How do you calculate the ROI of an OEE system?

The question arises sooner or later in every OEE project. Often it comes from the CFO. Sometimes it comes from the CEO. Sometimes it is the production manager themselves who wants to be certain before the decision is made.

What is the return on investment and how long does it take? This question is important, because there are examples of systems that have cost more than they have returned. Perhaps they are not used because they are too complicated to operate, or they have stalled as reporting tools with no connection to daily operations.

This guide shows you how to build a credible and reasonable business case or ROI calculation. It explains which savings can actually be achieved, which assumptions hold and which do not, how to avoid building fluff into the calculation, and how to present the figures in a way that withstands scrutiny.

The basic principle: an OEE system pays for itself through improvement work, not through the measurement itself. The platform is the tool you pay for. The improvements are what you get back.

Where does the money come from?

The money comes from five levers: higher output from the same machinery, lower downtime costs, less scrap and rework, shorter changeover times, and lower energy consumption per unit produced. The scale of these varies by industry and starting position, but the sum is often surprisingly large.

1. Higher output from the same machinery

The biggest lever in most factories. A typical OEE level of 50 to 60% means that 40 to 50% of machine time is not value-adding. Moving OEE from 55% to 65% yields 18% more production from the same machines, hours, and staff.

For a factory that would otherwise have had to run an extra shift, hire more operators, or invest in new equipment, this often becomes the largest single item. It becomes larger the closer it gets to the factory's existing capacity limits.

A concrete calculation example: A line currently produces 8,000 units per shift at an OEE of 44%. With the same machine, same shift, and same staff, but with OEE improved to 57%, the same line produces 10,000 units. Or if demand is not enough for the extra units, the factory can instead save an entire shift.

2. Lower downtime costs

Downtime costs more than what is visible in the OEE report. Each hour of downtime means idle labour, ongoing fixed costs, energy consumed without producing anything, and delivery risks that can escalate into express freight or lost orders.

Calculating the cost of a lost machine-hour is one of the most valuable exercises in a business case. It varies widely across industries, but a rough rule of thumb is that the hourly cost of a production line is often 2–5 times higher than operator costs. Depreciation, facilities, energy, and management costs all add to the true downtime cost.

At Sibbhultsverken, technical stoppages decreased by 73% and unplanned stoppages by 63%. In one cell where losses were shortened, OEE improved by 19.4% in just 12 months. In another cell, OEE increased by 40%.

3. Less scrap, rework, and quality losses

This item is systematically underestimated. When a product is scrapped or reworked, you have already paid for raw materials, energy, labour time, and, in many cases, packaging. Scrap is not just the cost of raw materials; it is the entire production chain up to the point where the defect was discovered.

At Barilla Wasa in Filipstad, product waste decreased by 15% when the platform was introduced, and the improvement work began. At Kopparbergs Bryggeri, the number of quality deviations fell by 68%. Both figures translate directly into money and provide better capacity utilisation in the same machinery.

4. Shorter changeover times

Changeovers are among the most hidden sources of waste. They are planned, appear in the schedule and are often not counted as "downtime" in the same way as breakdowns. But a changeover where the machine has been idle for three hours is three hours of capacity that is not utilised.

At Bostik in Helsingborg, which manufactures adhesives and sealants with over 80 products in the same plant, changeover time for the filling machines fell by 70% after systematic work following the lean Six Sigma DMAIC (Define, Measure, Analyse, Improve, Control) methodology. OEE improved by 40%. One of the key insights was that two operators working together completed the changeover faster than two working on separate machines. This is not a hypothesis; it is a measurement made visible by the platform.

5. Lower energy consumption per unit produced

Energy has gone from being a background cost to appearing in executive reports. Energy consumption per unit produced automatically decreases as OEE rises because the same kWh is used for more units. Add to that direct work on idle consumption, "peak shaving," and comparisons between similar machines.

A factory with 100 million euros in turnover can realistically save around 125,000 euros per year on energy by combining productivity improvement with targeted energy measures. Climate impact is reduced by 60 to 80 tonnes of CO2e. For many companies, this is also an item that is included in sustainability reporting and strengthens the brand.

How do you build the business case?

Build the business case in six parts: define an honest current state, set quantified targets in realistic phases, calculate annual savings distributed across the five levers, sum up the investment cost for the first year, state the ongoing cost for year two and onwards, and calculate payback period and net present value. This structure works in most presentations to management.

Current state (baseline). Define key performance indicators before the project starts. OEE per line, number of downtime hours per month, scrap and rework costs, changeover time per article swap, and energy cost per produced unit. This is the reference point against which the entire calculation will be measured. Without an honest baseline, it is impossible to prove an improvement.

Quantified targets. State realistic intervals, not single points. Example: OEE increase of 5–15 percentage points over 12 months. Scrap reduction of 10 to 25%. Changeover time 30 to 50% shorter. Use results from similar factories in the same industry as a benchmark, not the most spectacular examples.

Calculated savings per year, distributed across five levers. Add them up to a total annual saving. Be consistent: if the OEE increase already includes fewer stoppages, do not double-count the downtime cost in the same calculation.

Investment cost for the first year. Software license, IoT hardware if needed, implementation, training and internal work by own staff. Include the internal time, which should be counted in the total cost.

Ongoing cost from year two onwards. Software subscription, support and any hardware additions. This cost must be weighed against the ongoing annual savings.

