تحلیل های کلان داده به عنوان یک رویکرد عالی عملیاتی
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تحلیل های کلان داده به عنوان یک رویکرد عالی عملیاتی

عنوان فارسی مقاله: تحلیل های کلان داده به عنوان یک رویکرد برتر عملیاتی به منظور ارتقا عملکرد زنجیره تامین پایدار
عنوان انگلیسی مقاله: Big data analytics as an operational excellence approach to enhance sustainable supply chain performance
مجله/کنفرانس: منابع، حفاظت و بازیافت – Resources, Conservation and Recycling
رشته های تحصیلی مرتبط: مهندسی صنایع، مدیریت
گرایش های تحصیلی مرتبط: لجستیک و زنجیره تامین، کلان داده، مدیریت صنعتی
کلمات کلیدی فارسی: تحلیل های کلان داده، برتر عملیاتی، دیدگاه ظرفیت پویا، پایداری زنجیره تامین، عملکرد یادگیری
کلمات کلیدی انگلیسی: Big data analytics, Operational excellence, Dynamic capability view, Supply chain sustainability, Learning performance
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.resconrec.2019.104559
دانشگاه: University of Johannesburg, South Africa
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 7.082 در سال 2019
شاخص H_index: 103 در سال 2020
شاخص SJR: 1.541 در سال 2019
شناسه ISSN: 0921-3449
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 10
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: دارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E14164
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Literature review


3- Conceptual framework and hypothesis development


4- Research methods


5- Data analysis


6- Discussion


7- Conclusion


References

نمونه متن انگلیسی مقاله

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).

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