the following contains aothors, year and abstrsct of papers create alitrature review for Predictive Data analytics for Supply chain demand forecasting
Kamble S.S.; Gunasekaran A.; Gawankar S.A. 2020 The lack of industrialization, inadequacy of the management, information inaccuracy, and inefficient supply chains are the significant issues in an agri-food supply chain. The proposed solutions to overcome these challenges should not only consider the way the food is produced but also take care of societal, environmental and economic concerns. There has been increasing use of emerging technologies in the agriculture supply chains. The internet of things, the blockchain, and big data technologies are potential enablers of sustainable agriculture supply chains. These technologies are driving the agricultural supply chain towards a digital supply chain environment that is data-driven. Realizing the significance of a data-driven sustainable agriculture supply chain we extracted and reviewed 84 academic journals from 2000 to 2017. The primary purpose of the review was to understand the level of analytics used (descriptive, predictive and prescriptive), sustainable agriculture supply chain objectives attained (social, environmental and economic), the supply chain processes from where the data is collected, and the supply chain resources deployed for the same. Based on the results of the review, we propose an application framework for the practitioners involved in the agri-food supply chain that identifies the supply chain visibility and supply chain resources as the main driving force for developing data analytics capability and achieving the sustainable performance. The framework will guide the practitioners to plan their investments to build a robust data-driven agri-food supply chain. Finally, we outline the future research directions and limitations of our study. © 2019 Elsevier B.V.
Dubey R.; Gunasekaran A.; Childe S.J.; Roubaud D.; Fosso Wamba S.; Giannakis M.; Foropon C. 2019 The main objective of the study is to understand how big data analytics capability (BDAC) as an organizational culture can enhance trust and collaborative performance between civil and military organizations engaged in disaster relief operations. The theoretical framework is grounded in organizational information processing theory (OIPT). We have conceptualized an original theoretical model to show, using the competing value model (CVM), how BDAC, under a moderating influence of organizational culture, affects swift trust (ST) and collaborative performance (CP). We used WarpPLS 6.0 to test the proposed research hypotheses using multi-respondent data gathered through an email questionnaire sent to managers working in 373 organizations, including the military forces of different countries, government aid agencies, UN specialized agencies, international non-government organizations (NGOs), service providers, and contractors. The results offer four important implications. First, BDAC has a positive, significant effect on ST and CP. Second, flexible orientation (FO) and controlled orientation (CO) have no significant influence on building ST. Third, FO has a positive and significant moderating effect on the path joining BDAC and CP. Finally, CO has negative and significant moderating effect on the path joining BDAC and CP. The control variables: temporal orientation (TO) and interdependency (I) have significant effects on ST and CP. These results extend OIPT to create a better understanding of the application of information processing capabilities to build swift trust and improve collaborative performance. Furthermore, managers can derive multiple insights from this theoretically-grounded study to understand how BDAC can be exploited to gain insights in contexts of different management styles and cultures. We have also outlined the study limitations and provided numerous future research directions. © 2019
Brandtner P.; Udokwu C.; Darbanian F.; Falatouri T. 2021 The increased amount of data being generated in virtually every context provides huge potential for a variety of organisational application fields, one of them being Supply Chain Management (SCM). The possibilities and use cases of applying this new type of data, i.e. Big Data (BD), is huge and a large body of research has already been conducted in this area. The current paper aims at identifying the understanding and the applications of BD not from an academic but a practitioners’ point of view. By applying expert interviews, the main aim is to identify (i) a definition of Big Data from SCM practitioners’ point of view, (ii) current SCM activities and processes where BD is already used in practice, (iii) potential future application fields for BD as seen in SCM practice and (iv) main hinderers of BD application. The results show that Big Data is referred to as complex data sets with high volumes and a variety of sources that can’t be handled with traditional approaches and require data expert knowledge and SCM domain knowledge to be used in organisational practical. Current applications include the creation of transparency in logistics and SCM, the improvement of demand planning or the support of supplier quality management. The interviewed experts coincide in the view, that BD offers huge potential in future SCM. A shared vision was the implementation of real-time transparency of Supply Chains (SC), the ability to predict the behavior of SCs based on identified data patterns and the possibility to predict the impact of decisions on SCM before they are taken. © 2021 ACM.