Payback period and net present value. Payback period is often the figure that management looks for first. Net present value over a three-year or five-year horizon provides a more accurate picture. Use the company's normal discount rate.

Which assumptions hold in the calculation?

Assumptions that hold are gradual improvements over time: OEE increases in lower ranges in year one, scrap reduction as loss analysis matures, and changeover improvements over 6 to 12 months. Assumptions that do not hold are quick miracle results, such as a 50% OEE improvement in half a year. The most common pitfall is to calculate using the best case as if it were the expected case.

Assumptions that hold:

• OEE improvement in lower ranges for year one (5 to 10 percentage points), higher in years two and three, once working methods have settled.

• Scrap reduction as loss analysis matures.

• Changeover improvements that take 6 to 12 months to have a broad effect.

• Energy reduction as data becomes available and measures are implemented.

Assumptions that do not hold:

• 50% OEE improvement during the first half-year. Rarely happens, except from a very low starting point.

• Scrap reduction without prioritising quality analysis. Scrap does not decrease on its own.

• Energy savings without machine-level measurement. You cannot improve what you cannot see.

• The entire organisation suddenly begins working differently when the system is deployed. Change takes time.

Build the calculation with three scenarios: pessimistic, realistic and optimistic. Defend the realistic one. That is where you land in the conversation with management.

What does it cost to do nothing?

Doing nothing comes at a cost in terms of lost competitiveness. While you stand still, competitors can increase their capacity. You risk expensive, separate CSRD reporting tracks, and without ISO 27001-certified software, you could be losing business today. The status quo has a cost that is rarely visible on the income statement, but is there nonetheless.

If a competitor improves OEE from 55 to 65% while you stand still, they have gained 18% more capacity from the same machinery. They can take orders you cannot meet. They can push prices that you cannot keep up with. They can invest in next-generation equipment while you are still arguing for this.

If CSRD reporting becomes mandatory and you do not have structured energy data, you will need to set up a separate reporting track. This becomes both expensive and lower-quality than if the data were in the same system as the production monitoring.

If you do not have ISO 27001-certified software, you could already be losing business today. Larger customers and the public sector set this as a hard requirement in procurements.

Including the cost of doing nothing, even roughly, makes the business case more accurate. It shows that the alternative is not "saving the investment," but rather "losing competitiveness over time."

How do you avoid the case being fluff?

Avoid fluff through three things: ground the figures in your own reality, assign responsibility for each number, and measure continuously and adjust. This is what distinguishes a business case that holds from one that falls apart.

Ground the figures in your own reality. Use your own downtimes, scrap costs, and changeover times. Real values are more important than spectacular increases from other people's factories.

Assign responsibility for each number. Who owns making the OEE increase happen? Who owns the scrap reduction? Who owns the changeover improvement? If no one is singled out, the calculation becomes a document rather than a plan.

Measure continuously and adjust. The business case is not a one-time exercise. Check in monthly for the first year, quarterly thereafter. Adjust targets when reality shows they were set too low or too high. That is how the improvement works; therefore, the payback is maintained.

How does Good Solutions work with the business case?

The platform from Good Solutions is built to drive improvement work in daily operations. That is where the money pays off. Machine connectivity provides reliable baseline data. Operator tools, dashboards, and reports make the data useful throughout the organisation, from the factory floor to the management meeting. Timeline and loss analysis ensure that the right efforts are prioritised. The energy module allows cost savings and sustainability goals to be tracked in the same platform.

Operational implementation is just as important as the software. The platform is delivered with expert support from consultants with production experience, a dedicated Customer Success Manager who follows the customer over time, and a Swedish-based support organisation. Experience from more than 300 factory implementations means business cases are built realistically from the start, and followed up on together with the customer.

Among the results achieved: Sibbhultsverken increased OEE by 19.4% in 12 months and reduced technical stoppages by 73%. Bostik improved OEE by 40% and shortened changeover time by 70%. Barilla Wasa increased net production by 15% whilst simultaneously reducing CO2 consumption by 28%. Kavli produced 5,000 tonnes more in 2024 than the year before, without more shifts or more machines. That type of result is built step by step, driven by improvement work, with the platform as the engine.

Read more about how others have increased their factory's productivity

FAQ

What does an OEE system cost?
It depends on several aspects. It can be the number of machines and users, which modules you need, and how the implementation is set up. Good Solutions works with subscriptions that include everything you need: software, IoT hardware, cloud operations, support, and updates.

How long is a typical payback period?
For mid-sized factories, the payback period is often just a few months, depending on the starting position and how quickly new work methods are implemented. The lower the OEE at the start, the greater the potential. The more mature the improvement work already is in the organisation, the faster the platform is converted into results.

What should we measure before we begin?
Set the baseline on at least four things: OEE per line, total number of downtime hours per month, scrap and rework costs, and changeover time per article swap. For many factories, energy consumption per unit produced is also added. Without a documented baseline, it is impossible to prove an improvement.

How do we factor internal time into the investment cost?
Calculate time for operators, production management, continuous improvements, maintenance, and IT during implementation. For a mid-sized factory, this is often 100 to 300 internal hours during the implementation period. Add a realistic hourly cost. That figure should be included in the total cost.

What happens if we do not reach our targets?
The most likely reason is that the improvement work has not started in daily operations. The platform then becomes an advanced reporting tool rather than an improvement tool. Continuous follow-up, preferably together with the supplier, ensures problems are discovered early and that the right measures can be taken.