Digital Twin Implementation in the Logistics and Production Domain at the SOCOMEC Facility in Isola Vicentina

Company name

Sincon Srl

project title

Digital Twin Implementation in the Logistics and Production Domain at the SOCOMEC Facility in Isola Vicentina - DTW-ISIV

dimension and location

Large Enterprise, Isola Vicentina (VI)

technologies

Digital twin
Lean Manufacturing 4.0

Socomec is a French industrial group, established in 1922, specialized in low voltage power supply management. With over 3200 employees worldwide, it operates in key sectors such as Critical Power, Power Control & Safety, Solar Power, and Energy Efficiency. Internationally present with 30 subsidiaries across five continents, Socomec stands out for its innovation, customer proximity, and solutions tailored to the specific needs of each application.

The problem

Within the company, the process of modifying and introducing new lines, and the resultant changes to logistics, is managed by the Industrialization department. Once the production capacity is determined, a static 2D layout is produced and then implemented on-site. This lacks full sharing with all other company entities, detailed flow analysis, and preemptive interference calculation.

The solution

The project began with an assessment phase where the current production lines and raw material warehouse were analyzed. It then focused on creating a digital model of the logistic and production area to highlight safety concerns, handling issues, and efficiency improvements.

With the digital model, we have a virtual environment in which to test new layout and process solutions without necessarily moving to the implementation phase. This allows for an initial sharing phase with various departments, a more accurate evaluation of safety aspects for both line and warehouse operators (eliminating all interferences), and a more precise efficiency evaluation before moving to the investment phase.

Essentially, the project virtualized the facility and enabled decisions to be made more accurately and collaboratively, reducing the costs of rework caused by incorrect evaluations.

Participation in this project also allowed for the acquisition of internal expertise in constructing and utilizing a digital twin. Indeed, the company intends to promote the use of this tool to support its industrial operations, primarily for optimizing existing processes/facilities and designing new ones.

Technologies:
  1. Digital Twin

Desired Impacts

The project initiative has led to several industrial impacts as listed below:

  • Sicon must focus on flexibility and responsiveness. This means quick changes to production lines, new layouts, within short timespans (under three months) to produce new products and respond to market demands.
  • The digital model will allow us to automatically delineate areas equipped from those requiring operator intervention, drastically reducing unreported accidents. The preemptive evaluation of anticipated movements, both with machinery and at workstations, reduces interferences and increases the ergonomics of the workspace.
  • Being able to digitally simulate avoids waste in terms of scrap materials during workstation construction, allows for the preemptive definition of packaging types, reduces the variety of containers used, optimizes internal spaces reducing the industrial footprint, and decreases energy usage due to non-optimized lines.
  • Digital efficiency helps reduce the impact of direct costs on the product. This keeps production in Europe competitive against the Far East (India, China, etc.), ensuring higher product quality and closer proximity to the European market due to flexibility, thus reducing production lead times.

Benefits for the company

  • Expected increase in warehouse efficiency (line servicing times, material recall times from suppliers) with the implementation of the simulated scenarios.
  • Expected increase in productivity (products/day) with the implementation of the simulated scenarios.
  • Reduced warehouse accidents.
  • Reduced production line accidents.
  • Expected increase in production lead time (internal throughput time with the implementation of the simulated scenarios.
  • Inventory of raw materials (in million euros).
  • Investments in production lines (capitalization cost).

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