducing equipment changeover time to under 10 minutes (single-digit minutes). Originally developed for stamping operations, SMED now applies across all manufacturing sectors.

• Internal vs. External Activities: Internal activities require equipment stoppage (installing molds, adjusting settings). External activities can occur while machines run (preparing tools, staging materials). Converting internal to external activities is fundamental to changeover optimization.

• Overall Equipment Effectiveness (OEE): A metric combining availability, performance, and quality to measure manufacturing productivity. Calculated using planned production time, excluding planned stops.

• Overall Operations Effectiveness (OOE): Similar to OEE but includes planned stops in calculations, providing a complete picture of shift effectiveness—particularly valuable for changeover optimization projects.

• Video Analytics: AI-powered technology that converts camera footage into searchable, actionable data by detecting patterns, behaviors, and anomalies.

The hidden cost of inefficient changeovers

Manufacturing leaders need to increase production flexibility while reducing costs. Customer demands for customization, shorter lead times, and labor shortages mean that downtime directly impacts profitability. Inefficient changeovers can lead to delays and operational challenges that go beyond lost production time.

The inability to verify SOP compliance at scale represents a fundamental barrier to changeover optimization. When you can't verify consistent adherence to procedures across all shifts and locations, process variability becomes inevitable. This variability manifests in quality issues, safety incidents, and production schedule disruptions that frustrate both operators and management.

Hidden process waste amplifies these challenges. Minor inefficiencies in material handling, unnecessary motion, and waiting time accumulate into major productivity losses. Traditional observation methods—periodic Gemba walks and manual time studies—capture only snapshots of these issues. Critical events occurring between observations remain invisible, limiting the scope of improvement opportunities.

Understanding SMED fundamentals for manufacturing

Single-Minute Exchange of Dies provides a systematic approach to changeover optimization. The methodology uses a structured framework to identify and eliminate waste during equipment setups. By separating internal and external activities, SMED creates opportunities for considerable time reductions without compromising safety or quality.

Internal activities—those requiring equipment stoppage—typically include:

1. Removing and installing molds, dies, or flexible packaging components

2. Adjusting machine settings like temperature and pressure parameters

3. Conducting necessary cleaning operations

4. Performing quality checks on first articles

External activities—those possible during machine operation—encompass:

1. Retrieving and preparing parts, tools, or materials

2. Assembling fixtures required for subsequent setups

3. Pre-heating equipment or components

4. Conducting preparatory work that positions teams for rapid transitions

The core principle of SMED is to convert internal activities to external ones wherever possible. This conversion, combined with streamlining the remaining internal tasks, helps manufacturers achieve changeover times under 10 minutes for many processes (Source: Delta Mod Tech).

Video analytics: reshaping changeover visibility

Video AI is changing how manufacturers approach changeover optimization. By converting existing camera infrastructure into intelligent monitoring systems, video analytics delivers enhanced visibility that traditional methods cannot match. This technology addresses your core frustration with reactive problem-solving by enabling anticipatory interventions before issues impact production.

Advanced video AI platforms integrate seamlessly with Manufacturing Execution Systems, IoT sensors, and vision systems. They detect process variations, identify patterns invisible to human observation, and create searchable databases of operational events. This capability breaks the slow, reactive cycles of problem-solving that result from a lack of searchable, real-time video evidence.

The integration of video analytics with changeover procedures creates valuable opportunities for improvement. Computer vision algorithms can:

• Monitor adherence to standardized changeover sequences

• Detect deviations from established time standards

• Identify bottlenecks

• Track tool and material preparation efficiency

• Analyze operator movement patterns to eliminate waste

• Verify safety protocol compliance throughout procedures

Deployment can be rapid, allowing organizations to begin capturing baseline data quickly and build the evidence base needed for process improvements.

Example of changeover transformation: from 4 hours to under 10 minutes

For example, a food manufacturer applying SMED principles can improve its operations. A facility with inefficient changeovers between product varieties often faces production losses, delayed deliveries, reduced capacity, and increased labor costs.

The process followed a structured seven-step approach:

1. Complete current process analysis using video documentation

2. Classification of all activities into internal and external categories

3. Strategic conversion of internal tasks to external operations

4. Introduction of quick-lock mold systems and color-coded cleaning kits

5. Implementation of clear standard operating procedures

6. Thorough operator training on SMED techniques

7. Real-time changeover tracking via digital dashboards

Pre-heating ovens, preparing molds, and staging raw materials shifted from equipment downtime to concurrent activities. Visual work instructions ensured consistent execution across all teams. The transformation delivered substantial changeover time reductions, significantly boosting operational smoothness and increasing overall production capacity.

Electronics manufacturers can achieve similar results through video AI integration. By using Video AI to automatically analyze every changeover, facilities can pinpoint inefficiencies and standardize best practices to improve metrics like Unit Per Hour (UPH).

