Innovation and Continuous Improvement Leaders face a persistent challenge: weeks spent analyzing changeover procedures, countless hours walking production floors, yet still missing critical inefficiencies that drain productivity. You implement SMED methodologies and train operators on lean principles, but without comprehensive visibility into actual process execution, achieving significant changeover time reductions remains elusive. The challenge isn't your expertise or methodology—it's the inability to capture and analyze what really happens during every changeover across every shift.

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 face mounting pressure to increase production flexibility while reducing costs. Customer demands for customization, shrinking order lead times, and labor shortages create an environment where every minute of downtime directly impacts profitability. Many facilities still experience changeover times exceeding four hours (Source: Simbi). These delays create cascading operational challenges that extend far 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 unpredictable production schedules that frustrate both operators and management.

Hidden process waste compounds 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 optimization opportunities.

Understanding SMED fundamentals for manufacturing

Single-Minute Exchange of Dies advanced manufacturing by delivering a systematic approach to changeover optimization. The methodology's power lies in its structured framework for identifying and eliminating waste during equipment setups. By clearly separating internal and external activities, SMED creates opportunities for dramatic 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 fundamental principle driving SMED effectiveness involves systematically converting internal activities to external operations wherever technically feasible. This conversion, combined with streamlining remaining internal activities, allows manufacturers to achieve changeover times under 10 minutes for many processes (Source: Delta Mod Tech).

Video analytics: reshaping changeover visibility

Video artificial intelligence is changing how manufacturers approach changeover optimization. By converting existing camera infrastructure into intelligent monitoring systems, video analytics delivers the complete visibility that traditional methods cannot match. This technology addresses your core frustration with reactive problem-solving by facilitating predictive 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 directly addresses the slow cycles of boosting efficiency that result from lacking easy access to historical evidence.

The integration of video analytics with changeover procedures creates significant opportunities for optimization. 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

Implementation can be rapid, allowing organizations to begin capturing baseline data immediately, building the evidence base needed for systematic enhancement.

Real-world changeover transformation: from 4 hours to under 10 minutes

One case study of an Indian biscuit manufacturer shows the potential of combining SMED principles with systematic process analysis. Initially experiencing over four hours of daily production losses due to inefficient changeovers between biscuit varieties, the organization faced delayed deliveries, reduced capacity, and increased labor costs (Source: Simbi).

The implementation 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 achieved similar results through video AI integration. A project involving five electronic manufacturing service foundries in China showed that AI systems could record worker actions and analyze processes through artificial intelligence inference. Within two months, these facilities increased their Unit Per Hour (UPH) through optimized changeover procedures (Source: Advantech).

Implementing video-enhanced SMED: technical requirements

Successful video analytics implementation 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 facilitate necessary data flows between video systems and existing infrastructure.

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

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

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

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

• Automated documentation: Systems create 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 complete 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 implementing integrated approaches consistently report measurable advances across multiple categories.

Predictive maintenance integration delivers additional quantifiable benefits. Companies implementing video-based predictive maintenance experience significant annual savings on replacement parts and scrap reduction. These cost reductions result from early detection of equipment degradation patterns, facilitating proactive maintenance scheduling during planned changeover windows.

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 continuous advancement

Successful implementation extends beyond technology deployment to fundamental cultural transformation. Organizations must shift from responsive problem-solving to preventative optimization, leveraging video analytics as a tool for empowerment rather than surveillance.

Effective change management strategies focus on operational benefits:

• Reduced changeover times facilitating flexible production responses

• Enhanced quality control preventing 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 optimization. Diverse perspectives from operators, engineers, quality managers, 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 facilitate pattern recognition that identifies subtle operational inefficiencies invisible to human observation. During changeover procedures, minor variations in execution compound into significant productivity losses over time. Video-based analytics detect these patterns through:

• Movement analysis: Computer vision systems analyze operator movements and equipment utilization patterns, identifying optimization 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. Immediate identification facilitates a rapid response before constraints impact production schedules.

• Predictive capabilities: AI systems learn from historical patterns to predict potential issues before they occur. This predictive capability evolves changeover planning from a firefighting approach to forward-thinking optimization.

• Automated recommendations: Systems offer specific guidance for addressing identified issues, including resource redeployment strategies, schedule adjustments, and maintenance intervention requirements. These recommendations align with broader production planning requirements through MES integration.

Machine learning applications in manufacturing can deliver measurable performance gains. Facilities implementing AI have reported boosts in production processes and increases in EBITA margins through data-supported decision-making (Source: LitsLink).

Overcoming implementation challenges

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

• Network architecture: Design systems 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 facilitating 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 surveillance and job security. Address these concerns through:

• Transparent communication about system purposes and benefits

• Emphasis on safety and efficiency gains rather than punitive monitoring

• Involvement of operators in system design and implementation

• 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. Some edge AI systems can analyze 20-30 live video streams simultaneously per device, increasing visibility of workers and equipment positioning during potentially hazardous setup activities (Source: Advantech).

Live safety monitoring facilitates:

• Immediate alert generation for safety violations

• PPE compliance verification throughout procedures

• Detection of workers approaching moving machinery

• Identification of improper lockout/tagout procedures

• Documentation of safety protocol adherence for regulatory compliance

For food manufacturing environments, video analytics addresses specific challenges. AI systems can monitor handwashing procedures to help maintain compliance with regulatory requirements. This includes verifying minimum 20-second duration and six-step process adherence (Source: Advantech). Automated monitoring maintains productivity while maintaining strict sanitation standards during changeover procedures.

Personal Protective Equipment monitoring through video analytics creates detailed safety documentation. Video analytics systems can monitor PPE usage across all shifts. They also create time-stamped audit trails to align with safety management standards. This documentation proves invaluable during regulatory audits and insurance reviews.

The path to breakthrough changeover performance

The convergence of video analytics and lean manufacturing methodologies offers Innovation and Continuous Improvement Leaders an exceptional opportunity to achieve breakthrough performance gains. Organizations implementing these integrated approaches can achieve significant changeover time reductions. They also see enhancements in safety, quality, and overall operational effectiveness.

The evidence is clear. Manufacturers across diverse sectors, including electronics, automotive, food processing, and plastics production, validate the potential of video-enhanced SMED implementation. The question isn't whether to implement these technologies, but how quickly you can begin capturing the operational advances available today.

Ready to eliminate the guesswork from your changeover optimization efforts? Discover how video analytics can deliver the complete visibility and actionable data you need to achieve your enhancement targets. Book a consultation with our manufacturing optimization experts to explore how integrated video AI and lean methodologies can reshape your changeover performance.

Frequently asked questions

What are the best practices for reducing changeover time?

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

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 changeover optimization specifically, video AI monitors adherence to standardized sequences, tracks preparation efficiency, analyzes movement patterns to eliminate waste, and verifies safety compliance. This complete visibility facilitates 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 actionable recommendations for manufacturing optimization. Through pattern recognition and machine learning, AI identifies subtle inefficiencies invisible to human observation. It predicts potential issues before they impact production, offers automated recommendations for process enhancements, and facilitates immediate decision-making during critical operations like changeovers. AI systems learn from historical data to continuously refine their detection capabilities, driving ongoing efficiency gains.

How do lean manufacturing principles apply to changeover processes?

Lean manufacturing principles directly address changeover optimization through systematic waste elimination. 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 continuous advancement, and using data to drive decisions. Video analytics enhances these principles by delivering the measurement and verification capabilities needed for sustainable lean implementation.

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 facilitates automated collection and reporting of these metrics, delivering real-time visibility into performance trends.

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.