To introduce a store brand or not: Roles of market information in supply chains
معرفی یک نام تجاری فروشگاهی یا نه: نقش اطلاعات بازار در زنجیره های تأمین-2021
Retailers in multiple market segments have opposing business practices regarding whether to introduce a store brand. Assuming that manufacturers and retailers have symmetric knowledge on market information, prior literature has shown that retailers have incentive to introduce a store brand in cases in which the store brand intensively competes with the national brand. Information asymmetry, however, is prevalent in supply chains. In the era of big data, the latest advances in technology intensify information asymmetry by allowing retailers to access market information with improved accuracy. To address such information asymmetry, some retailers share information with manufacturers, while others do not. The gap between prior studies and current industrial practices motivates us to explore the roles of market information in supply chains regarding store brand
Keywords: Store brand | National brand | Market information | Information accuracy | Information sharing
A framework based on BWM for big data analytics (BDA) barriers in manufacturing supply chains
چارچوبی مبتنی بر BWM برای موانع تجزیه و تحلیل داده های بزرگ (BDA) در تولید زنجیره های تأمین-2021
Due to its potential utility, Big Data (BD) recently attracted researchers and practitioners in decision- making. Big Data analytics (BDA) becomes more common among manufacturing companies because it lets them gain insight and make decisions based on BD. Given the importance of both BD and BDA, this study aims to identify and analyse essential BDA adoption barriers in supply chains. This study explores the current knowledge base using a BWM (Best Worst Method) to discuss these barriers. Data were obtained from five Indian manufacturing companies. Research findings show that data-related barriers are most significant. The findings will help managers understand the exact nature of the challenges and possible advantages of the BDA and implement BDA policies for the growth and output of supply chain operations.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 3rd International e-Con- ference on Frontiers in Mechanical Engineering and nano Technology.
Keywords: Big data analytics | Barriers | Manufacturing supply chains | Best worst method (BWM)
Circular supply chain management with large scale group decision making in the big data era: The macro-micro model
مدیریت زنجیره تأمین دایره ای با تصمیم گیری گروهی در مقیاس بزرگ در عصر داده های بزرگ: مدل خرد خرد-2021
Today, achieving the circular economy is a common goal for many enterprises and governments all around the world. In the big data era, decision making is well-supported and enhanced by a massive amount of data. In particular, large scale group decision making (LSGDM), which refers to the case in which a lot of decision makers join the decision making process, has emerged. Social network analyses are known to be relevant to LSGDM. In this paper, we examine the literature on LSGDM and highlight the current methodological advances in the area. We review the works focusing on applications of LSGDM. We study how big data can be used in circular supply chains. Based on the reviewed studies, we further construct the three-stage LSGDM CSCM micro framework as well as the five-step LSGDM CSCM macro framework (with a feedback loop) and form the Macro-Micro Model. We discuss how the Macro-Micro Model can help to support circular supply chain management (CSCM). We propose future research directions and areas. This paper contributes by being the first study uncovering systematically how LSGDM can be applied to support CSCM in the big data era using the Macro-Micro Model.
Keywords: Large scale group decision making (LSGDM) | Circular supply chains | Research agenda | Literature review | Frameworks | Macro-micro model
A fuzzy based hybrid decision framework to circularity in dairy supply chains through big data solutions
چارچوب تصمیم ترکیبی مبتنی بر فازی برای مدور بودن در زنجیره های تامین لبنیات از طریق راه حل های داده های بزرگ-2021
This study determines the potential barriers to achieving circularity in dairy supply chains; it proposes a framework which covers big data driven solutions to deal with the suggested barriers. The main contribution of the study is to propose a framework by making ideal matching and ranking of big data solutions to barriers to circularity in dairy supply chains. This framework further offers a specific roadmap as a practical contribution while investigating companies with restricted resources. In this study the main barriers are classified as ‘eco- nomic’, ‘environmental’, ‘social and legal’, ‘technological’, ‘supply chain management’ and ‘strategic’ with twenty-seven sub-barriers. Various big data solutions such as machine learning, optimization, data mining, cloud computing, artificial neural network, statistical techniques and social network analysis have been suggested. Big data solutions are matched with circularity focused barriers to show which solutions succeed in overcoming barriers. A hybrid decision framework based on the fuzzy ANP and the fuzzy VIKOR is developed to find the weights of the barriers and to rank the big data driven solutions. The results indicate that among the main barriers, ‘economic’ was of the highest importance, followed by ‘technological’, ‘environmental’, ‘strategic’, ‘supply chain management’ then ‘social and legal barrier’ in dairy supply chains. In order to overcome circularity focused barriers, ‘optimization’ is determined to be the most important big data solution. The other solutions to overcoming proposed challenges are ‘data mining’, ‘machine learning’, ‘statistical techniques’ and ‘artificial neural network’ respectively. The suggested big data solutions will be useful for policy makers and managers to deal with potential barriers in implementing circularity in the context of dairy supply chains.
