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


  • KAM is always looking for methods for optimizing its production lines. Despite a vast data set their challenge is to gain a comprehensive overview.
  • 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.



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.

  • 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.
  • 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.
  • Monitoring: Online prediction of production stops should considerably reduce downtime by reducing mean time to repair (MTTR) and further encourage a MaaS production model.
  • Sustainability: Increased insights into environmental footprint of products, from supplier to customer, by utilising DKG. Better forecasting of the need of shopfloor personnel to ensure stable employments.

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


  • JPB production experiences increasing demands and is willing to keep their historic high performance KPIs (OTD, OQD). 
  • JPB is producing mainly for the stock to ensure 100% external OTD to its customers but the growing volumes automatically lead to an increased level of stocks, which, in the current times, means a too significant level of inventory, reducing the available cash.
  • Despite being a highly automated company, there is a lack of a holistic overview that enables them to adapt to external changing market 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 and decrease some internal stocks levels.
  • Enable a MaaS business model.


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

  • 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.
  • 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.
  • Stocks optimization keeping the highest performance standards. JPB will work on a better control of its stock and on an increased robustness of the production / assembly flows to secure the external OTD from the production/assembly itself instead of from the stock
  • 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