Summary :

In CNC machining workshops, changes in production orders are a major barrier to performance, but are rarely measured accurately. Traditional SMED approaches, based on spot observation and manual timing, struggle to capture daily reality, the variability of practices, and time drift. By leveraging real-time machine data, it becomes possible to automatically and continuously measure changes in production orders, identify downtime and micro-stops, and objectively compare machines, series, and teams. Atsora Tracking industrializes this approach by detecting changes, distinguishing between internal and external phases, and ensuring sustainable tracking of gains. Result: shorter changes, increased flexibility, better machine availability, and improved OEE, without additional material investment.

Changes in production orders, a major barrier to industrial 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,

• occasional shop floor observations,

• 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.

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).

Une analyse comparative factuelle

The data enables objective comparisons between:

• different production orders,

• similar machines,

• teams or 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.

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.

FAQ – SMED, Production Order Changeovers and Machine Data