Keywords: Dairy supply chain | Barriers | Circular economy | Big data solution | Fuzzy ANP - VIKOR | Group decision making system
A big data based architecture for collaborative networks: Supply chains mixed-network
یک معماری مبتنی بر داده های بزرگ برای شبکه های مشارکتی: شبکه های مخلوط شبکه های تأمین-2021
Nowadays, the world knows a high-speed development and evolution of technologies, vulnerable economic environments, market changes, and personalised consumer trends. The issue and challenge related to enterprises networks design are more and more critical. These networks are often designed for short terms since their strategies must be competitive and better adapted to the environment, social and economical changes. As a solution, to design a flexible and robust network, it is necessary to deal with the trade-off between conflicting qualitative and quantitative criteria such as cost, quality, delivery time, and competition, etc. To this end, using Big Data (BD) as emerging technology will enhance the real performances of these kinds of networks. Moreover, even if the literature is rich with BD models and frameworks developed for a single supply chain network (SCN), there is a real need to scale and extend these BD models to networked supply chains (NSCs). To do so, this paper proposes a BD architecture to drive a mixed-network of SCs that collaborate in serial and parallel fashions. The collaboration is set up by sharing their resources, capabilities, competencies, and information to imitate a unique organisation. The objective is to increase internal value to their shareholders (where value is seen as wealth) and deliver better external value to the end-customer (where value represents customer satisfaction). Within a mixed-network of SCs, both values are formally calculated considering both serial and parallel networks configurations. Besides, some performance factors of the proposed BD architecture such as security, flexibility, robustness and resilience are discussed.
Keywords: Big data architecture | Collaborative networks | Enterprises network | Supply chain network | Flexibility | Robustness
Big data driven supply chain design and applications for blockchain: An action research using case study approach
داده های بزرگ طراحی زنجیره تأمین و کاربردهای بلاکچین: یک اقدام پژوهی با استفاده از رویکرد مطالعه موردی-2021
Blockchain appears to still be nascent in its growth and a relatively untapped asset. This research investigates the need of blockchain in Industry 4.0 environment from Big Data perspective in supply chain management. The research method used in this study involves a combination of an Action Research method and Case Study research. More specifically, the action research method was applied in two industry case studies that implemented and tested the designed architecture in a global logistics environment. Case Study A examined the blockchain application in cross-border cargo movements whereas Case Study B investigated the application in a liquid chemical logistics company serving to petroleum industries. Our research analysis has identified that the Case A subject had disconnected systems and services for blockchain wherein the big data interactions had failed (failure case). Whereas in Case B, the company has achieved nearly 25% increase in revenue through its customer service after the blockchain implementation and thereby reduction in paperwork and carbon emissions (success case). This research contributes to the advancement of the body of knowledge to big data and blockchain by identifying key implementation guideline and issues for blockchain in supply chain management. Further, action-based research coupled with a case study approach has been used to evaluate the application aspects of the architecture’s scalability and functionality of bigdata and blockchain in supply chain management.
Keywords: Big data architecture | Action research | Case study research | Blockchain adoption | Supply chain management
Improving supply chain resilience through industry 4:0: A systematic literature review under the impressions of the COVID-19 pandemic
بهبود انعطاف پذیری زنجیره تأمین از طریق صنعت 4:0: بررسی ادبیات سیستماتیک تحت تأثیر همه گیری COVID-19-2021
The COVID-19 pandemic is one of the most severe supply chain disruptions in history and has challenged practitioners and scholars to improve the resilience of supply chains. Recent technological progress, especially industry 4.0, indicates promising possibilities to mitigate supply chain risks such as the COVID-19 pandemic. However, the literature lacks a comprehensive analysis of the link between industry 4.0 and supply chain resilience. To close this research gap, we present evidence from a systematic literature review, including 62 papers from high-quality journals. Based on a categorization of industry 4.0 enabler technologies and supply chain resilience antecedents, we introduce a holistic framework depicting the relationship between both areas while exploring the current state-of-the-art. To verify industry 4.0’s resilience opportunities in a severe supply chain disruption, we apply our framework to a use case, the COVID-19-affected automotive industry. Overall, our results reveal that big data analytics is particularly suitable for improving supply chain resilience, while other industry 4.0 enabler technologies, including additive manufacturing and cyber-physical systems, still lack proof of effectiveness. Moreover, we demonstrate that visibility and velocity are the resilience antecedents that benefit most from industry 4.0 implementation. We also establish that industry 4.0 holistically supports pre-disruption resilience measures, enabling more effective proactive risk management. Both research and practice can benefit from this study. While scholars may analyze resilience potentials of under-explored enabler technologies, practitioners can use our findings to guide industry 4.0 investment decisions.
