تحلیل های کلان داده به عنوان یک رویکرد عالی عملیاتی
Abstract
1- Introduction
2- Literature review
3- Conceptual framework and hypothesis development
4- Research methods
5- Data analysis
6- Discussion
7- Conclusion
References
Abstract
Operations management is a core organizational function involved in the management of activities to produce and deliver products and services. Appropriate operations decisions rely on assessing and using information; a task made more challenging in the Big Data era. Effective management of data (big data analytics; BDA), along with staff capabilities (the talent capability in the use of big data) support firms to leverage big data analytics and organizational learning in support of sustainable supply chain management outcomes. The current study uses dynamic capability theory as a foundation for evaluating the role of BDA capability as an operational excellence approach in improving sustainable supply chain performance. We surveyed mining executives in the emerging economy of South Africa and received 520 valid responses (47% response rate). We used Partial Least Squares Structural Equation Modelling (PLS-SEM) to analyze the data. The findings show that big data analytics management capabilities have a strong and significant effect on innovative green product development and sustainable supply chain outcomes. Big data analytics talent capabilities have a weaker but still significant effect on employee development and sustainable supply chain outcomes. Innovation and learning performance affect sustainable supply chain performance, and supply chain innovativeness has an important moderating role. A contribution of the study is identifying two pathways that managers can use to improve sustainable supply chain outcomes in the mining industry, based on big data analytics capabilities.
Introduction
Operations management as a discipline emphasizes planning and configurations of resources to achieve organizational outcomes, especially in engineering and management sciences. The synchronization between operational activities (internal) and supply chain management (external) activities is indispensable to ensure enduring supply chain performance. Supply chain managers carefully benchmark their operational performance (Lun, 2011; Zhou and Zhou, 2015; Hu et al., 2019; Mangla et al., 2019; Taelman et al., 2019). However, contemporary supply chains are exposed to dynamic business environments with high levels of uncertainties (Ahmadi et al., 2017; Bag, 2017; Bag et al., 2018; Bag et al., 2019). In response to uncertainty, rather than develop resources, firms focus on the development of dynamic capabilities to mitigate risks (e.g., a loss of reputation) and build competitive advantages. The concern with risks is particularly relevant now as the environmental impact from firms’ activities can lead to reputational and financial risks for failing to meet sustainability objectives (Wood et al., 2018).
Big data analytics (BDA) tools may support significant business benefits and drive organizational improvements (Gunasekaran et al., 2017). In general, big data is characterized by 5 Vs (viz., volume, veracity, variety, velocity, and value) (Tao et al., 2018). BDA elicits two major viewpoints to achieve the operational excellence of the organizations. First, the collection of big data (BD) from the firm and external environment. This type of data suggests high volume and velocity of processing data that can provide many improvements and benefits when compared with the existing form of traditional data processing systems (Frank et al., 2019). Second, the use of BD in business analytics (BA) to inform decisions and manage operations. BA consists of capabilities and the potential to assess the strategic move of organizations to attain successful planning of businesses of the organizations. The strategic improvements available through BA (such as forecasting, statistical, and operational analysis via optimization techniques) significantly contribute to the enhanced operational efficiency (Mathivathanan et al., 2018; Chams and García-Blandón, 2019).