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Tracking unplanned downtime represents a fundamental element of industrial performance. Precise measurement and analysis of machine downtime allow identification of inefficiencies, optimization of production processes, and significant improvement of overall industrial productivity. Through the lens of OEE (Overall Equipment Effectiveness) and TEEP (Total Effective Equipment Performance), companies can quantify their losses related to unplanned downtime and precisely target their improvement actions. In Quebec's manufacturing sector, companies have managed to increase their productivity by 25% to 35% simply by reducing their unplanned downtime by half, which represents annual financial gains often exceeding $500,000 for a typical SME without major equipment investment.
OEE (Overall Equipment Effectiveness) constitutes the first essential indicator for evaluating the efficiency of your industrial equipment. You've probably already noticed that without precise measurement of unplanned downtime, it's difficult to identify where productivity losses are hiding in your factory. OEE isn't the ultimate indicator, but rather the essential starting point for any improvement approach.
What makes OEE so relevant is its composition. It results from multiplying three independent sub-indicators:
This breakdown allows precise identification of where problems related to unplanned downtime are located: are your machines stopping too often, running too slowly, or producing too many defects?
Let's take a concrete example: a plastic injection press operating on an 8-hour shift experienced 120 minutes of unplanned downtime, runs at 80% of its nominal speed, and produces 95% conforming parts. Its OEE is calculated as follows:
This result means that the press is only truly productive for 57% of the planned time. Analysis of the 120 minutes of unplanned downtime often reveals considerable improvement potential: if you manage to reduce these stops by half, your availability rate would increase to 87.5% and your OEE to 66.5%, representing a productivity increase of nearly 17%.
You may be wondering how to distinguish OEE from TEEP. The fundamental difference lies in the calculation of the reference time:
OEE (Overall Equipment Effectiveness) uses planned production time as a reference. This means that planned stops (breaks, preventive maintenance, etc.) are already subtracted from the total available time.
TEEP (Total Effective Equipment Performance) takes into account the total available time, including planned stops. It therefore offers a more global vision of equipment utilization.
To illustrate this difference, let's examine a complete case over a 24-hour period:
Total available time: 24 hours (1440 minutes)
Planned stops: 8 hours (480 minutes) including:
Planned production time: 16 hours (960 minutes)
Unplanned downtime: 4 hours (240 minutes)
Actual production time: 12 hours (720 minutes)
OEE Calculation:
TEEP Calculation:
This comparison shows that the same machine can display a relatively acceptable OEE of 62.5% while having a much lower TEEP of 41.7%, highlighting different optimization opportunities depending on the indicator considered.
The current situation in factories may be surprising: according to data collected by the Industrial Maintenance Institute in 2022, the average manufacturing machines in Quebec display an availability rate of only about 35%. When this figure is multiplied by performance rates (often around 75%) and quality rates (generally between 90% and 95%), the resulting OEE frequently oscillates between 20% and 25%, highlighting the significant impact of unplanned downtime on productivity.
This reality represents both a challenge and an opportunity. You've probably already noticed that the natural reflex when facing a lack of productive capacity is to invest in new equipment. However, improving the OEE of existing machines by reducing unplanned downtime often offers a much greater potential for gain at lower cost. An improvement of just 10 percentage points in OEE (for example from 25% to 35%) represents a 40% increase in production capacity without any investment in additional equipment.
To precisely measure the causes of unplanned downtime, several approaches are possible:
The objective is to clearly determine when a machine is running or stopped. It's not simply about knowing if the machine is powered on, but whether it is actively producing. More importantly, when it is stopped, the causes must be identified and categorized.
Unplanned downtime can result from:
Different tools can be used to effectively measure unplanned downtime:
The choice of tool often depends on the digital maturity level of the company and the strategic importance of the equipment concerned. In Quebec's food processing industry, a processing company managed to increase its OEE from 42% to 68% over six months simply by implementing a tablet-based unplanned downtime tracking system and involving operators in cause analysis. This improvement translated into a production capacity increase of more than 60%, allowing them to meet growing demand without investing in a new production line.
Once data is collected, different methods can be applied:
Pareto analysis typically reveals that 20% of downtime causes are responsible for 80% of lost time. This concentration allows for effectively targeting improvement actions. In an electronic component manufacturing plant, analysis revealed that three types of failures on critical equipment represented 65% of downtime. By concentrating their efforts on these three problems, they reduced unplanned downtime by 47% in just three months.
