For manufacturing leaders focused on continuous improvement, changeover time is a primary source of lost productivity. While manual time studies can document procedures, they often fail to capture the subtle, shift-to-shift variations that separate a 90-minute changeover from a 5-minute one. This gap between documented processes and actual execution is where substantial efficiency gains are lost.
Video analytics evolves Single-Minute Exchange of Dies (SMED) from a manual practice into a data-driven system that delivers measurable results. By turning existing cameras into smart monitoring tools, you can analyze what occurs during changeovers and take action based on evidence, not assumptions.
Understanding SMED fundamentals
Single-Minute Exchange of Dies (SMED) is a systematic approach to shortening equipment changeover times, ideally to under ten minutes. The methodology separates setup activities into two categories:
Internal tasks: Activities that must be performed while equipment is stopped
External tasks: Activities that can be conducted while machinery continues running
The five foundational steps of SMED implementation include:
Preparation phase activities
Mounting and extraction procedures
Establishing control settings
Achieving first-run capability
Continuous setup improvement processes
Manufacturing organizations that adopt SMED report average changeover time reductions of up to 94%, with some facilities cutting transition times from 90 minutes to under 5 minutes (Source: Spot AI).
The hidden costs of traditional SMED limitations
Your changeover process likely follows a predictable pattern: considerable time spent preparing tools and materials, minimal time on physically mounting and removing equipment, moderate time on adjusting settings and calibrations, and the majority of time on trial runs and fine-tuning processes.
A primary limitation is operator variability, where setup times for identical product changes vary markedly depending on which personnel perform the work. This variance often stems from a reliance on individual expertise, varied procedure execution, and limited feedback mechanisms.
For continuous improvement leaders, these limitations translate into:
Inability to verify SOP compliance at scale across all shifts and locations
Slow cycles of efficiency gains due to lack of evidence
Hidden process waste going undetected
Difficulty quantifying improvement opportunities to demonstrate ROI to leadership
How video analytics transforms SMED practices
Real-time changeover documentation
Video analytics decreases the need for manual Gemba walks, which consume time while providing only snapshot views. Instead, the technology captures detailed operator movements, timing sequences, and equipment interaction patterns during every setup transition. This creates baseline measurements for SMED improvement initiatives while identifying specific inefficiencies invisible to traditional observation methods.
Automated time study capabilities replace manual stopwatch approaches, providing precise measurements of individual changeover steps. The system reveals subtle inefficiencies in operator movements, tool preparation sequences, and equipment adjustment procedures that contribute to extended changeover durations.
Ensuring SOP compliance at scale
Without automated monitoring, you can't ensure consistent adherence to standard operating procedures across different shifts and operators. Video AI platforms verify procedure compliance, while digital work instruction integration provides step-by-step guidance with video demonstrations and real-time updates.
Electronic work instruction systems can be updated in real time and accessed through tablets or smartphones on the manufacturing floor. This standardization decreases the variability that occurs when changeovers depend on individual operator preferences and experience levels.
Data-driven performance optimization
Video analytics delivers objective measurement capabilities for changeover performance tracking, including:
Start and end time documentation
Operator efficiency comparisons
Setup time variability analysis
These measurements support ongoing improvement initiatives by identifying top-performing operators and documenting their methods for standardization across all shifts. Automated monitoring captures detailed performance data without disrupting operational activities, allowing for a full analysis of changeover trends and equipment-specific optimization opportunities.
Measuring ROI through operational efficiency metrics
Overall Equipment Effectiveness (OEE) enhancement
OEE encompasses three critical components:
Availability: Keeping equipment running
Performance: Maintaining optimal operational speed
Quality: Verifying product compliance with specifications
Video analytics and AI integration help optimize OEE by providing real-time operational insights into availability and performance. The modern application of OEE helps turn this metric from a lagging indicator reviewed weekly into a real-time operational tool that guides timely decision-making.
Quantifiable cost savings
Video analytics deployments in manufacturing demonstrate measurable return on investment through various operational improvements and cost avoidance measures. These include cost savings from mitigating waste overflow, reduced overhead by addressing bottlenecks, productivity improvements through optimized workstation staffing, improved safety from detecting unattended machinery, and throughput improvements.
Integrating with existing manufacturing systems
Legacy system compatibility
Video analytics platforms work with existing camera infrastructure and traditional manufacturing equipment, minimizing disruption during the rollout. Cloud-based AI camera systems bridge existing camera networks to secure, scalable dashboards while reducing the need for bulky local server maintenance.
Deployment best practices for sustainable results
Phased deployment approach
A successful deployment of video analytics for SMED follows systematic phases:
Baseline changeover documentation
Pilot area deployment
Performance measurement
Gradual expansion across production lines
The initial rollout should focus on high-impact areas where changeover frequency and duration create major operational constraints. Pilot programs typically achieve substantial setup time reductions during initial phases.
