What is loss analysis and how do you use it in production?

This guide covers how loss analysis works in practice, which questions it should be able to answer, which traps make the analysis misleading, and how factories have used it to reduce stoppages by over 70%.

Why is the OEE figure not enough without a loss analysis?

The OEE figure is not enough, because it only shows that you have a fever without indicating which diagnosis is causing it. Similarly, an OEE of 58% shows that 42% of the planned time did not result in value-creating production, but it says nothing about the cause: breakdowns, changeovers, material shortages, speed losses, or defects. Without that knowledge, it is impossible to choose the right action, and improvement work becomes guesswork.

The loss analysis breaks down the gap. It is based on a loss model where every lost hour is assigned an address: a category, a cause, a location, and a timestamp. The foundation is the TPM tradition's six major losses: breakdowns, changeovers, idling and minor stoppages, reduced speed, defects in normal production, and startup defects. Added to this are micro-stoppages, those short interruptions lasting seconds which individually are insignificant but collectively can account for a large portion of performance losses.

An important insight changes how the analysis should be structured: data from factories that have worked with OEE for a long time shows that around 80% of stoppage causes are often not machine-related. They are about materials, planning, waiting for orders, communication, and staffing. This means that automatic machine data is never enough for a complete analysis. The operators' coding of causes is what gives losses their addresses.

Which questions should a loss analysis be able to answer?

A good loss analysis should be able to answer concrete prioritization questions in a few minutes, without specialist intervention. The most important questions are these:

What are our three largest losses in hours and money? The fundamental question. The answer determines where improvement resources should be deployed.

Which losses are frequent but short, and which are rare but costly? The two types require different actions. A hundred micro-stoppages a day are tackled differently than one breakdown a month.

Why does the same item perform differently on two different lines? The difference between the lines often contains a ready-made solution: the better line's way of working can be standardized for the poorer line.

Which stoppage causes have increased over the last month? Trends catch problems that are on their way to becoming costly before they actually do.

Which technical problems recur on the same machine part? Recurring failures on the same component represent a maintenance pattern pointing to the root cause, not the symptom.

How does the outcome differ between shifts? Differences between shifts are rarely about effort, but often about work methods, and work methods can be standardized.

At Sibbhultsverken, it was this type of breakdown that made the difference. By systematically analyzing and addressing the largest loss categories, they were able to reduce technical stoppages by 73% and unplanned stoppages by 63% in just 12 months. OEE rose by 19.4% in one cell and 40% in another, and net production could be increased by 15% without adding more shifts.

What is required for the loss data to be reliable?

Loss data can be trusted when three things are in place: consistent definitions, simple coding for operators, and visibility of poor data.

Consistent definitions. Planned production time, changeovers, planned maintenance, and ideal cycle time must be handled the same way over time and across lines. Otherwise, the figures cannot be compared and the analysis is built on too weak a foundation. A documented OEE policy, where the team has defined what is measured and how, is the simplest protection.

Simple coding. If it takes longer to code a stoppage than the stoppage itself lasts, the coding will not happen. The interface on the floor should handle coding in seconds, with clear cause codes that the operators themselves have helped to design. Orkla Nidar eliminated the "Other" category from its reporting when operators were given tools that made it easy to code the correct cause directly. A growing "Other" category is otherwise the surest sign that the analysis is losing its foundation.

Visible data quality. The system itself should show where data is lacking: uncoded stoppages, activity outside schedule, unrealistic speed patterns. Then the deficiencies can be corrected continuously instead of being discovered only after someone has already made decisions based on incorrect data.

How do you go from analysis to action?

You move from analysis to action through prioritization, root cause analysis, and follow-up, in a cycle that repeats. Analysis in itself improves nothing. The value arises when it guides action.

A proven workflow looks like this:

Prioritize by value. Choose the two or three losses that cost the most, not the ten that are visible. Doing a few things with proper resources beats doing many without.

Search for the root cause. Feel free to start with a fishbone diagram (called Ishikawa in Lean) to brainstorm and identify causes. All suggestions are welcome. Then the likely causes are prioritized and the most probable ones are investigated using the "five whys" method to find underlying causes. This method generates ideas and focuses on the most likely ones to find the root cause. 