Sathyan R.; Parthiban P.; Dhanalakshmi R.; Minz A. 2021 The vital task of improving the Responsiveness of the automotive supply chains is to forecast the demand and analyze the vehicle’s most influential attributes. The purpose of this paper is to develop a model to forecast the demand and analyzing the vehicle attributes using a combined approach of big data analytics and fuzzy decision-making trial and evaluation laboratory (DEMATEL) technique. The forecasting process includes the sentiment analysis of product review and creating a predictive model using an artificial neural network algorithm. The most influential attributes of the vehicle were extracted from online customer reviews and these attributes were analyzed using the Fuzzy DEMATEL method. A newly introduced vehicle in the Mid- SUV segment of the Indian automotive sector has been chosen as a case to illustrate the developed model. The forecasted demand shows an accuracy of 95.5% and the price of the vehicle and safety features are identified as attributes with higher prominence value. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
Sharma P.; Tiwari S.; Choi T.; Kaul A. 2022 COVID-19 pandemic has created disruptions and risks in global supply chains. Big data analytics (BDA) has emerged in recent years as a potential solution for provisioning predictive and pre-emptive information to companies in order to preplan and mitigate the impacts of such risks. The focus of this article is to gain insights into how BDA can help companies combat a crisis like COVID-19 via a multimethodological scientific study. The advent of a crisis like COVID-19 brings with it uncertainties, and information processing theory (IPT) provides a perspective on the ways to deal with such uncertainties. We use IPT, in conjunction with the Crisis Management Theory, to lay the foundation of the article. After establishing the theoretical basis, we conduct two surveys towards supply chain managers, one before and one after the onset of the COVID-19 pandemic in India. We follow it up with qualitative interviews to gain further insights. The application of multiple methods helps ensure the triangulation of results and, hence, enhances the research rigor. Our research finds that although the current adoption of BDA in the Indian industry has not grown to a statistically significant level, there are serious future plans for the industry to adopt BDA for crisis management. The interviews also highlight the current status of adoption and the growth of BDA in the Indian industry. The article interestingly identifies that the traditional barriers to implementing new technologies (like BDA for crisis management) are no longer present in the current times. The COVID-19 pandemic has hence accelerated technology adoption and at the same time uncovered some BDA implementation challenges in practice (e.g., a lack of data scientists). IEEE
Cadden T.; Cao G.; Treacy R.; Yang Y.; Onofrei G. 2021 The study draws on the dynamic capability perspective to explore how turbulent and competitive environments influence big data analytics capabilities which, in turn, impact supply chain (SC) agility. Survey data from 201 UK manufacturers is collected and analysed, and a moderation model is presented. The results show that in turbulent environments, characterized by high degrees of environmental dynamism, firms should leverage the volume, velocity and variety facets of big data which, in turn, enable sensing and creative search (dynamic) capabilities needed to adapt in such environments. In competitive environments however, where first mover advantage is crucial, firms should scale back on time consuming search capabilities (data variety). At the operational level, firms should exclusively leverage the velocity aspects of big data to enhance SC agility. Finally, while, previous studies have focused on analytical maturity as a prerequisite to big data implementation, this study finds that a reconfigured analytical orientation culture specifically on responsiveness, i.e. strategic alignment and predictive forecasting analytics, moderates the relationship between big data velocity and SC agility. The results of this study therefore fill a key gap in the SC management literature as the study demonstrates how environmental factors, both internal and external, influence big data and dynamic capability development in order to enhance SC agility. © 2021, IFIP International Federation for Information Processing.