Change management and operator training
An effective rollout requires comprehensive operator training that addresses both SMED methodology and video analytics technology. Training should include:
Hands-on practice sessions
Video demonstration materials
Regular refresher programs
Change management strategies must address potential operator concerns regarding video monitoring while emphasizing the technology's role in supporting rather than replacing human expertise.
Building a continuous improvement framework
Video analytics supports systematic process refinement through:
Automated data collection
Trend analysis
Performance comparison capabilities
Adopting Digital Kaizen enables frontline teams to conduct root cause analysis using integrated diagnostic tools while tracking improvement results. Advanced analytics identify systematic problems invisible to traditional observation methods, enabling forward-looking optimization rather than reactive problem-solving.
Overcoming common adoption obstacles
Addressing technology integration complexity
For professionals focused on continuous improvement navigating IT/OT convergence complexities, modern video analytics platforms offer plug-and-play solutions that work with legacy equipment and existing software platforms. This lowers the traditional barriers of complex network infrastructure requirements.
Managing multi-site standardization
Video analytics enables consistent process implementation across multiple facilities by providing centralized visibility into adherence to best practices. A unified dashboard allows you to monitor changeover performance across all locations, identifying which sites excel and which need additional support.
Reducing compliance documentation challenges
Automated reporting capabilities cut down on the excessive time spent creating reports for regulatory compliance and customer audits. The system generates documentation automatically, freeing you to focus on driving actual improvements rather than paperwork.
Achieving measurable SMED success with video analytics
Integrating video analytics with SMED practices addresses the core frustrations that professionals focused on continuous improvement face daily. Instead of reactive problem-solving after equipment failures or quality defects occur, you gain actionable insights that enable early intervention as issues arise.
Your manual Gemba walks evolve into 24/7 monitoring that captures key process variations and improvement opportunities. The inability to verify SOP compliance at scale becomes automated detection of process deviations with real-time alerts and comprehensive reporting.
Most importantly, slow cycles of improving efficiency accelerate. With swift access to historical video evidence, root cause analysis that once took weeks is now completed in a fraction of the time. Computer vision analytics reveal hidden process waste by detecting inefficient movement patterns and unnecessary waiting time.
Manufacturing organizations that use video analytics for SMED optimization benefit from notable reductions in calibration time, substantial increases in machine ROI, reduced dependency on specialized personnel, and measurable gains in changeover uniformity across shifts.
See how video AI can streamline your SMED process and boost changeover efficiency. Request a demo to experience Spot AI’s capabilities in action.
Frequently asked questions
What are the best practices for implementing SMED?
Best practices for adopting SMED include systematic documentation of current changeover processes, clear separation of internal and external tasks, standardization of procedures across all shifts, ongoing operator training, and regular performance measurement. Video analytics enhances these practices by delivering automated documentation, direct compliance monitoring, and objective performance data that removes guesswork from the optimization process.
How can video analytics enhance manufacturing efficiency?
Video analytics enhances manufacturing efficiency by offering 24/7 monitoring of production processes, automated detection of bottlenecks and waste, timely alerts for deviations from standard procedures, and data-driven details for optimization. The technology evolves existing cameras into smart sensors that capture operational data around the clock, allowing for faster decision-making and proactive problem resolution.
How to measure the ROI of video analytics in manufacturing?
ROI measurement for video analytics includes both quantitative and qualitative metrics. Quantitative measures include reduced changeover times, decreased safety incidents, higher OEE scores, and direct cost savings from waste reduction. Qualitative benefits include greater operator confidence, enhanced knowledge transfer, better compliance documentation, and reduced management stress through automated monitoring capabilities.
What obstacles are faced during SMED adoption?
Common obstacles to SMED adoption include operator resistance to change, varied execution across shifts, difficulty in sustaining gains over time, lack of accurate baseline data, and limited visibility into actual changeover procedures. Video analytics addresses these hurdles by delivering objective performance data, verifying procedural adherence, and creating durable refinement systems through ongoing monitoring and feedback.
How does video AI help identify the 8 wastes of lean manufacturing?
Video AI makes the 8 wastes visible during changeovers. It automatically flags operators waiting for tools (Waiting), analyzes movement paths to find ergonomic inefficiencies (Motion), and highlights excess work-in-progress materials staged around a machine (Inventory). This turns abstract lean principles into specific, actionable data points for continuous improvement teams, helping you see and solve waste that was previously hidden.
How can video analytics reduce scrap during changeovers?
Video analytics provides visual evidence to find the root cause of startup scrap. By reviewing time-stamped video of a setup, your team can pinpoint the exact cause—like an incorrect setting or a missed SOP step—that data logs miss. This allows you to preemptively correct procedures instead of reacting to quality issues after they occur.
About the author
Rish Gupta is CEO and Co-founder of Spot AI, leading the charge in business strategy and the future of video intelligence. With extensive experience in AI-powered security and digital transformation, Rish helps organizations unlock the full potential of their video data.
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