Predictive technologies for Industry 4.0.

Measurements 4 Management

The aim of Measurement 4 Management (M4M) is to develop operational predictive technologies for Industry 4.0.

Firstly, the predictive technologies should be transparent and include all available process information. Secondly, they provide predictions of Key Performance Indicators on Safety, Environmental Sustainability and Economic Performance, and thirdly, they engage all relevant stakeholders in the company to understand how to optimally implement the M4M methodology in practice. This is in direct alignment with the TKI E&I mission to “create a sustainable and inclusive industry that operates within the boundaries of climate and environment”.


Process industry is under continuous pressure to not only return high profits, but also to improve their short term and long term sustainability standards. Each company has to reach a specific leverage between people, planet and profit (Cramer, 2005).

Insights from Digital Technologies, termed as Industry 4.0, are at the core of finding potential synergies and trade offs between economic profit and sustainability targets. Current digital technologies in Industry 4.0 allow, however, for only limited inclusion of process information available within companies, while the degree of acceptance of such digital technologies as managerial and operational tools is rather limited. Gains in sustainability, aligned with cost reductions, increases in safety and product quality and adequate market compliance do however require involvement of all stakeholders within the company; from manager to process operator.

Description of activities

M4M investigates how to integrate growing data streams from various sources towards meaningful high-level business performance indicators. It also addresses the industrial need to realize predictive sustainability performance indicators, with a low threshold to give support to design decisions. Care is taken that the predictive systems provide transparency in their decisions and engage the end-user in performing their tasks.

In M4M, data analysis methods are developed to enable inclusion of novel information and prediction of Key Performance Indicators for Business excellence (BNR Webredactie, 2019). The Department of Analytical Chemistry & Chemometrics at Radboud University leads this task, as they have world-class expertise in the development of data science methods specifically for chemical, physical and other measured data.

Relevant and feasible key performance indicators of Safety and Environmental Sustainability for the predictive evaluation of chemical process optimisation and management are systematically identified and implemented in the M4M methodology. The department of Environmental Sciences at Radboud University will take up this task.

The M4M project addresses the acceptance issue and involvement of employers to tackle the complaint of nearly half the employer population: that they are not or very late involved in the digitization of their tasks and environment. Increasingly, the human factor is considered to be crucial for the optimal implementation of Industry 4.0.

In this project, Wageningen University studies how Industry 4.0 impacts employees’ jobs and responsibilities and how to involve employees and their managers in co-creating Industry 4.0 solutions.

The Industrial partners contribute case studies, consisting of running or developed processes that have a perspective on reducing environmental footprint within scope of economic performance and employee safety. The companies engage their Industry 4.0 teams to provide their chemical, process and sustainability knowledge to the academic development of the methods. This adds value for the partners themselves, but also leads to engaging case studies that are suitable as value proposition to other industries to enrich societal impact of the M4M results.

Acknowledgement & partners