HVLV environments are characterized by high product variety and small lot production, pushing companies to recursively design and optimize their production systems in a very short time to reach high-level performance. To increase their competitiveness, companies belonging to these industries, often SMEs working as third parties, ask for decision-making tools to support them in a quick and reactive reconfiguration of their production lines. Traditional discrete event simulation models, widely studied in the literature to solve production-related issues, do not allow real-time support to business decisions in dynamic contexts, due to the time-consuming activities needed to re-align parameters to changing environments. Data-driven approach overcomes these limitations, giving the possibility to easily update input and quickly rebuild the model itself without any changes in the modeling code. The proposed data-driven simulation model has also been interfaced with a commonly-used BI tool to support companies in the iterative comparison of different scenarios to define the optimal resource allocation for the requested production plan. The simulation model has been implemented into a SME operating in the footwear industry, showing how this approach can be used by companies to increase their performance even without a specific knowledge in building and validating simulation models.
Designing and optimizing production in a high variety / low volume environment through data-driven simulation
Bindi B.;
2021-01-01
Abstract
HVLV environments are characterized by high product variety and small lot production, pushing companies to recursively design and optimize their production systems in a very short time to reach high-level performance. To increase their competitiveness, companies belonging to these industries, often SMEs working as third parties, ask for decision-making tools to support them in a quick and reactive reconfiguration of their production lines. Traditional discrete event simulation models, widely studied in the literature to solve production-related issues, do not allow real-time support to business decisions in dynamic contexts, due to the time-consuming activities needed to re-align parameters to changing environments. Data-driven approach overcomes these limitations, giving the possibility to easily update input and quickly rebuild the model itself without any changes in the modeling code. The proposed data-driven simulation model has also been interfaced with a commonly-used BI tool to support companies in the iterative comparison of different scenarios to define the optimal resource allocation for the requested production plan. The simulation model has been implemented into a SME operating in the footwear industry, showing how this approach can be used by companies to increase their performance even without a specific knowledge in building and validating simulation models.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