For complex problems, a structured DMAIC project may be needed, as with Bostik where the analysis of changeover data led to 70% shorter changeover times.

Address with owner and date. An action without an owner remains on the list.

Follow up in the statistics. The addressed loss should disappear or decrease significantly in the data. If it doesn't, the problem was not solved fundamentally, and another cycle is needed. The follow-up takes place in the same system as the analysis, making it almost free.

Barilla Wasa in Filipstad demonstrates the big picture. Through systematic work with losses, the utilization rate rose from 75% to 94%, downtime decreased by 22%, product waste decreased by 15%, and net production increased by 15%. At the same time, CO2 consumption fell by 28%, because lost machine time also means lost energy. Loss analysis carried the entire chain, from prioritization to proven effect.

What are the most common traps in loss analysis?

The most common traps are analyzing too rarely, getting stuck in the overall figure, comparing the wrong things, and forgetting the small losses.

Analysis once a quarter. By then, the problem has time to grow costly before it is discovered. Loss analysis must be integrated into the weekly rhythm, with the largest losses as a standing item in the improvement meeting.

Staring at the total OEE. The overall figure moves slowly and hides underlying opposing trends when some causes decrease while others increase. A line can improve changeovers while micro-stoppages increase, and the total remains flat. Analyze the parts, not just the sum.

Comparing lines and factories directly. Different definitions and different conditions make direct comparisons misleading. Compare each unit with its own history, and use differences between units as a source of questions, not as a grade.

Ignoring micro-stoppages. They are barely visible individually and are not captured manually, but together they can represent one of the largest loss categories. Automatic measurement is the only way to see them.

How does Good Solutions work with loss analysis?

Loss analysis is the core of the platform from Good Solutions, built on a proprietary loss model that gives every lost hour an address. The reports break down losses by stoppage cause, cause group, line, item, shift, and trend over time. The timeline shows the 24-hour day visually, with stoppages, volume, and OEE in the same view, and stoppages can be coded directly in the view. The operator tool makes coding fast enough to happen in the moment, which is the prerequisite for the analysis to cover the approximately 80% of stoppage causes that machine data cannot explain.

Around the analysis are the work practices. The operational deployment, with experience from 300+ factories, ensures definitions and data quality right from the start. The OEE policy workshop documents what is measured and how it is measured, so that the figures remain comparable. The training sessions give operators and superusers the common understanding that keeps coding consistent. And because the platform is built to drive continuous improvements, not just measure, the analysis leads directly to prioritization, action, and follow-up in the same tool.

The results for our customers come precisely through this path. Sibbhultsverken reduced technical stoppages by 73%. Bostik shortened changeovers by 70%. Barilla Wasa raised its utilization rate to 94%. Kopparbergs reduced quality deviations by 68%. In all cases, the work began in loss analysis and ended in proven effect.

FAQ

What is the difference between OEE and loss analysis?
OEE is the figure, loss analysis is the explanation. OEE shows what portion of the planned time became value-creating production. Loss analysis breaks down the rest by cause, location, and time, so that improvement work knows where to focus.

Which loss categories should we use?
Start with the six major losses, with micro-stoppages as their own category, and adapt the cause codes to your operations together with the operators. The codes should be few enough to be quick to select and clear enough not to be confused. A growing "Other" category is a signal that the structure needs to be reviewed.

How often should we perform a loss analysis?
Continuously. The largest losses should be a standing item in the weekly improvement meeting, and deviations should be visible in daily management. Quarterly analyses are too infrequent, as problems have time to become costly between occasions.

Can we perform a loss analysis in Excel?
On a small scale, as a start, yes. But manual collection misses micro-stoppages, coding becomes incomplete, and the analysis work takes up time that should be spent on actions. An OEE system collects data automatically, keeps it consistent, and performs breakdowns in minutes.

How do we know the analysis is leading us in the right direction?
Through follow-up. When a prioritized loss is addressed, it should decrease significantly in the statistics. If it does, the analysis is correct. Redo the analysis if the action has addressed a symptom instead of the root cause. In that case, the analysis needs to be deepened, preferably using the operators' knowledge as a complement to the data.