How do you calculate machine downtime?

Machine downtime is the total time equipment is stopped during planned production time. In OEE, availability equals run time divided by planned production time, where run time is planned production time minus downtime. Collecting machine states automatically from controllers makes this calculation accurate and removes manual reporting errors.

What is downtime in OEE?

In OEE, downtime covers stops that reduce availability, both unplanned failures and planned stops such as changeovers that fall within planned production time. Short stops and reduced speed are usually counted as performance losses, while defects and rework affect the quality factor.

How can manufacturers reduce machine downtime?

Start by capturing downtime automatically and adding context such as shift, operator and product. Analyse the patterns to find root causes, then combine technical measures, such as predictive maintenance, with organisational ones, such as clear procedures, accountability and training. In the project described above, enforcing procedures and retraining night shift teams increased machine availability.

Why does shift-level data matter in downtime analysis?

Plant-wide KPIs can hide differences between shifts. Correlating downtime with shift schedules and operator data shows whether losses are linked to specific teams, times of day or procedures. In this project, shift-level analysis revealed that a significant share of unreported downtime occurred at night.

Do you need an MES to track machine downtime?

Not necessarily. Downtime can be tracked with a production monitoring system connected directly to PLCs. Integrating that data with MES and ERP, however, adds production orders, schedules and planning context, which makes root cause analysis and KPI reporting more accurate.

Machine Downtime Tracking: How Shift Data Improved FMCG KPIs

Why real-time data?

<strong>At TT PSC, we believe that the real goals of the Industry 4.0 (r)evolution can only be reached when industrial leaders rely on accurate data collected in real time.</strong> That data builds knowledge and awareness, and it shows which improvement activities will pay off.<br><br>This article breaks down a completed digital manufacturing project and the practical lessons it produced, so you can avoid some of the most common pitfalls on your way to digital transformation.

Machine downtime tracking improves KPIs only when downtime data carries context. A food and beverage manufacturer running semi-automated production lines already calculated availability, performance and quality from PLC data, yet its KPIs kept falling short. Once TT PSC correlated every downtime event with shift and operator data, the root cause became visible: unreported stoppages on night shifts, driven by non-compliance with standard operating procedures. Targeted corrective actions followed, machine availability rose and the plant met its performance and quality targets.

Industry	FMCG, food and beverage
Production environment	Semi-automated production lines
Starting point	KPIs (availability, performance, quality) calculated automatically from PLC data, but still below target
Root cause	Unreported downtime on night shifts, caused by non-compliance with standard operating procedures
Solution	Downtime data contextualised with shift and operator data, PLC integration with MES and ERP, real-time KPI dashboards, signal and event history
Corrective actions	Supervisor accountability and targeted retraining of night shift personnel
Outcome	Higher machine availability, significantly fewer downtime incidents, performance and quality targets met

Why machine downtime tracking matters for KPIs in FMCG manufacturing

In the highly competitive food and beverage industry, availability, performance and quality are the KPIs that decide operational efficiency and profitability. Together they make up Overall Equipment Effectiveness (OEE), the standard measure of how well a production line uses its planned production time. Availability is often the first factor to suffer, because every unplanned stop removes productive time from the line.

Semi-automated lines make the picture harder to read. Machines generate signals automatically, but part of the work, and often the explanation of why a line stopped, still depends on people. When downtime is reported manually, the gap between what happened and what was recorded can hide the real source of losses. This is why machine downtime tracking, built on Industrial IoT connectivity and a data-driven approach, is a core building block of digital manufacturing.

The manufacturer in this project faced exactly that problem. Its KPIs kept underperforming, and it needed a precise, data-driven way to monitor production processes and identify the root causes behind the decline.

The challenge: PLC data alone did not explain low availability

The client had already deployed a system that acquired real-time data from machine controllers (PLCs) and used it to calculate KPIs. Even so, significant inefficiencies persisted, particularly in machine availability and overall performance. Preliminary data analysis showed that the existing downtime monitoring did not capture the full scope of the operational issues.

Automated KPI calculation showed how much time the lines were losing. It could not show why the losses occurred, or under which conditions. That distinction shaped the rest of the project.

Night shifts as the first signal

A closer examination of the data showed that downtime events were disproportionately high during night shifts. This led to a working hypothesis: procedural or operational inefficiencies specific to night shift management were contributing to the overall KPI shortfall. Testing it required downtime data that could be analysed shift by shift.

The solution: machine downtime tracking with shift and operator context

To address the KPI challenges, TT PSC implemented a multi-layered solution that integrated real-time data from production machines and contextualised it with shift information. The aim was to turn raw machine signals into actionable insights for production managers, in line with the Industry 4.0 principle of a connected factory. The solution combined four components.

1. Contextualised data correlation

Downtime data was enriched with contextual information, in particular shift schedules and operator data. This allowed a granular analysis of downtime causes, correlated with the specific shifts responsible, and gave a clear indication of shift-specific performance bottlenecks. Context is what turns a list of stoppages into a reliable basis for root cause analysis in manufacturing.

2. PLC integration with MES and ERP

The existing PLC architecture was extended with integration into the corporate Manufacturing Execution System (MES) and Enterprise Resource Planning (ERP) system. Machine states were synchronised in real time, so the data used for KPI calculation was collected accurately and without manual intervention. Connecting the shop floor with MES and ERP also links production data to Manufacturing Operations Management (MOM) processes and to planning across the digital supply chain.