Keywords: Industry 4.0 | Supply chain risk management | Supply chain resilience | Supply chain disruption | Digital supply chain | Literature review
Integrating big data analytics into supply chain finance: The roles of information processing and data-driven culture
ادغام تجزیه و تحلیل داده های بزرگ در امور مالی زنجیره تأمین: نقش پردازش اطلاعات و فرهنگ داده محور-2021
The role of big data in implementing supply chain finance (SCF) initiatives lacks empirical study. There is little guidance available for managers on developing an integrated SCF process in the era of big data. Using organizational information processing theory, this study develops and empirically tests a theoretical framework that investigates the effect of big data analytics capability (BDAC) on SCF Integration, and the moderating effect of data-driven culture. The hypothesized relationships were tested using structural equation modelling and moderated regression analysis, with primary survey data collected from a sample of 307 manufacturing firms in China. The results indicate that BDAC has a significant positive effect on internal SCF Integration, and internal SCF Integration fully mediates the relationships between BDAC and SCF Integration with customers and sup- pliers. Data-driven culture significantly moderates the effect of BDAC on internal SCF Integration. These empirical findings provide timely and useful guidance for managers on using big data analytics and data-driven culture to implement integrated SCF practices to survive in today’s data-rich and uncertain environment.
Keywords: Big data analytics capability | Data-driven culture | Integrated supply chain finance | Information processing capability
A dynamic classification unit for online segmentation of big data via small data buffers
واحد طبقه بندی پویا برای تقسیم آنلاین داده های بزرگ از طریق بافر داده های کوچک-2020
In many segmentation processes, we assign new cases according to a model that was built on the basis of past cases. As long as the new cases are “similar enough” to the past cases, segmentation proceeds normally. However, when a new case is substantially different from the known cases, a reexamination of the previously created segments is required. The reexamination may result in the creation of new segments or in the updating of the existing ones. In this paper, we assume that in big and dynamic data environments it is not possible to reexamine all past data and, therefore, we suggest using small groups of selected cases, stored in small data buffers, as an alternative to the collection of all past data. We present an incremental dynamic classifier that supports real-time unsupervised segmentation in big and dynamic data environments. In order to reduce the computational effort of unsupervised clustering in such environments, the suggested model performs calculations only on the relevant data buffers that store the relevant representative cases. In addition, the suggested model can serve as a dynamic classification unit (DCU) that can act as an autonomous agent, as well as collaborate with other DCUs. The evaluation is presented by comparing three approaches: static, dynamic, and incremental dynamic.
Keywords: Incremental dynamic classifier | Dynamic segmentation | Incremental data analysis | Cluster analysis | Classification | Big data
Quantile regression in big data: A divide and conquer based strategy
رگرسیون کمی در داده های بزرگ: یک استراتژی مبتنی بر تقسیم و غلبه-2020
Quantile regression, which analyzes the conditional distribution of outcomes given a set of covariates, has been widely used in many fields. However, the volume and velocity of big data make the estimation of quantile regression model extremely difficult due to the intensive computation and the limited storage. Based on divide and conquer strategy, a simple and efficient method is proposed to address this problem. The proposed approach only keeps summary statistics of each data block and then can use them to reconstruct the estimator of the entire data with asymptotically negligible approximation error. This property makes the proposed method particularly appealing when data blocks are retained in multiple servers or come in the form of data stream. Furthermore, the proposed estimator is shown to be consistent and asymptotically as efficient as the estimating equation estimator calculated using the entire data together when certain conditions hold. The merits of the proposed method are illustrated using both simulation studies and real data analysis
Keywords: Data stream | Divide and conquer | Estimating equation | Massive data sets | Quantile regression