How do you calculate the ROI of an OEE system?

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

What is the return on investment and how long does it take? The question is important, as there are examples of systems that have cost more than they have returned. Perhaps because they are not used because they are too complicated to operate, or they have stalled as reporting tools without any connection to daily work.

This guide shows you how to build a credible and reasonable business case or ROI calculation. Which savings can actually be realized, 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 is justified by the improvement work, not by 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 less rework, shorter changeover times and lower energy consumption per unit produced. The scale of these varies by industry and baseline, but the total 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, the same hours, and the same staff.

For a factory that would otherwise have had to run an extra shift, hire more operators, or invest in new equipment, this is often the largest single item. It becomes larger the closer the factory already is to its capacity limit.

A line produces 8,000 approved units per shift at 44 percent OEE. If OEE increases to 57 percent, with unchanged planned production time, product mix, and ideal cycle time, this corresponds to approximately 10,364 approved units per shift. The calculation is 8,000 × 57/44. That is an increase of approximately 29.5 percent.

If demand is unchanged, the improvement can instead reduce the required production time. Whether an entire shift can be eliminated depends on production volume, staffing, bottlenecks, and scheduling.

2. Lower downtime costs

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

Calculating the cost of a stopped machine hour is one of the most valuable exercises in a business case. It varies greatly between 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, premises, energy, and management all burden the downtime.

At Sibbhultsverken, technical stoppages were reduced by 73% and unplanned stoppages by 63%. In one cell where they shortened losses, OEE improved by 19.4% in just 12 months. In another cell, OEE was 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, labor 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 detected.

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

4. Shorter changeover times

Changeovers are one of the most hidden areas of loss. They are planned, they appear in the schedule, and they are often not counted as "stoppages" in the same way as breakdowns. But a changeover where the machine has stood idle for three hours represents three hours of unused capacity.

At Bostik in Helsingborg, which manufactures adhesives and sealants with over 80 products on the same line, changeover time fell by 70% on the filling machines following systematic work according to the Lean Six Sigma DMAIC methodology (Define, Measure, Analyze, Improve, Control). 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 management reports. Higher OEE can reduce energy use per approved unit, for example through fewer stoppages and less waste. The effect depends on how energy use and production change and needs to be verified through measurement. Add to this direct work with idle consumption and comparisons between similar machines. Shaving peak demands means reducing the highest simultaneous power load, measured in kW. This can lower power costs depending on electricity contracts and grid fees. It does not automatically mean that total energy consumption, measured in kWh, decreases.

A factory with 100 million euros in turnover can realistically save around 125,000 euros per year on energy by combining productivity improvements with targeted energy measures. Climate impact is reduced by 60 to 80 tonnes of CO2e. For many companies, this is also an item that enters 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 goals in realistic intervals, 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 a number of key performance indicators before the project starts. OEE per line, number of downtime hours per month, scrap and rework costs, changeover time per article change, energy cost per unit produced. 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 goals. 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 over 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.

First-year investment cost. Software license, IoT hardware if needed, implementation, training, internal work by own staff. Include the internal time which should be factored into the total cost.

Ongoing cost from year two and onwards. Software subscription, support, optional hardware additions. This is the cost to be weighed against the continued annual savings.

Payback period and net present value. Payback period is often the figure 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 true in the calculation?

Assumptions that hold true are gradual improvements over time: OEE increase 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 six months. The most common trap is calculating based on 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 the way of working is established.

• Scrap reduction as loss analysis matures.

• Changeover improvements that take 6 to 12 months to yield widespread results.

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

Assumptions that do not hold:

• A 50% OEE improvement during the first six months. This rarely happens, except from a very low starting point.

• Scrap reduction without prioritizing quality analysis. Scrap does not decrease by itself.

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

• That the entire organization suddenly begins to work differently when the system goes live. Change takes time.

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

What is the cost of doing nothing?

Doing nothing costs in terms of lost competitiveness. While you stand still, competitors can increase their capacity. Status quo has a cost that rarely shows up 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 you cannot keep up with. They can invest in next-generation equipment while you are still arguing for this.

Production and energy data can provide input for sustainability reporting. What data and processes are required depends on the company's applicable reporting requirements.

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

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

How do you prevent the business case from being fluff?

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

Anchor 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 every figure. Who owns the OEE increase? Who owns the scrap reduction? Who owns the changeover improvement? If no one is assigned, the calculation becomes just a document, not a plan.

Continuously measure and adjust. The business case is not a one-time exercise. Check in monthly in the first year, quarterly thereafter. Adjust targets when reality shows they were set too low or too high. This is how the improvement work, and thereby the payback, is kept alive.

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 returns are realized. Machine connectivity provides reliable baseline data. Operator tools, dashboards, and reports make the data useful throughout the organization, from the shop floor to the management meeting. Timeline and loss analysis ensure that the right efforts are prioritized. 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 support organization. Experience from implementations in 300+ factories means that business cases are built realistically from the start and followed up together with the customer.

Among results that have been realized: 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% while reducing CO2 consumption by 28%. Kavli produced 5,000 tonnes more in 2024 than the previous year, 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 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 medium-sized factories, the payback period is often just a few months, depending on the starting point and how quickly the ways of working get started. The lower the OEE at the start, the greater the potential. The more mature the improvement work already is in the organization, the faster the platform translates into results.

What should we measure before we begin?
Establish the baseline for at least four things. OEE per line, total number of downtime hours per month, scrap and rework costs, changeover time per article change. 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 include internal time in the investment cost?
Count the time spent by operators, production management, continuous improvement teams, maintenance, and IT during implementation. For a medium-sized factory, this often amounts to 100 to 300 hours internally 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 the goals?
The most likely reason is that the improvement work has not started in daily operations. The platform then becomes an advanced report instead of an improvement tool. Continuous follow-up, preferably together with the supplier, ensures that problems are detected early and that the right measures can be taken.