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Demo Cases

These demo cases illustrate how our innovative platform can significantly enhance resilience, adaptability, and sustainability in manufacturing.

 

Demo Case 1: Electronic Sector

KAMSTRUP (KAM), a world-leading manufacturer of system solutions for smart energy and water metering, empowers utilities and societies to reduce water loss and increase energy efficiency by providing them with the insight to act and the data to target their efforts. KAM enables their customers to make more certain, smarter business decisions and inspire the communities they serve by providing them with intelligent tools and data.

Production Line

Challenges

  • KAM struggles to identify areas for improvement and gain a comprehensive overview of their operations despite having vast data on their production line.
  • With a 20% annual growth rate and lengthy lead times for acquiring new equipment, it is crucial to simulate their impact in advance.
  • The complexity of the product portfolio and supply chain makes it difficult to realistically assess different scenarios.
  • Challenges in identifying bottlenecks within a workcentre due to product type differences and inter-robot dependencies.
  • External threats such as unforeseen drops or surges in demand and significant supply disruptions.
  • The company’s advanced level of automation and digitalization, as well as established contingency plans aligned with the DMaaST project, make them an ideal use case to showcase DMaaST’s capabilities.

Opportunities

Thanks to DMaaST project, KAM will have access to a powerful tool for enhancing decision-making (MO-DSS), data management and arrangement (DKG), and production impacts (DTs), as the DMaaST’s SMAP platform provides a comprehensive overview of the production process and value chain information.

  1. Simulation: Simulating alternative production setups involving new equipment and/or changed configurations of existing equipment. This will provide comprehensive insights into the impact on specific workcentres as well as the full value chain thanks to the manufacturing ecosystem two-levels DT.

  2. Learning: By learning the properties of integrated production setups locally and between work-centres, KAM will be able to optimize production efforts and achieve the largest gains.

  3. Monitoring: Online prediction of production stops should considerably reduce downtime by reducing mean time to repair (MTTR) and further encourage a MaaS production model.

Demo Case 2: Aeronautic Sector

JPB système designs, develops and produces an elite set of ready-to-use Lockwireless Anti-Rotational Devices. Initially made for the aeronautic and aerospace industry, however, their production is cross-sectorial, being also applied across the automotive, railways, marine, and nuclear industries. JPB is committed to sustainability and research and innovation activities to improve the production in those terms (additive manufacturing, etc.).

Production Line

Challenges

  • JPB production experiences fluctuating demand due to the great complexity of the sectors in which they operate, mainly the aeronautics sector.
  • These changes can be found at the level of suppliers with potential shortages in both raw materials and processing capacity (COVID-19 can be used as reference).
  • Despite being a highly automated and digitalised company, there is a lack of a holistic overview that enables them to adapt to external changing conditions and swiftly adapt production.
  • The challenges to be solved are to allow better resilience in production according to the hazards linked to external factors.
  • Enable a MaaS business model.

Opportunities

The SMAP platform suits perfectly for this use case thanks to the data availability level in JPB.  DMaaST will help in:

  1. Internal anticipation. The manufacturing services cognitive DTs will simulate “what-if” based on the status of each phase. Next, identify potential threats, arise risks to end-user and evaluate the impacts on the other phases.
  2. External anticipation. Thanks to the scenarios generated by the value-chain cognitive DT, anticipate potential risks along the value-chain due to dependencies on external entities and evaluate the impact on JPB production.
  3. Decision-making. Speed-up and enhance the decision making suggesting several solutions for each objective of the company, while taking into consideration possible outcomes and impacts. Ease the multi-objective optimisation.

Overall benefits

Production efficiency and resilience

Production adaptability and agility

Improvements in logistics 

Improvements in production scheduling 

Sustainability

Project financed by: Horizon Europe Programme, HORIZON-CL4-2023-TWIN-TRANSITION-01-07. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. 

 

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