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What value does a 1% increase in productivity in production offer?

Published: · Last updated: · 5 min reading time

In addition to investment in other plants and machinery, better organization and use of existing production capacities can offer considerable potential. Digitization of production processes and the establishment of a smart factory allow these potential opportunities to be targeted and shift the use of investment resources and attention to expanding production capacities further into the future.

What added value can digitization offer your production? We demonstrate a simple method for assessing the monetary added value of a smart factory. As a basis, we use the calculation of a 1% increase in productivity (typical productivity gains are significantly higher) of a machine, more precisely, the overall equipment effectiveness (OEE). For simplicity, we ignore secondary effects (which contribue added value) and obtain a strong, albeit conservative, estimate that is a lower bound of the value of digitizing production.

We provide an Excel tool for a pragmatic assessment of the digitization of production in your context.

Procedure: Calculate the monetary value of a 1% increase in productivity

First, it is necessary to calculate the operating hours per machine per year, multiply that by the operating costs per hour (hourly personnel rate and hourly machine rate) and then break that down to 1%. This percent represents the potential revenue per machine.

Calculation of operating hours

The total operating hours of a machine are calculated based on:

  •  the number of working days per year (influenced by company vacations, work days per week, and other factors),
  •  the shift model (1-shift, 2-shift, 3-shift) and
  •  the duration of a shift.

Operating hours = working days per year x number of shifts x time per shift.

Thus, this parameter describes the planned annual working hours.

Calculation of operating costs per year

The hourly rate per hour is the sum of the hourly personnel rate and the hourly machine rate. The hourly machine rate usually results from an extensive calculation of variable, quasivariable and fixed costs. For the sake of simplicity, we take it as a first approximation (for certain operating hours).

Hourly rate per machine = personnel cost rate + machine hourly rate

Then determine the operating costs for the planned working hours in one year by multiplying the scheduled hours by the hourly rate per machine.

Operating costs per year = Operating hours x hourly rate per machine

The operating costs per year describe the total costs incurred per year.

Value of productivity increase

Let’s do a thought experiment to determine the monetary value of the productivity increase: To achieve 1% more output, operating hours can be expanded by 1%. This increases operating costs, under our assumptions of a constant labor and machine hour rate, also by 1%.

If you can achieve the same 1% increase in output without expanding operating hours but through higher productivity, the value of higher productivity is equal to the cost of increasing operating hours. It can be stated as the value of a one percent increase in productivity:

Value of a 1% increase in productivity = operating costs per year / 100.

Increasing productivity through connectivity, visibility and transparency

Now let’s take a closer look at how digitizing production can reduce unproductive time on the shop floor and thus increase productivity. Digitizing production starts with establishing transparency in production to make informed decisions with short-, medium- and long-term implications. A necessary foundation is an up-to-date, comprehensive and trustworthy description of the status and history of production. A machine connection and automated machine data acquisition are of central importance.

Machine connectivity and production data acquisition

The primary goal of machine connectivity is to create a reliable data basis to gain visibility and transparency about manufacturing events. The first step towards a smart factory is, therefore machine connectivity. For this purpose, both modern plants and existing plants must be connected. The connection of brownfield machines poses several challenges. Machine manufacturers use different communication standards, meaning that a connectivity solution must be able to understand and standardize various protocols.

Machine data connectivity (MDE) combined with store floor data collection (SFDC) creates the data basis for continuous calculation of OEE. FORCAM provides a production data model for this purpose, a foundation for different use cases and systems. Existing and future systems can be efficiently integrated via open interfaces and fed with the data from production.

Visibility and transparency for production optimization

Overall Equipment Efficiency (OEE) makes it possible to uncover waste in the three areas of equipment availability, performance level and quality level. OEE thus creates the necessary transparency about the performance of a plant or machine. Disturbance factors that reduce the planned performance can be filtered out with the help of a root cause analysis and addressed with improvement measures.

The resulting benefits can have an impact on all areas of OEE, e.g.:

– Increased plant availability leads to more parts produced

– Improving the performance rate improves planning and costing

– Increase in quality rate reduces scrap and material costs

Derive measures to increase productivity

Once a plant identifies the causes of poor productivity, it can introduce targeted measures to increase productivity. In this context, improving production through a continuous improvement process (CIP) is advisable. The effect of CIP measures can be quantified and evaluated over time based on a continuously recorded key performance indicator (OEE): Were the original goals achieved, or should further or alternative improvement measures be initiated?


The business impact of an investment in the digitization of production can be measured in a first approximation by changes in productivity. A simplified estimate of the expected benefits can be helpful for this purpose. MDE and BDE combined with the use case OEE creates the necessary transparency about plants. Causes for low plant availability can be identified and eliminated by targeted measures. The effect of the optimization measures can be calculated. Acting instead of reacting, manufacturing transparency enables companies. These benefits are offset by the initial and ongoing costs of digitized production.

Would you like to estimate the profitability of an investment in the digitization of your production? Please write to us or use our simple Excel tool to evaluate the effects you can expect. We will be happy to help you further estimate the value and show you a path to leverage that value.