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SIMMILAR: Systems-of-Systems for Intelligent Manufacturing Maintenance using Industry 4.0, Lean, AI Reasoning


Industrial production is meeting new challenges in the global and digitalized economy where consumers are expecting almost instant delivery and where consumption patterns change rapidly based on quickly evolving trends. Those challenges include:

  • Increasing product variability requiring more flexible manufacturing solutions.
  • Need to optimize cross-organizational supply chains to improve productivity and quality.
  • Faster turn-around time and 24/7 production.

A particular concern relates to the maintenance of manufacturing equipment, which is necessary to uphold and improve productivity, product quality, safety, and environmental care. Given the more rapid changes and constant production, maintenance windows will become shorter. The increasing variability will also mean shorter production series and hence less time to gather experience necessary for a correct analysis of quality and productivity problems.


It is generally believed that a key to addressing these challenges is an increased use of digital technology in manufacturing, to make more information available for planning and follow-up of production and increase automation.

This is clearly manifested in Industry 4.0 (I4.0) and similar initiatives, which strive to seamlessly connect all assets used in a production flow across multiple organizations. I4.0 is thus taking a system-of-systems (SoS) approach, requiring support for collaboration and information exchange across the companies involved in a production and supply chain.

This project strives to provide better support for maintenance engineers through analyses and work methods, combining Lean, basic maintenance management, and artificial intelligence (AI) using extensive data collected across the manufacturing SoS and represented in a digital twin of the production flow.

Project facts

The SIMMILAR project is a cross-disciplinary collaboration between researchers in systems engineering, manufacturing, and AI at Mälardalen University. KTH and RISE are associated partners. The project is running in a first phase from December 2018 to November 2019.


Jakob  Axelsson
Jakob Axelsson
XPRES-funded 072-734 29 52
Mälardalen University Högskoleplan 1 721 23 Västerås Sweden Room U1-062
Belongs to: Excellence in production research (XPRES)
Last changed: May 21, 2020