Investigating the impact of smart manufacturing on firms' operational and financial performance

Journal of Manufacturing Technology Management(2024)

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摘要
PurposeSmart manufacturing (SM) lies at the core of Industry 4.0. Uniform adoption of SM across business partners is crucial to exploit its value creation potential. However, firms' willingness to invest in SM is limited by insufficient or inconclusive evidence on its performance-related benefits. To close this gap, this paper develops and tests a model linking SM adoption to firms' financial performance. Improvements along the four dimensions of operational performance (i.e. cost quality, delivery and flexibility) mediate this relation.Design/methodology/approachThis study follows an empirical research approach. In particular, survey data from 234 automotive component suppliers are analyzed via covariance-based structural equation modeling to explore the link between SM adoption and operational performance. Survey data are then matched with secondary data from balance sheets of 81 firms to investigate the impact of SM on financial performance via partial least square structural equation modeling.FindingsFindings highlight that adoption of SM results in improvements in cost, quality, delivery performance, thus suggesting that SM is a mean to overcome performance trade-offs. Improvements in operational performance enabled by SM do not give rise to superior financial performance, thus implying that SM might support firms in maintaining the competitive position in the market, but could be insufficient to generate higher margin.Originality/valueResults have implications for SM research and for manufacturing executives engaged in the adoption of SM, as they provide a detailed analysis of the impact of SM on operational performance and clarify the effect that SM adoption has on financial performance.
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关键词
Performance,Industry 4.0,Data analysis method - sophisticated and advanced statistical analysis,Data analysis method - simple statistical analysis (e.g. structural equation modeling such as partial least square),Type of industry - motor vehicle industry,Data collection method - secondary data
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