Empirical analysis of productivity enhancement strategies in the North American automotive industry

Abolhassani, Amir; Harner, E. James; Jaridi, Majid

doi:10.1016/j.ijpe.2018.11.014

Cited by 19 publications

(17 citation statements)

References 31 publications

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“…Tvrdon and Fedorko [7] presented a framework for using dynamic simulation in production systems design and successfully applied to a real-life project of innovation of a production system in a pressing shop. Abolhassani et al [8] developed the reliable and hybrid model, hours-per-vehicle, the most popular efficiency measurement system in the automotive industry. Jakubovskis [9] created a model aimed at examining the relationship between the overall capacity utilization rates and the rate of flexible technologies obtained under uncertain demand conditions by using an optimization algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

A Simulation Approach for Transition to JIT Production System

Kilic¹,

Erkayman²

2021

Int. j. simul. model.

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Consumption is increasing day by day due to rapid developments in technology and increasing world population. Companies should review their production processes in order to keep up with the consumption rate and increase their competitiveness as well as profit margins. This research shows a case study of simulation application to improve solar panel manufacturing process. The aim is to achieve goals such as increasing system efficiency, reducing inventory and shortening delivery time. Efficient manufacturing process design may reduce work-in-process (WIP), waste, rework and faults, which may reduce the total number of products and yields an increase in the system cost. Inefficiency points were determined in the process based on the obtained observations of the manufacturing system. Various scenarios have been proposed to design such an efficient production system. According to the proposed scenarios, an increase in the utilization rates of machinery, personnel and the total number of final products has been achieved.

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Section: Literature Surveymentioning

confidence: 99%

A Simulation Approach for Transition to JIT Production System

Kilic¹,

Erkayman²

2021

Int. j. simul. model.

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“…Through this commitment to developing new abilities, human capabilities provide a foundation for performance (cf. Ployhart et al 2011;Abolhassani et al 2019). For example, Collins and Smith (2006) suggest that commitment created through human resources (HR) practices can improve firm performance by increasing employees' willingness to work together to create and exchange knowledge.…”

Section: Operational Capabilities As Antecedents Of Total Productivitymentioning

confidence: 99%

“…High commitment to firm operations is proven to result in higher job satisfaction and performance (Ployhart et al 2011;Latorre et al 2016;Abolhassani et al 2019). Labor productivity generally reflects the quality of labor efforts, so it likely is affected by human capabilities.…”

Section: Operational Capabilities As Antecedents Of Labor Productivitymentioning

confidence: 99%

Preceding operational capabilities as antecedents for productivity and innovation performance

et al. 2019

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This study focuses on the connections among operational capabilities, productivity, and innovation performance. Specifically, the study investigates whether preceding operational capabilities, particularly human, time management, and financing capabilities, affect total productivity, labor productivity, and innovation performance. Using data from 203 Finnish small- and medium-sized enterprises, this study shows that human and time management capabilities affect total productivity, whereas only human capabilities affect labor productivity. Both human and time management capabilities affect innovation performance. Hence, this study makes two main contributions to existing research on operational capabilities. As a first contribution, the study introduces three preceding operational capabilities—human, time management, and financing capabilities—as crucial elements to realize operational resources and practices, on one hand, and to establish operational capabilities, on the other hand. As a second contribution, this research adds to the literature a comprehensive investigation of the links of preceding operational capabilities with productivity (i.e., total productivity and labor productivity) and innovation performance.

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“…Consequently, multiple technological innovations and innovative PI systems have emerged (e.g. Schmenner, 2015; Park and Li, 2019; Abolhassani et al , 2019), and they have helped to increase productivity levels in practice and advanced the development of the operations management discipline (Schmenner and Swink, 1998).…”

Section: Introductionmentioning

confidence: 99%

“…We now know a great deal about how PI within manufacturing is affected by a complex combination of contextual variables (Spring et al , 2017), resource variables (Abolhassani et al , 2019; Park and Li, 2019), management practices and concepts such as lean (Gunasekaran et al , 2000; Abolhassani et al , 2019), and leadership and communication (Sting and Loch, 2016). However, we know less about the combination of different types of management control systems (MCSs) that are involved in and affect PI activities (Jin et al , 2016; Güldenpfennig and Hald, 2019).…”

Section: Introductionmentioning

confidence: 99%

Productivity improvement and multiple management controls: evidence from a manufacturing firm

Güldenpfennig

Hald

Hansen

2021

IJOPM

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PurposeThe present paper explores the multiple management control systems (MCSs) involved in productivity improvement (PI) in manufacturing and how they interrelate. Research has largely neglected the multiplicity and interrelationships of these MCSs.Design/methodology/approachDrawing on an abductive case study approach, the authors collected empirical data from a global automotive supplier that produces complex systems for passenger cars. Recent PI activities are analysed to identify and explain the interrelationships among the multiple MCSs affecting these activities.FindingsThe study shows how a broad range of MCSs are involved in PI. The study identifies and explores both complementary and conflicting relationships among the MCSs and demonstrates how managers rely on a set of mechanisms to alleviate tensions and strengthen complementarities among these MCSs.Research limitations/implicationsAs this paper is based on a single case study, future research can contribute further generalisations (analytical and statistical) with respect to the MCSs involved in PI, how they are interrelated and which mechanisms managers use to manage their interrelationships.Practical implicationsManagers seeking to control and improve productivity should consider the complete control package and its interrelationships instead of focussing on each MCS separately.Originality/valueThe present paper contributes to the knowledge of the multiplicity and interrelationships of MCSs involved in PI and the type of managerial work required to manage their interrelationships.

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Empirical analysis of productivity enhancement strategies in the North American automotive industry

Cited by 19 publications

References 31 publications

A Simulation Approach for Transition to JIT Production System

A Simulation Approach for Transition to JIT Production System

Preceding operational capabilities as antecedents for productivity and innovation performance

Productivity improvement and multiple management controls: evidence from a manufacturing firm

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