Supply chain integration and operational performance: The contingency effects of production systems

Highlights

• The performance effect of supply chain integration varies across production system.

• Customer integration improves the one-of-a-kind production performance.

• Supplier integration improves the batch production performance.

• Supplier integration only contributes to the cost performance of mass production.

Abstract

The boundary conditions of supply chain integration (SCI) have been widely studied in order to find out when SCI is applicable and effective. However, prior studies have mainly focused on external contextual factors, such as supply complexity, environmental uncertainty and country-level infrastructure. This study contributes to the SCI literature by examining the contingency effects of internal production systems on the relationship between supplier integration, customer integration and operational performance. Based on organizational information processing theory, we provide evidence to show that the impact of supplier and customer integration on operational performance varies across production systems, such as one-of-a-kind production, batch production and mass production systems. The empirical results also reveal how supplier and customer integration can be matched with different configurations of production systems in order to achieve the desired quality, flexibility, delivery or cost performance.

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.