Measurement can be done by counting:
This is often the most difficult aspect to measure in real time, except for highly automated lines with integrated control systems. Nevertheless, even an incomplete OEE (without the quality indicator) remains valuable for analyzing and improving unplanned downtime.
The concept of OEE and tracking unplanned downtime can be applied well beyond individual machines. This multi-scale approach provides a coherent view of performance at all organizational levels:
At the individual machine level, tracking unplanned downtime identifies equipment-specific problems. For example, a CNC milling machine in a machining workshop may experience recurring stops related to material feeding problems or tool wear. Fine analysis of these stops at the machine level allows establishing a targeted maintenance program and optimizing tool change procedures.
At the scale of a complete production line, OEE helps detect bottlenecks and problematic interactions between equipment. In a bottling line, for example, frequent stops of the labeler may be caused by upstream problems on the filler. Line OEE identifies these interdependencies that machine-by-machine analysis would not reveal.
For manual workstations, the concept adapts by measuring productive time versus waiting time or activities without added value. In a manual assembly workshop, applying OEE allowed a manufacturing company to identify that 40% of operator time was devoted to searching for components or tools. Reorganizing the workstation reduced these unproductive times to less than 15%.
At the scale of an entire plant, aggregated OEE offers a macroscopic view of performance and allows comparing different departments or processes. A metallurgical plant thus discovered that its finishing department displayed an average OEE of 30% compared to 55% for the machining department, which oriented improvement priorities.
Finally, for a multi-site company, comparative OEE between plants highlights best practices and standardization opportunities. A manufacturer of electrical equipment with three plants in Quebec found OEE gaps ranging from 38% to 62% on similar production lines, which triggered a program for sharing best practices between sites.
This flexibility allows "zooming" to different levels of the organization and precisely identifying where productivity losses related to machine downtime are concentrated.
The collection of data necessary for calculating OEE and tracking unplanned downtime varies according to equipment:
For recent machines equipped with IoT (Internet of Things) systems, data can be collected via standard communication protocols such as OPC-UA, MQTT, or ModBus. These solutions offer exceptional precision and allow real-time monitoring. The investment generally ranges between $1,000 and $5,000 per piece of equipment depending on the complexity of the system and necessary integrations. The main advantage is the complete automation of collection, eliminating human errors and freeing up time for analysis rather than data entry.
For older equipment, retrofit solutions can capture relevant electrical signals and transform them into usable indicators for tracking downtime. These systems, costing between $500 and $3,000 per piece of equipment, generally consist of non-invasive sensors that detect machine activity (vibration, electrical consumption, etc.) without requiring major equipment modification. Installation can be done in a few hours and generally does not require prolonged production shutdown.
For companies with a limited budget, more pragmatic approaches remain effective: shop floor tablets for manual entry by operators, visual tracking boards updated each shift, or even use of dedicated mobile applications with investment limited to a few hundred dollars per workstation. These solutions, although less automated, can constitute a very effective first step, especially when coupled with strong team involvement.
The important thing is to start with the essentials: if preliminary analysis shows that problems are mainly related to availability, first focus on this measure before complicating your unplanned downtime tracking system. A wood processing company thus began with a simple manual tracking board for stops longer than 10 minutes, which already allowed identifying and resolving problems representing more than 20% of their lost production time.
Once data is collected, analysis of unplanned downtime allows:
To structure this prioritization, an impact/effort matrix proves particularly effective. Here's how to apply it:
For each identified cause of downtime, evaluate:
Actions to prioritize are those with high impact and low effort. For example, in a metal parts factory, analysis of unplanned downtime revealed that tool changes represented 35% of total downtime. The impact was therefore very high. A finer analysis showed that tool preparation before the change (relatively low effort) could reduce this time by 60%. This action was therefore prioritized and allowed an immediate gain of 21% on total downtime.
A typical action plan to reduce unplanned downtime could be structured as follows:
This methodical approach avoids dispersing improvement efforts and maximizes their impact on reducing unplanned downtime and overall performance.