3. Real-time KPI dashboards

A dashboard system gave operations personnel a live view of KPI data such as machine uptime, throughput and quality metrics. The user-friendly interface made deviations visible immediately, so teams could respond to production issues faster. This production analytics layer is where plant teams see the effect of downtime as it happens, not at the end of the week.

4. Signal and event history

Comprehensive historical logging archived detailed machine signals, downtime events and system outputs over time. This provided long-term visibility into performance trends and helped identify recurring issues linked to specific production conditions. A reliable data history is also the foundation for more advanced use cases, such as predictive maintenance in a smart factory.

What the data revealed: unreported downtime on night shifts

Once the data collection infrastructure and KPI analysis tools were in place, a clear pattern emerged: a significant percentage of unreported downtime occurred during night shifts. None of it had been flagged in the original manual reporting system.

Correlating downtime events with shift schedules showed that these stoppages were frequently caused by non-compliance with standard operating procedures. Further analysis suggested that the events were largely unintentional, which pointed to a need for stricter oversight and closer adherence to established workflows during night operations.

The finding also explains why the existing KPIs could not reveal the problem on their own. Manual downtime reports are prone to gaps, and OEE figures built on incomplete data can mislead decision-makers about where losses really come from.

Corrective actions: accountability and targeted retraining

With the root cause identified, the client introduced two corrective measures.

Enforcing accountability

One night shift supervisor was held accountable for repeated procedural violations, and disciplinary action was taken. The step reinforced the importance of following operational guidelines and was meant to ensure that such issues would not recur.

Targeted retraining for night shift teams

Night shift personnel received extensive retraining on operational standards, system use and machine maintenance protocols. The training emphasised real-time reporting and proactive machine monitoring, so that the new data infrastructure would be used consistently on every shift.

Results: higher availability and KPI targets met

The corrective actions led to a marked improvement in operational performance:

Machine availability increased.
Downtime incidents were significantly reduced.
Overall KPI targets for performance and quality were met.
Production efficiency and throughput improved.

The client originally wanted automated KPI calculation based on machine data. The project showed that advanced downtime monitoring and data contextualisation were essential to uncover deeper operational inefficiencies. The decisive insight came from correlating downtime events with shift performance, which exposed a management issue during night shifts.

How much is downtime costing your plant?

Use the ROI estimator on our Digital Manufacturing page to size potential savings in downtime, OEE, maintenance, energy and quality, then see how TT PSC takes manufacturers from OT/IT connectivity to AI-driven operations, one measurable phase at a time.

Estimate your ROI

Key lessons for data-driven manufacturing teams

Automated KPI calculation is not root cause analysis. PLC data shows how much time a line loses. Finding out why requires additional context.
Context turns downtime data into decisions. Shift schedules, operator data and machine states need to be analysed together, so losses can be traced to specific conditions.
Manual reporting hides losses. Capturing machine states automatically and in real time closes the gap between what happened and what was reported.
Integration beats isolated tools. Connecting PLCs with MES and ERP creates a consistent data foundation. A Unified Namespace architecture helps scale that foundation across lines and plants.
Data shows where to act, people make the change. In this project the fix was organisational: accountability and retraining, guided by evidence from the shop floor.

How machine downtime tracking fits into a digital manufacturing strategy

Downtime tracking is often the first measurable step in digital manufacturing. It relies on the same foundations as every later initiative: industrial connectivity between OT and IT systems, reliable data collection and a shared context for machine, shift and order data. Once that foundation is in place, manufacturers can build on it:

Production monitoring with OEE: live visibility of availability, performance and quality across lines and sites.
Predictive maintenance: signal and event history used to anticipate failures before they cause unplanned downtime.
Predictive quality analytics: machine and process data correlated with quality results to prevent defects.
Digital twin: a consistent data history that makes it possible to model and test production scenarios virtually. Read more about the digital thread and digital twin.
Data and AI for manufacturing: contextualised data as the input for models that support operational decisions, as described in our guide to predictive AI vs generative AI in manufacturing.

Together, these capabilities form the operational core of a smart factory and of Manufacturing Operations Management, where MES, ERP and Industrial IoT data work as one connected system rather than separate islands. For a step-by-step view of that journey, see our digital transformation in manufacturing roadmap and the Industry 4.0 use cases with real results.

Looking for proven use cases?

Download our Industry 4.0 Use Cases Catalogue with measured results from real manufacturing projects, from production monitoring to predictive quality.

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Frequently asked questions

Machine downtime tracking is the process of recording when, for how long and why production equipment stops. Modern systems capture machine states automatically from PLCs and enrich them with context such as shift, operator, product and downtime reason, so teams can analyse losses and act on their root causes.

Summary

Machine downtime tracking delivers results when it explains why a line stopped, not only for how long. For this FMCG manufacturer, automated KPI calculation from PLC data was a good start. Only contextualised downtime data, correlated with shifts and operators and integrated with MES and ERP, exposed the real problem: unreported downtime on night shifts caused by non-compliance with procedures. Accountability and targeted retraining addressed it, machine availability rose, downtime incidents fell and performance and quality targets were met. For any digital manufacturing programme, reliable real-time data with context is the foundation for every improvement that follows.