Implementing video-enhanced SMED: technical requirements

Successful video analytics deployment requires strategic planning addressing both technical architecture and organizational change management. Manufacturing facilities must design network architectures that maintain operational technology security. These architectures must also enable necessary data flows between video systems and existing infrastructure.

Advanced video analytics platforms offer several critical advantages for changeover optimization:

• Camera-agnostic compatibility: Platforms connect to existing infrastructure through standard ethernet ports, eliminating the need for camera replacement. This compatibility protects previous investments while enabling rapid deployment.

• Edge computing capabilities: AI systems analyze visual data and deliver feedback without relying on external connectivity. This supports faster decision-making during changeover procedures and helps reduce deviations from standardized processes.

• Seamless integration: Open APIs enable connection with Manufacturing Execution Systems, Enterprise Resource Planning platforms, and production monitoring infrastructure. This integration helps align improvement recommendations with broader production planning requirements.

• Automated documentation: The technology creates time-stamped audit trails to support quality management standards. Compliance reporting becomes automatic rather than manual.

• Scalable deployment: Organizations can focus initial deployment on high-impact areas like changeover monitoring before expanding to broader operational oversight. This phased approach validates ROI while building organizational confidence.

Measuring success: beyond traditional metrics

While Overall Equipment Effectiveness remains fundamental for evaluating changeover gains, thorough assessment requires additional indicators capturing the full scope of video analytics benefits. Manufacturing organizations using integrated approaches consistently report measurable advances across multiple categories.

Leading indicators for ROI validation include:

• Number of critical alerts resolved promptly

• Time to action following alert generation

• Reduction in emergency repairs or unplanned maintenance

• Decrease in investigation time for quality issues

• Enhancement in changeover time consistency across shifts

Building a culture of anticipatory improvement

Successful adoption requires a cultural shift from responsive problem-solving to anticipatory improvement. Organizations can leverage video analytics as a tool for empowerment rather than monitoring.

Effective change management strategies focus on operational benefits:

• Reduced changeover times enabling flexible production responses

• Enhanced quality control mitigating customer complaints

• Improved safety that reduces injury risks and associated costs

• Faster problem resolution minimizing production disruptions

• Better resource utilization increasing overall profitability

Cross-functional team engagement proves essential for thorough improvement. Diverse perspectives from operators, engineers, quality professionals, and maintenance personnel generate multiple enhancement opportunities while avoiding analytical tunnel vision. Video analytics delivers the objective data needed to align these different viewpoints around common goals.

Training programs must emphasize both technical competencies and cultural transformation. Operators need education on SMED techniques combined with encouragement to suggest enhancements based on frontline experience. Video analytics validates these suggestions with concrete data, creating an ownership mindset among personnel directly responsible for changeover execution.

Advanced optimization through AI-driven insights

Artificial intelligence algorithms use pattern recognition to identify operational inefficiencies that may be invisible to human observation. During changeovers, small variations in execution can lead to productivity losses over time. Video analytics detect these patterns through:

• Movement analysis: Computer vision systems analyze operator movements and equipment utilization patterns, identifying improvement opportunities that traditional methods miss. Inefficient movement patterns, excessive waiting times, and unnecessary motion become visible and quantifiable.

• Bottleneck detection: Advanced analytics process complex manufacturing data to identify constraints that shift based on product mix variations, shift patterns, or seasonal demand fluctuations. Rapid identification enables a swift response before constraints impact production schedules.

By turning video into a source of operational data, AI-driven platforms empower teams with the insights needed to steadily improve production processes and financial outcomes.

Overcoming implementation challenges

Technology integration complexity represents a major hurdle for many organizations. The convergence of IT and OT systems requires careful planning to maintain security while enabling necessary data flows. Key considerations include:

• Network architecture: Design architectures maintaining complete separation between video analytics and critical control systems

• Cybersecurity compliance: Adhere to relevant standards like IEC 62443 and NIST frameworks

• Legacy system integration: Utilize platforms with open APIs enabling connection without wholesale replacement

• Scalability planning: Select solutions that grow with organizational needs without requiring infrastructure overhauls

Change management resistance often stems from employee concerns about monitoring and job security. Address these concerns through:

• Transparent communication about system purposes and benefits

• Emphasis on safety and efficiency gains rather than disciplinary monitoring

• Involvement of operators in system design and deployment

• Celebration of advances achieved through employee suggestions validated by video data

• Clear policies regarding data usage and retention

Safety and compliance: beyond changeover efficiency

Video analytics enhances safety during changeover procedures through its monitoring capabilities. Edge AI systems act as an intelligent teammate, analyzing multiple live video streams to increase visibility and proactively identify hazards during setup activities.

