Supply chain integration and operational performance: The contingency effects of production systems
Introduction
Supply chain integration (SCI) indicates strategic collaboration, information-sharing, joint decision-making and system-coupling between the manufacturer and its supply chain partners, especially in the production phase (Alfalla-Luque et al., 2013, Demeter et al., 2016, Flynn et al., 2010, Kauppi et al., 2016). In prior studies, scholars have confirmed the positive effects of SCI on operational performance (OP). Some studies have examined the contextual conditions under which SCI is effective (Sousa and Voss, 2008). For example, Wong et al. (2011) demonstrate that environmental uncertainty moderates the relationship between SCI and OP. Similarly, Gimenez et al. (2012) indicate that SCI improves performance, but only when the buyer-supplier relationship is characterized by high supply complexity. Wiengarten et al. (2014) extend the literature by considering the role of a country's logistics capability in external SCI.
However, most extant studies on the contextual conditions of SCI focus on the effects of external environmental factors. Few studies have considered internal factors. In a conceptual paper, Ellram et al. (2007) suggest taking product and process characteristics into consideration and establishing an appropriate match between product design, process design and supply chain structure. Tsinopoulos and Mena (2015) assert that manufacturers’ internal process structure and product newness require different supply chain configurations at different stages of the product life cycle. Using qualitative data from British manufacturers, Tsinopoulos and Mena (2015) conclude that firms tend to implement supplier integration (SI) and customer integration (CI), especially in the case of high customization and low-volume production, whereas external integration is not particularly necessary for production with high standardization and high volume. In short, it is very important to fit external integration decisions with internal organizational characteristics in order to yield better OP. Nevertheless, limited empirical evidence is available in the extant literature to support the in-depth analysis of how external SCI matches with internal process structures in order to determine individual dimensions of OP.
To address this research gap, this paper aims to empirically explore the boundary conditions (Busse et al., 2017) where external SCI (i.e., SI and CI) is effective in terms of quality, flexibility, delivery or cost improvement by analyzing the contingency effects of internal production systems. One-of-a-kind production (OKP), batch production (BP) and mass production (MP) systems are regarded as three main kinds of production system in modern manufacturing practices. Consequently, this study attempts to answer the research question: How can manufacturing firms utilize external SCI to achieve the desired OP, given their production system configuration?
In response to the research question, this paper applies the lens of organizational information processing theory (OIPT). OIPT insists that an organization should align its information-processing capability with information-processing requirements under different conditions (Galbraith, 1973). In prior studies, scholars have investigated SCI as an information-processing system to cope with task interdependence and uncertainty based on OIPT (Hult et al., 2004, Srinivasan and Swink, 2015, Wong et al., 2011). SI and CI can improve an organization's information-processing capability through inter-organizational flows and information-sharing mechanisms (Flynn et al., 2016), as well as by establishing lateral and collaborative relationships with supply chain partners (Srinivasan and Swink, 2015). Production systems, including OKP, BP and MP, differ from each other in terms of the number of variants, lot sizes, automation, specificity of the equipment, and control of production (Woodward et al., 1965; Tu and Dean, 2011). We speculate that the operation of different production systems may have distinct information-processing requirements, which should be matched with different configurations of SI and CI in order to achieve superior OP.
This paper contributes to the boundary condition research on SCI. Based on a survey of 791 firms, our findings provide empirical evidence concerning the importance of the fit between SCI and production systems. This study poses both theoretical and managerial implications.
Section snippets
The relationship between SCI and operational performance
There is a growing body of research on the theory and practices of SCI. Integration is elaborated as collaboration (which suggests joint goals and collaborative behaviors) and interaction (which indicates formal communication and information exchange). Correspondingly, external SCI is defined as the degree to which a manufacturer strategically collaborates with its suppliers and customers, as well as collaboratively manages cross-firm business processes (Flynn et al., 2010, Wong et al., 2011).
Sample
We used data collected from the sixth round of the International Manufacturing Strategy Survey (IMSS) in the period 2013–2014 to test the proposed hypotheses. Developed by an international team of operations strategy researchers, the IMSS has been collecting data at the plant level in over 20 countries through common research methodology since 1992. The IMSS research group has developed a standard English questionnaire, which is then translated into the local language by research coordinators
Analyses and results
Our research found both direct effects of SCI on OP and contingency effects of production systems. A structural equation model using AMOS 20.0 was first established to test the SCI-OP relationships (H1 and H2). We then performed a multi-group analysis across OKP-dominated, BP-dominated and MP-dominated groups to examine the contingency effects of production systems (H3 and H4), according to the approach used by Byrne (2013), Nyaga et al. (2010) and Wiengarten et al. (2014). Table 6 summarizes
Discussion
This study explores how external SCI (i.e., SI and CI) influences quality, flexibility, delivery and cost performance, given the different production system configurations. Although prior studies have examined the value of SI and CI in relation to OP, the results of this study provide deeper insights and demonstrate that the effectiveness of external SCI is contingent on the internal production system configuration. Specifically, this study suggests that, for manufacturers with an OKP-dominated
Conclusions
In summary, this study shows how the fit between external SCI and internal production systems determines individual dimensions of OP. The findings contribute to the boundary condition research on SCI by focusing on the contingency effects of production systems. In addition, this study provides a nuanced understanding of the fit between external SCI and OKP, BP and MP systems from the perspective of OIPT, which in turn extends the application of OIPT to supply chain management research. This
Acknowledgements
The authors would like to express their gratitude to the associate editor and two reviewers whose suggestions have led to significant improvements of this paper. This work was supported by the National Natural Science Foundation of China under Grant number 71472166.
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