Summary :
SMED (Single Minute Exchange of Die) aims to reduce changeover times to gain availability and flexibility.
Changeovers interrupt production and are rarely measured accurately – a blind spot in optimization.
Manual timing is partial, dependent on the observer, and not representative of daily reality. Machine data measures changes automatically and continuously, transforming SMED into a sustainable approach.
SMED reduces manufacturing order (OF) changeover times, a major bottleneck in multi-OF machining. But without reliable data, these times are estimated and the approach remains fragile. Real-time machine data automatically measures changes, reveals micro-stops, and connects SMED to OEE for sustainable management.
Why do OF changeovers weigh so heavily on performance?
In CNC machining workshops, production order (PO) changeovers are one of the main obstacles to industrial performance. They interrupt production, require skilled personnel, and generate unproductive downtime that is difficult to recover, particularly in high-mix, low- to medium-volume manufacturing environments.
The issue is well known to industrial managers, manufacturing engineers, and shop floor supervisors. Yet it is rarely measured with precision. Changeover times are often estimated, reconstructed after the fact, or observed only occasionally. Without reliable data, they cannot be managed effectively and remain a major blind spot in production optimization.
It is in this context that real-time machine data fundamentally changes the way SMED is approached.
SMED: a synthetic and operational reminder
The objective of SMED
The SMED (Single-Minute Exchange of Die) methodology aims to reduce changeover times in order to increase equipment availability, improve shop floor flexibility, and enhance overall industrial performance. Its primary objective is to optimize the transition between production orders, rather than simply achieving a theoretical reduction in changeover time.
The limits of traditional approaches
In many machining workshops, SMED still relies on:
Manual time tracking,
deanalyses conducted during isolated Lean workshops
While these approaches can initiate awareness, they remain partial. They are dependent on the observer, time-consuming, and not very representative of daily reality. They rarely produce lasting results.
Why SMED often fails in practice
A lack of objective data
Without reliable machine data, it is difficult to define exactly what constitutes a production order changeover. Changeover times are estimated, debated, and sometimes even disputed. As a result, production management becomes subjective, and the SMED approach quickly loses credibility.
A largely underestimated variability
No two changeovers are ever exactly the same. Depending on the machine, the production order, the team, or the production context, significant variations can occur. One-off analyses tend to smooth out this variability, making it difficult to identify the real opportunities for improvement.
Gains that are difficult to sustain
Even when improvements are identified, sustaining them over time is rarely guaranteed. Without continuous monitoring, standards gradually decline, practices drift, and SMED gains erode without being immediately detected.
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Real-time machine data: a decisive lever for SMED
An automatic and continuous measurement of changes
Machine data makes it possible to measure production order changeover times automatically, without any manual intervention. Machine downtime, setup, and restart phases are identified accurately, continuously, and consistently.
Highlighting downtime and micro-stops
Beyond overall changeover time, machine data reveals micro-stoppages, waiting periods, and unproductive sequences that reduce equipment availability. These often invisible losses account for a significant share of the decline in Overall Equipment Effectiveness (OEE).
A factual comparative analysis
Data allows for comparison objectively of different OFs, similar machines, or teams and time slots.
SMED then becomes a true industrial analysis tool, driven by facts rather than assumptions.
The role of Atsora Tracking in an industrialized SMED approach
Automatic detection of production order changes
Atsora Tracking leverages real-time machine data to automatically detect production order and changeovers. Measurement is reliable, continuous, and completely independent of operator input.
Factual analysis of internal and external phases
By analyzing machine states, Atsora Tracking distinguishes between time that is genuinely constrained by machine downtime and activities that can be anticipated or performed externally. This fact-based insight is essential for prioritizing the SMED actions with the greatest impact.
Monitoring over time and controlling deviations
Changeover-related KPIs are monitored over time. Atsora Tracking quickly identifies deviations, verifies compliance with established standards, and embeds the SMED approach into a continuous improvement process.
A direct link to availability and OEE
Production order changeovers have a direct impact on equipment availability and Overall Equipment Effectiveness (OEE). By linking SMED analysis with industrial performance indicators, Atsora Tracking places changeover management at the heart of operational control in the machining workshop.
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Concrete industrial benefits
Measurable reduction of changeover times
Continuous measurement makes it possible to pinpoint actual performance losses with precision. Optimization efforts are prioritized based on observed data, and their impact can be measured over time.
Improvement of production flexibility
Shorter, better-controlled production order changeovers make it easier to adapt to fluctuations in demand without compromising overall industrial performance.
Better utilization of equipment
Reducing the unproductive time associated with changeovers automatically increases productive machine time, without requiring any additional hardware investment.
Decisions based on machine data
Machine data eliminates subjective debates. Decisions are based on objective, comparable, and shared data across teams.
Conclusion: measure before optimizing
SMED remains a key driver of industrial performance, provided it is firmly rooted in the operational reality of the machining workshop. Without reliable machine data, the approach remains fragile and difficult to sustain over time..
Real-time machine data transforms SMED into a measurable, manageable, and sustainable process. Atsora Tracking is built around this practical approach, helping manufacturers make production order changeovers more reliable, improve Overall Equipment Effectiveness (OEE), and turn changeovers into a tangible driver of production optimization.
To remember
- SMED reduces setup times, a major bottleneck in multi-job machining and small series.
- Manual timing is partial, subjective, and not sustainable.
- Machine data measures changes automatically, continuously, and without bias.
- It also reveals micro-stops and allows for distinguishing between internal and external phases.
- SMED becomes measurable, manageable, and directly linked to OEE.
Optimize your series changes with Atsora Tracking
Make your SMED reliable and sustainably improve workshop performance. We help you measure your setup changes continuously, target real losses, and anchor your gains over time. Request a demo of Atsora Tracking and make series change a true optimization lever.
FAQ – Frequently Asked Questions
SMED (Single Minute Exchange of Die) is a Lean approach aimed at reducing setup times. In machining, the challenge is to master transitions between production orders to gain availability and flexibility.
It allows for automatic and continuous measurement of setup times, without relying on sporadic observations or manual declarations. It objectifies losses, reveals variability, and enables sustainable management of the approach.
Yes, it is even the context where it is most strategic. When series changes are frequent, their impact on availability and OEE becomes significant. Machine data helps prioritize actions where gains are real.
Yes. The use of machine data makes manual timing largely obsolete: downtime, setup, and restart times are detected automatically, with much greater accuracy and continuity.
The key is continuous monitoring. Without reliable indicators over time, standards erode. A data-driven approach quickly detects deviations, compares practices, and maintains gains.
Changeovers directly impact equipment availability, one of the pillars of OEE. By making these times more reliable and reducing them, SMED directly contributes to improving OEE and optimizing production.