Moro Visconti R.; Morea D. 2019 This study aims to detect if and how big data can improve the quality and timeliness of information in infrastructural healthcare Project Finance (PF) investments,making themmore sustainable, and increasing their overall efficiency. Interactions with telemedicine or disease management and prediction are promising but are still underexploited. However, given rising health expenditure and shrinking budgets, data-driven cost-cutting is inevitably required. An interdisciplinary approach combines complementary aspects concerning big data, healthcare information technology, and PF investments. The methodology is based on a business plan of a standard healthcare Public-Private Partnership (PPP) investment, compared with a big data-driven business model that incorporates predictive analytics in different scenarios. When Public and Private Partners interact through networking big data and interoperable databases, they boost value co-creation, improving Value for Money and reducing risk. Big data can also help by shortening supply chain steps, expanding economic marginality and easing the sustainable planning of smart healthcare investments. Flexibility, driven by timely big data feedbacks, contributes to reducing the intrinsic rigidity of long-termed PF healthcare investments. Healthcare is a highly networked and systemic industry, that can benefit from interacting with big data that provide timely feedbacks for continuous business model re-engineering, reducing the distance between forecasts and actual occurrences. Risk shrinks and sustainability is fostered, together with the bankability of the infrastructural investment. © 2019 by the authors.
Furstenau L.B.; Zani C.; Terra S.X.; Sott M.K.; Choo K.-K.R.; Saurin T.A. 2022 Digital technologies (DT) help decision-makers cope with disruptive events and, consequently, boost the resilience of healthcare supply chains (HSC). However, previous studies on HSC have not focused on the types of resilience capabilities supported by respective DT or taken a holistic perspective of resilience from both proactive and reactive dimensions. In order to address this gap, we conducted a study of eight healthcare organizations based on semi-structured interviews with 15 HSC managers and document analysis. As a result, we identified 14 DT, such as big data analytics, predictive health data analysis, and remote monitoring of inventories. These DT supported organizational capabilities of resilient HSC, such as long-term collaborative planning and strategic alliances based on trust and shared goals. These findings were articulated in a framework that describes what and how technology adoption improves HSC resilience. We also present four research propositions for theory-testing in future studies, which stress that the relationship between DT and the four major potentials of resilient systems (i.e., anticipating, responding, monitoring and learning) is mediated by organizational resilience capabilities. The framework is expected to offer guidance and ideas for managers and policy-makers interested in designing and operating resilient HSC. © 2022
Bag S.; Arnesh T. 2019 Big data and Predictive analytics (BDPA) research in humanitarian supply chain operations has gained popularity for its ability to manage disaster risks. BDPA approach not only aims to predict future events, but also concentrates on current events and further prepare for the future events. However, review of prior studies shows this area is highly diverse and fragmented. Further, BDPA driven approach in HSC operations is complicated due to presence of multiple barriers, whose interrelationships are yet to be documented for purpose of enhancing existing knowledge base. The gaps in existing literature motivated to pursue the current study and aim to identify the leading barriers and further categorize them and finally develop the contextual interrelationships using popular Fuzzy Total Interpretive Structural Modeling (TISM) approach. Fuzzy TISM is a well expressed mental model interpreting both the nodes (indicating ‘what’) and links (indicating ‘how’ and ‘why’) which serves as the basis for theory building process. The TISM model shows that the fifteen barriers have achieved eight levels and decision makers must aim to remove the bottom level barriers for achieving sustainability in humanitarian supply chain operations. © 2019, IEOM Society International.
Kumar A.; Shrivastav S.K.; Oberoi S.S. 2021 Information and communication technology (ICT) has been the backbone of businesses for some time now. ICT adoption has rendered data availability in vertically connected organizations. In recent times, the rise of analytics has paved the way to rethink the structure and working of vertically connected firms within a supply chain. Big data analytics (BDA) for instance, has become a prominent tool to analyse unstructured data. This study explores the application and benefit of various types of analytics such as descriptive analytics, predictive analytics and prescriptive analytics in the process of supply chain management (SCM). Additionally, the study also looks at ways by which analytics could integrally be included within the SCM curriculum in higher education to prepare future supply chain analyst. Notably, the article is positioned for bridging the gap between the use of analytics in SCM, both from academia and the industry perspective. © 2021 Fortune Institute of International Business.