Live safety monitoring enables:

• Timely alert generation for safety violations

• PPE compliance verification throughout procedures

• Detection of workers approaching moving machinery

• Documentation of safety protocol adherence for regulatory compliance

For food manufacturing environments, video analytics addresses specific challenges. The AI automatically monitors compliance with critical procedural steps, like those in handwashing protocols, to help maintain regulatory standards. Automated monitoring helps maintain productivity while upholding strict sanitation standards during changeover procedures.

Personal protective equipment monitoring through video analytics creates detailed safety documentation. The video analytics platform can monitor PPE usage across all shifts. It also creates time-stamped audit trails to align with safety management standards. This documentation proves invaluable during regulatory audits and insurance reviews.

Achieving better changeover performance

Combining video analytics with lean manufacturing methodologies offers leaders an opportunity to achieve performance gains. Organizations that adopt these integrated approaches can reduce changeover times while enhancing safety, quality, and overall operational effectiveness.

See how video AI can streamline your changeover process and deliver the visibility you need to reach your goals. Request a demo to experience Spot AI in action and explore how integrated video AI and lean methodologies can enhance your manufacturing performance.

Frequently asked questions

What are the best practices for reducing changeover time?

Best practices for reducing changeover time center on applying the SMED methodology combined with video monitoring technologies. Start by documenting current processes using video recording to establish accurate baselines. Classify all activities as internal or external, then convert internal activities to external operations wherever possible. Implement quick-release mechanisms, modular fixtures, and visual management tools. Standardize procedures across all shifts and monitor adherence using video analytics to identify improvement opportunities. Regular training and cross-functional team involvement help maintain sustained progress.

How can video analytics boost manufacturing processes?

Video analytics reshapes manufacturing processes by delivering continuous, objective monitoring of operations. The technology detects process variations, identifies bottlenecks automatically, and creates searchable databases of operational events. For improving changeovers specifically, video AI monitors adherence to standardized sequences, tracks preparation efficiency, analyzes movement patterns to eliminate waste, and verifies safety compliance. This enhanced visibility supports data-driven decision-making and validates enhancement initiatives with concrete evidence.

What is the role of AI in optimizing manufacturing efficiency?

AI serves as the analytical layer that converts raw video data into recommendations for manufacturing enhancement. Through pattern recognition and machine learning, AI identifies subtle inefficiencies that may be invisible to human observation. It can identify patterns that indicate potential issues, allowing teams to act before they impact production. The system also offers automated recommendations for process enhancements and supports decision-making during critical operations like changeovers. AI systems learn from historical data to refine their detection capabilities, driving ongoing efficiency gains.

How do lean manufacturing principles apply to changeover processes?

Lean manufacturing principles directly address improving changeovers through systematic waste elimination. The SMED methodology exemplifies lean thinking by categorizing and minimizing non-value-added activities during setups. Key lean principles applicable to changeovers include standardizing work procedures, implementing visual management systems, engaging frontline workers in improvement efforts, and using data to drive decisions. Video analytics enhances these principles by delivering the measurement and verification capabilities needed for sustainable lean execution.

What metrics should be used to measure manufacturing efficiency?

Manufacturing efficiency measurement requires a balanced set of metrics capturing multiple performance dimensions. Overall Equipment Effectiveness (OEE) delivers a complete view combining availability, performance, and quality. For changeover-specific metrics, track average setup time, SMED completion rate, and changeover time consistency across shifts. Safety metrics include TRIR and PPE compliance rates. Quality indicators encompass First Pass Yield and defect rates. Operational efficiency metrics include cycle time, throughput, and resource utilization. Video analytics enables automated collection and reporting of these metrics, delivering visibility into performance trends.

How does improving uptime with video AI analytics lower overtime costs?

Inefficient changeovers and unplanned downtime are primary drivers of schedule delays, which often force companies to use costly overtime labor to meet production targets. By using video AI analytics to streamline changeovers and identify bottlenecks, manufacturers increase machine availability and create more insight-driven production schedules. This enhanced uptime allows more work to be completed within standard shifts, directly reducing or eliminating the need for overtime and lowering overall operational costs for a clear return on investment.

How can video analytics reduce scrap during start-up and changeovers?

Video analytics helps reduce scrap by enabling teams to visually verify that all pre-startup procedures are followed correctly. The system can confirm SOP adherence for critical steps like machine cleaning, correct setting adjustments, and first-article quality checks. By creating an auditable video record of this pre-run compliance, AI helps mitigate the common setup errors that lead to defective products and wasted material from the very start of a new production run, improving first pass yield.

About the author

Amrish Kapoor is VP of Engineering at Spot AI, leading platform and product engineering teams that build the scalable edge-cloud and AI infrastructure behind Spot AI’s video AI—powering operations, safety, and security use cases.