دانلود مقاله تحلیل انتقادی تاثیر آنالیز کلان داده ها بر عملیات زنجیره تامین
چکیده
1. مقدمه
2. مرور مطالعات پیشین: مفهوم سازی BDA و SCO
3. روش شناسی تحقیق
4. یافته های تجربی
5. چارچوب پیشنهادی
6. بحث و مفاهیم
7. نتیجه گیری
منابع
Abstract
1. Introduction
2. Literature review: conceptualising BDA and SCO
3. Research methodology
4. Empirical findings
5. Proposed framework
6. Discussion and implications
7. Conclusions
Disclosure statement
Notes on contributors
References
چکیده
بدون شک به دلیل فشارهای رقابتی فزاینده و گام برداشتن تقاضاهای مختلف، نوسانات و اختلالات به استاندارد بازارهای جهانی امروزی تبدیل شده است. گسترش کووید-19 نمونه بارز آن است. به دلیل افزایش عدم اطمینان در طرف عرضه و تقاضا، رقابت بین شرکای زنجیره تامین و نیاز به شناسایی راه‌هایی برای ارائه محصولات شخصی‌شده، از مدیران زنجیره تامین خواسته می‌شود تا در استراتژی‌های رقابتی خود برای استفاده از تجزیه و تحلیل داده‌های بزرگ (BDA) تجدید نظر کنند. و خدمات با توجه به اینکه بسیاری از مدیران زنجیره تامین نیاز به "بهبود با داده ها" را تشخیص داده اند، کسب و کارهای زنجیره تامین باید خود را به روش ها/تکنیک های پیچیده BDA مجهز کنند تا بینش های ارزشمندی از کلان داده ایجاد کنند، بنابراین، فرآیند تصمیم گیری را افزایش داده و کارایی تامین را بهینه کنند. عملیات زنجیره ای (SCO). این مقاله بلوک‌های ساختاری یک چارچوب نظری را برای درک تأثیر BDA بر SCO پیشنهاد می‌کند. این چارچوب بر اساس یک مرور ادبیات سیستماتیک (SLR) بر روی BDA و SCO است که بر اساس نظریه Task-Technology-Fit و نظریه نهادی پشتیبانی می شود. این مقاله با ایجاد بستری برای کار آینده در مورد بررسی عوامل محرک و مهار تأثیر BDA بر SCO به ادبیات کمک می کند.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Undoubtedly, due to the increasingly competitive pressures and the stride of varying demands, volatility and disturbance have become the standard in today’s global markets. The spread of Covid-19 is a prime example of that. Supply chain managers are urged to rethink their competitive strategies to make use of Big Data Analytics (BDA), due to the increasing uncertainty in both demand and supply side, the competition among the supply chain partners and the need to identify ways to offer personalised products and services. With many supply chain executives recognising the need of ‘improving with data’, supply chain businesses need to equip themselves with sophisticated BDA methods/techniques to create valuable insights from big data, thus, enhancing the decision-making process and optimising the efficiency of Supply Chain Operations (SCO). This paper proposes the building blocks of a theoretical framework for understanding the impact of BDA on SCO. The framework is based on a Systematic Literature Review (SLR) on BDA and SCO, underpinned by Task-Technology-Fit theory and Institutional Theory. The paper contributes to the literature by building a platform for future work on investigating factors driving and inhibiting BDA impact on SCO.
Introduction
Over the past decade, the manufacturing sector has been in the midst of a fourth wave of technological advancement (Rüßmann et al. 2015). During this, a plethora of manufacturing and supply chain businesses have transformed their operations into intelligent/smart manufacturing. This is by adopting a variety of innovative technologies such as autonomous robots, simulations, horizontal and vertical systems integration, internet of things [IoT], cybersecurity, cloud services, additive manufacturing, and most importantly, big data analytics (Wamba et al. 2020). Several noticeable researchers and practitioners have recognised the significance and applicability of Industry 4.0 (I_4.0) for operations, logistics and production management at large (Sivarajah et al. 2017; Rahman et al. 2022). However, relatively little is known about the impact of BDA on SCO – particularly focussing on the five dimensions of SCO: demand planning, production and manufacturing, logistics, procurement, and inventory. Among the extant research studies published, a few shed light on the link between IoT and SCO (e.g. Ben-Daya, Hassini, and Bahroun 2019) and the impact of additive manufacturing on SCO, processes and performances (e.g. Li, Tang, et al. 2017).
Conclusions
Arguably, SCO and BDA can enable the dynamic capabilities of firms, allowing decision makers to enhance the corporate or company abilities or to better sense emerging opportunities and threats. This paper presents the past trends and current state of BDA research published specifically in the context of SCOs and its respective dimensions. We proposed a conceptual framework based on two research questions: Investigating the benefits of implementing BDA methods/techniques to achieve better decision-making for SCO? and Investigating the benefits of implementing BDA methods/techniques in achieving optimisation of SCOs? The continuing interest and the use of BDA specifies that in future research studies academics, researchers and practitioners may focus on the factors driving and inhibiting BDA to further propose robust solutions to the service related problems. The intention in conducting this detailed investigation was to provide a useful and yet usable source of information for future researchers. The discussion also highlighted several research limitations and future directions for BDA applications within the SCO research area to catalyse the research development of the topic.