Digital Twin-Driven SimLean-TRIZ Framework in Cold Room Door Production

The study aims to increase productivity in the cold room door manufacturing industry by addressing non-value-adding operations, identifying bottlenecks, and reducing processing time through digital twin (DT)-based simulation. The goal is to eliminate the need for supply chain outsourcing and increase overall efficiency. The research involves developing a DT of the existing production process for five distinct categories of cold room doors: flush door, single door, double door, face-mounted door, and sliding door. Simulation was used to uncover problems at multiple stations, encompassing curing, welding, and packing. Lean principles were used to identify the causes of inefficiency, and the process was improved using TRIZ principles. These changes produced a 42.90% improvement in productivity, a 20% dependence reduction on outsourcing and an increase of 10.5% added inventory to the shortage demand level. The approach presented is provided for a particular manufacturer of cold room doors, but the methods and techniques used are generally applicable to other manufacturing companies to support systematic innovation. Combining DT simulation, lean techniques and TRIZ principles, this study presents a strong approach to addressing the productivity challenges in manufacturing. The incorporation of these methods has brought considerable operational efficiency and has minimised dependency on external outsourcing.

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Publication Details

Journal
Modelling—International Open Access Journal of Modelling in Engineering Science
Published
2025-07-14
DOI
https://doi.org/10.3390/modelling6030067
Primary Topic
Digital Transformation in Industry
Type
article
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article

Digital Twin-Driven SimLean-TRIZ Framework in Cold Room Door Production

Olivia McDermott, A. Sumesh, M. Thenarasu, M.S. Narassima et al.
Modelling—International Open Access Journal of Modelling in Engineering Science
Digital Transformation in Industry
article

Digital Twin-Driven SimLean-TRIZ Framework in Cold Room Door Production

Olivia McDermott, A. Sumesh, M. Thenarasu, M.S. Narassima, Arjun Panicker
article en

Abstract

The study aims to increase productivity in the cold room door manufacturing industry by addressing non-value-adding operations, identifying bottlenecks, and reducing processing time through digital twin (DT)-based simulation. The goal is to eliminate the need for supply chain outsourcing and increase overall efficiency. The research involves developing a DT of the existing production process for five distinct categories of cold room doors: flush door, single door, double door, face-mounted door, and sliding door. Simulation was used to uncover problems at multiple stations, encompassing curing, welding, and packing. Lean principles were used to identify the causes of inefficiency, and the process was improved using TRIZ principles. These changes produced a 42.90% improvement in productivity, a 20% dependence reduction on outsourcing and an increase of 10.5% added inventory to the shortage demand level. The approach presented is provided for a particular manufacturer of cold room doors, but the methods and techniques used are generally applicable to other manufacturing companies to support systematic innovation. Combining DT simulation, lean techniques and TRIZ principles, this study presents a strong approach to addressing the productivity challenges in manufacturing. The incorporation of these methods has brought considerable operational efficiency and has minimised dependency on external outsourcing.

Modelling—International Open Access Journal of Modelling in Engineering ScienceVol. 6(3)
Ollscoil na Gaillimhe – University of Galway (IE), Great Lakes Institute of Management (IN), Amrita Vishwa Vidyapeetham (IN)
Openalex Percentile: Top 76%
Digital Transformation in Industry
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Digital Twin-Driven SimLean-TRIZ Framework in Cold Room Door Production — Olivia McDermott, A. Sumesh, et al. · Modelling—International Open Access Journal of Modelling in Engineering Science (2025) | TGRS Research Map | TGRS