Increase your industrial performance by connecting your equipment

The remote measurement and analysis of industrial performance is one of the most highly anticipated use cases among manufacturing companies. This use case, confirmed by a recent report by IoT Analytics (IoT Use Case Adoption Report), is a standard use case of our platform. It is the first and indispensable step on the path to operational excellence. It lays the foundation for resolving the most complex issues.

Assess and analyse your equipment’s performance with ease

In a production environment where performance is not measured systematically and the gathering of information is often tedious, industrial IoT offers vital decision support for production teams and equipment manufacturers: accelerated performance analysis, an improved understanding of equipment behaviour, facilitated prioritisation of incidents to resolve.

An unparalleled view of your production operations


  • Fact-based, real-time measurement of the industrial performance of your production sites or machines
  • Standardised provision of key indicators and dashboards for industrial performance: overall output, performance, availability, quality, etc.
  • More precise performance analysis, available via several adapted views based on the organisational level being analysed: site, production line, equipment, subset, etc.
  • Reports and dashboards delivered automatically by connected equipment save valuable time and increase productivity for shop floor teams (operators, manager, etc.).

Easily identify incidents that significantly affect production


  • Identify and rank machine defaults and stoppages by their level of impact on production thanks to Pareto diagrams of errors (occurrences, total duration..)
  • Key analytical tools for introducing corrective actions for continuous improvement: changes in industrial processes, part replacements, implementation of predictive maintenance, etc.

Identify the factors that influence your equipment’s performance

How can significant performance differences in recurring production orders be explained? As production equipment and processes have become more complex, a wide range of potential factors come into play. The ability to identify the factors affecting performance exceeds the capacity of human analysis and requires advanced analysis technology.

Analytical capacities vastly increased by machine learning


  • Implementation of advanced analysis based on all available production data representing thousands of variables
  • Systematic identification and ranking of performance factors according to those with the greatest impact on production (costs, unavailability, etc.)

Accelerated continuous improvement processes


  • A better understanding of production processes results in insights that will drive the implementation of new continuous improvement processes
  • Digitalisation and modernisation of continuous improvement methods such as Lean management and Kaizen provide new experience for business teams
  • Significant gains observed in the very the first months using this type of analysis: OEE increase and stabilisation of performance variations over time

Optimise your production scheduling and format changeovers

The implementation of SMED techniques has become standard practice in industry, with the aim of reducing changeover time between series. By systematically measuring these times, the InUse platform facilitates the identification of the longest changeover times and can therefore improve production line availability. Furthermore, advanced analysis provides new perspectives that optimise the scheduling of production orders.

Faster changeovers and improved scheduling


  • Systematic identification of inefficient format changeovers to facilitate the implementation of improved action plans
  • Implementation of advanced analysis for all format changeovers performed over several months
  • Recommendations for optimal format changeover scheduling to increase production availability time and improve operational excellence
  • Optimisation of 10-20% observed in average format changeover times


Format changeovers shortened by 25%
the case of the Hellenic Dairies group

Find out how the systematic identification of deviant changeovers has enabled the Hellenic Dairies Group to reduce its average changeover time by 25%.

Find out more