Short answer

Integrate comprehensive, multi-level simulation models into the design and operational planning of manufacturing systems to proactively identify and address energy inefficiencies.

Field: Modelling
Source: Journal of Manufacturing Systems (2019)
Method: Literature Review and Framework Development
Evidence: Strong effect

Comprehensive computational models that integrate machine-level analysis with process chain and built environment considerations can significantly improve energy efficiency in manufacturing. This modelling research insight is drawn from a 2019 study published in Journal of Manufacturing Systems. Using Literature review and framework development, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Integrate comprehensive, multi-level simulation models into the design and operational planning of manufacturing systems to proactively identify and address energy inefficiencies.

Study

ModellingHigh ImpactStrong effect

Holistic Simulation Models Boost Manufacturing Energy Efficiency by Identifying Bottlenecks

Comprehensive computational models that integrate machine-level analysis with process chain and built environment considerations can significantly improve energy efficiency in manufacturing.

Journal of Manufacturing Systems · 2019

Key Findings

01Computational modelling is essential for energy analysis and decision-making in manufacturing.
02Holistic, multi-level models are required for accurate understanding of energy flows.
03Industry 4.0 technologies and visualization tools can enhance manufacturing process understanding and efficiency.

Application

Design takeaway

Integrate comprehensive, multi-level simulation models into the design and operational planning of manufacturing systems to proactively identify and address energy inefficiencies.

How to apply

Utilize discrete event simulation software to model a manufacturing line, incorporating energy consumption data for each machine and process step to identify potential energy savings.

Project actions

01When designing a manufacturing process, consider using simulation software to predict energy usage.
02Explore how different technologies like digital twins could be used to visualize energy flows in your design project.

Method & Evidence

AimTo investigate and develop methodologies for computational modelling to improve energy efficiency in manufacturing processes and systems.

MethodLiterature Review and Framework Development

ProcedureThe study reviewed existing methodologies and frameworks for analyzing energy consumption at the machine process level. It explored multi-level holistic analysis, incorporating individual machines, the manufacturing process chain, and the built environment, utilizing both discrete event and continuous simulation. The research also considered the impact of Industry 4.0 technologies and visualization techniques like digital twins, VR, and AR.

ContextManufacturing Engineering and Industrial Processes

Variables

IVMethodologies and frameworks for computational modelling (e.g., discrete event simulation, holistic analysis).

DVEnergy efficiency in manufacturing processes and systems.

CVManufacturing process parameters, machine specifications, built environment characteristics.

Strengths & Limitations

Strengths

+Provides a comprehensive overview of modelling approaches for energy efficiency.
+Highlights the importance of holistic analysis and emerging technologies.

Limitations

Developing accurate simulation models requires significant data and expertise, which may be difficult to obtain for a design project.

Reliability & validity

The reliability and validity of the findings depend heavily on the quality and comprehensiveness of the reviewed literature and the robustness of the proposed frameworks. The practical application of these models in real-world scenarios would require empirical validation.

Think critically

How can the complexity of real-world manufacturing environments be accurately represented in computational models, and what are the trade-offs between model fidelity and computational cost?

Design Principles

"Holistic system simulation is key to optimizing energy efficiency in complex manufacturing environments."

By simulating material, energy, and resource flows across an entire facility, designers and engineers can identify inefficiencies and optimize operations. This approach allows for proactive decision-making to reduce energy consumption without compromising productivity, a critical factor in today's resource-conscious industrial landscape.

What This Means for Your Design

Using computer models to map out how energy is used in a factory helps find ways to save energy without slowing down production.

How to use in your project

1.Reference this research when discussing the importance of modelling and simulation for energy efficiency in your design project's analysis or evaluation sections.

Add to My Project

Quick Cite

Paragraph starter

Computational modelling, particularly holistic, multi-level simulations that integrate machine, process chain, and built environment factors, offers a powerful approach to optimizing energy efficiency in manufacturing. This methodology allows for the detailed analysis of material, energy, and resource flows, enabling the identification of bottlenecks and inefficient operations. The integration of Industry 4.0 technologies and visualization tools further enhances the ability to understand and improve these complex systems, providing a robust framework for design decisions aimed at reducing energy consumption while maintaining productivity.

Source

Journal of Manufacturing Systems

The development of modelling tools to improve energy efficiency in manufacturing processes and systems

journal · 2019

View source

Questions About This Research

What does the research say about holistic simulation models boost manufacturing energy efficiency by identifying bottlenecks?: Integrate comprehensive, multi-level simulation models into the design and operational planning of manufacturing systems to proactively identify and address energy inefficiencies. Evidence: Journal of Manufacturing Systems (2019).
Why does "Holistic Simulation Models Boost Manufacturing Energy Efficiency by Identifying Bottlenecks" matter for design?: By simulating material, energy, and resource flows across an entire facility, designers and engineers can identify inefficiencies and optimize operations. This approach allows for proactive decision-making to reduce energy consumption without compromising productivity, a critical factor in today's resource-conscious industrial landscape.
How can designers apply this research?: Integrate comprehensive, multi-level simulation models into the design and operational planning of manufacturing systems to proactively identify and address energy inefficiencies.
What were the main findings?: Computational modelling is essential for energy analysis and decision-making in manufacturing.. Holistic, multi-level models are required for accurate understanding of energy flows.. Industry 4.0 technologies and visualization tools can enhance manufacturing process understanding and efficiency.
What research method was used?: Literature Review and Framework Development.
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2019 journal from Journal of Manufacturing Systems.
What should I do differently in my next project?: Utilize discrete event simulation software to model a manufacturing line, incorporating energy consumption data for each machine and process step to identify potential energy savings.
What are the limitations?: Achieving a complete, high-accuracy computational model with all relevant parameters presents significant challenges.