To effectively reduce machine downtime and optimize your industrial productivity, here are the recommended practices in detail:
Implementing a standardized classification system for unplanned downtime is essential to ensure consistent data collection. This standardization must include a precise definition of each downtime category, with concrete examples to avoid ambiguities. For example, clearly distinguishing an "electrical failure" from a "mechanical failure," or a "quality adjustment" from a "series change." A plastic products company thus developed a taxonomy of 25 types of stops, grouped into 5 main categories, which improved analysis precision by 65% and identified problems previously masked by overly generic categorizations.
Involving operators in the process of reporting and analyzing unplanned downtime strengthens data accuracy and accelerates solution identification. To maximize this involvement, it is crucial to establish a simple and immediate information reporting system, such as touch tablets with intuitive interfaces directly in the production area. More importantly, operators must see that their reports lead to concrete actions. An electronic equipment manufacturer established daily 15-minute "downtime meetings" where production and maintenance teams together analyze the main stops from the previous day and decide on immediate actions, which allowed resolving 40% of recurring problems in less than three months.
"OEE improvement begins with precise and categorized measurement of downtime. Without this foundation, all improvement actions are merely hypotheses," explains Marc Tremblay, Operations Director at a major Quebec manufacturer. "Since we implemented our unplanned downtime tracking system, our overall OEE has increased from 32% to 58% in 18 months."
Establishing progressive OEE improvement objectives is preferable to unrealistic ambitions. An effective approach is to aim for a 5% reduction in unplanned downtime each month for six months, rather than an immediate 30% reduction. These progressive objectives must be associated with specific intermediate indicators, such as the number of maintenance interventions per week or the average duration of tool changes. A food processing company thus set an intermediate objective of reducing format change time from 45 to 30 minutes, before aiming for a more ambitious objective of 15 minutes, which allowed consistent and motivating progression for the teams.
Visualizing downtime tracking data in the production workshop helps raise awareness among all staff and maintain the improvement dynamic over the long term. Displays must be simple, visual, and regularly updated (ideally in real time or at least daily). Intuitive color codes, evolution graphs, and comparisons with objectives make the information immediately understandable. An automotive parts manufacturer installed large screens displaying in real time the OEE of each line and the main causes of downtime, which created positive emulation between teams and contributed to an overall OEE improvement of 18% over one year.
"Visualizing performance data in real time has completely changed our company culture," testifies Isabelle Côté, Chief Engineer of a manufacturing SME. "Previously, no one knew our actual OEE. Now, it's the first topic of discussion at our team meetings, and everyone proposes solutions to reduce unplanned downtime."
Systematic analysis of the first stops of shift or day can reveal recurring problems often neglected. These "initialization" stops frequently represent a significant portion of total unproductive time. A metallurgical company found that the first 30 minutes of each shift had a downtime rate three times higher than the rest of the day. By standardizing startup procedures and implementing preventive checks, they reduced these early shift stops by 70%, increasing their overall OEE by 8 points.
Documentation and sharing of best practices between teams and between sites constitute a powerful lever for improvement. Effective solutions developed by one team can often be transposed elsewhere with minimal adaptation. An accessible knowledge management system, grouping resolved problems and their solutions, considerably accelerates the resolution of new stops. A multi-site manufacturing company created a shared database of solutions to unplanned downtime, reducing the resolution time of recurring problems by an average of 65%.
Measuring and analyzing unplanned downtime constitutes the fundamental basis of any approach to improving industrial productivity. OEE, with its three components (availability, performance, quality), offers a structured vision of improvement opportunities to reduce machine downtime and generate substantial gains without major equipment investments.
The current reality of industrial performance, with OEE often below 35%, reveals considerable improvement potential. By precisely identifying the causes of unplanned downtime, their frequency, and their impact, companies can target their improvement actions and obtain significant gains, often exceeding 30% additional productivity.
Let's remember that it's not necessary to implement a perfect system from the start. Beginning by measuring the most critical aspects, such as availability and main unplanned downtime, already constitutes a major advance. The essential thing is to engage in the process of tracking downtime and to include it in a logic of continuous improvement of industrial productivity.
To take action right now, we recommend:
This methodical approach, even applied on a reduced scale initially, will quickly demonstrate the potential of unplanned downtime analysis and create a positive dynamic of continuous improvement within your organization.
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