Big data and big data analytics have contributed to the collection of large amounts of data. This has led to the transformation to data-driven supply chain (DDSC) structures. The supply chain coordination capability (SCCC) has also been strengthened in DDSC. In this study, the effect of DDSC on SCCC and the moderator effect of supplier coordination (SCO) have been investigated. The research was carried out in the manufacturing firms. Three hundred eighty-three pieces of data were obtained between August and December 2021 by the simple random sampling method. Two hypotheses were developed. Simple regression analysis was used to test the first hypothesis, and moderator effect analysis was performed with the SPSS process to test the second hypothesis. It has been determined that the DDSC has a significant effect on the SCCC. In addition, SCO has a moderating effect, and when there is a high SCO, the DDSC has a greater effect on the SCCC.
TopIntroduction
In today's world, where the transition from Industry 4.0 to Industry 5.0 is experienced, the level of commitment to information technologies and the level of benefiting from information technologies is increasing. “Big data (BD)” and smart systems that came with Industry 4.0 have taken their place in all industrial activities and have enabled the transition to big data-based applications (Shin et al., 2018). BD has enabled the collection and storage of large amounts of data. “Big data analytics (BDA)”, on the other hand, enabled BD analyzes (Tsai et al., 2015). The competitive advantage gained by performing real-time data analysis with BDA has played a triggering role in the transformation of organizations into data-based structures (Sundarakani et al., 2021). This change has also taken place in supply chains. Supply chain integration is also increasing in supply chains where the use of information technologies is increasing (Khanuja and Jain, 2021). Thus, the gap between companies and organizations in the supply chain is closed, contributing to the formation of sustainable supply chain structures (Bag et al., 2020). BD and BDA usage approaches in the supply chain have developed the data-driven supply chain (DDSC) structure (Yu et al., 2018).
DDSC provides various advantages at different nodes of the supply chain (Biswas and Sen, 2017). Li and Liu (2019) demonstrated the benefits of BD and BDA in the design and development of the supply chain, purchasing, manufacturing, logistics, customer service, marketing, and sales stages, and developed a DDSC management model based on these benefits. Thekkoote (2021) states that DDSC has developed supply chain applications especially in terms of information sharing, integration, and coordination, and has increased customer satisfaction to the highest level. Supply chain coordination (SCC) has been discussed in the literature from four different perspectives (Kanda and Deshmukh, 2008). The first perspective is fully collaborative working within the supply chain (Larsen, 2003). The second perspective is partial collaborative work within the supply chain (Simatupang and Sridharan, 2002). The third perspective is coordination based on a win-win policy (McClellan, 2003). The fourth perspective is coordination against the difficulties encountered in the supply chain (Xu and Beamon, 2006).
Information technologies play an active role in the rise of SCC (Bi et al., 2011). In addition, the DDSC structure, which is mainly used in supply chain management of information technologies, plays an active role in raising SCC. Supply chain coordination capability (SCCC) is needed to ensure coordination within the supply chain, especially in the Resource-based view (RBV) approach. This capability is used to establish an effective coordination structure within the supply chain. In addition, the existing supplier coordination (SCO) between suppliers also indicate the existence of SCCC. At this point, it is expected that there is a significantly relationship among DDSC, SCCC and SCO. This expectation creates two basic research questions. The research questions that inspired this study are as follows: