Galli B.J.; Ocampo L.A.	2019	This study aimed to examine the elements and applications of predictive analytics (PA) models within the field of continuous improvement (CI). While the roles of data science, PA, and big data have been explored in the current literature (e.g., supply chain management), fragmented insights scarcely exist concerning the role of PA on CI initiatives. Articles concerning the role of PA and BI in CI projects were utilised in a systematic literature review. Furthermore, the paper stresses the vital role that big data analytics plays in both CI and PA during decision-making. The paper studies other important CI and PA aspects that pertain to continuous improvement projects, which are aspects that are often overlooked in the existing literature. The findings from this study emphasise the critical role that PA tools and concepts can and should have in CI initiatives. Critical organisational and operational structures also need to be implemented to establish and maintain the use of PA tools and concepts in CI initiatives. Overall, this paper serves to fill the gap that overlooks the relationship between these two variables. Copyright © 2019 Inderscience Enterprises Ltd.
Gong J.; Yu H.; Huang C.	2021	In the process of industrial steelmaking, the existing predictive models and control models are not good enough for the recognition and extraction of important processes. How to use these data to extract important processes in the process industry to achieve high-quality automated production and more accurate and rapid prediction effect is also a problem worth challenging at present. Therefore, this paper proposes a parameter selection auto-encoder neural network predictor (PS-AENN) based on the interpretability of the fully connected neural network. Combining the multi-dimensional characteristics of process industry data, the neural network interpretability scheme is first introduced to identify important process parameters, then the feature extraction is carried out by the auto-encoder, and finally to make prediction. This method of deep learning and process selection provides new solutions for important process extraction and prediction results. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lee I.; Mangalaraj G.	2022	Big data analytics has been successfully used for various business functions, such as ac-counting, marketing, supply chain, and operations. Currently, along with the recent development in machine learning and computing infrastructure, big data analytics in the supply chain are surging in importance. In light of the great interest and evolving nature of big data analytics in supply chains, this study conducts a systematic review of existing studies in big data analytics. This study presents a framework of a systematic literature review from interdisciplinary perspectives. From the organizational perspective, this study examines the theoretical foundations and research models that explain the sustainability and performances achieved through the use of big data analytics. Then, from the technical perspective, this study analyzes types of big data analytics, techniques, algorithms, and features developed for enhanced supply chain functions. Finally, this study identifies the research gap and suggests future research directions. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Sodero A.; Jin Y.H.; Barratt M.	2019	Purpose: The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations. Design/methodology/approach: The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC. Findings: Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations. Practical implications: This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place. Originality/value: The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area. © 2019, Emerald Publishing Limited.
Puica E.	2021	The research problem that is the interest in this thesis is to understand the Big Data Analytics (BDA) potential in achieving a much better Supply Chain Management (SCM). Based on this premise, it was conducted a Regression Predictive Model to comprehend the usage of Big Data Analytics in SCM and to have insights of the requirements for the potential applications of BDA. In this study were analyzed the main sources of BDA utilized in present by Supply Chain professionals and it was provided future suggestions. The findings of the study suggest that BDA may bring operational and strategic benefit to SCM, and the application of BDA may have positive implication for industry sector. © 2021, IFIP International Federation for Information Processing.
Bag S.; Rahman M.S.; Srivastava G.; Chan H.-L.; Bryde D.J.	2022	Most parts of the world have suffered the negative impacts from extreme weather events, in whatever form they may take. To mitigate such impacts, attention in the operations management literature has focused on how firms build resilience in their supply chains, in order to quickly respond to such events and also to return, as soon as possible, to a business-as-usual state. Prior studies have examined the process of building a supply chain (SC) in different countries, industries and in response to various disruptions, such as the COVID-19 pandemic, whilst, at the same time, calling for further research in different contexts. We respond to these calls by exploring SC resilience ability in the South African mining industry under extreme weather events. We situated our study in the dynamic capability view (DCV) view of the firm. We examined the direct effect of big data and predictive analytics (BDPA) capabilities on SC visibility and the final effects on community and resource resilience. We adopted a sequential mixed methods research design, collecting data from interviews with 10 industry practitioners and from 219 respondents to an online survey. We built and tested our theoretical model using partial least squares structured equation modelling (PLS-SEM). Notable theoretical contributions of our study are that big data enables a more efficient supply chain monitoring system, which, in turn, improves SC visibility. BDPA capability improves a company's ability to make the best use of its available resources. It improves the South African mining industry's dynamic capability, allowing them to adjust their strategies in response to diverse adverse weather conditions. Furthermore, BDPA capability's ability to improve SC visibility is enhanced when there is strong alignment between BDPA strategy and initiatives. Finally, having a high level of SC visibility helps develop community and resource resilience, which are necessary to ensure that firms in the industry fulfil their responsibilities in relation to social sustainability. © 2022 Elsevier B.V.
Koot M.; Mes M.R.K.; Iacob M.E.	2021	The willingness to invest in Internet of Things (IoT) and Big Data Analytics (BDA) seems not to depend on supply nor demand of technological innovations. The required sensing and communication technologies have already matured and became affordable for most organizations. Businesses on the other hand require more operational data to address the dynamic and stochastic nature of supply chains. So why should we wait for the actual implementation of tracking and monitoring devices within the supply chain itself? This paper provides an objective overview of state-of-the-art IoT developments in today's supply chain and logistics research. The main aim is to find examples of academic literature that explain how organizations can incorporate real-time data of physically operating objects into their decision making. A systematic literature review is conducted to gain insight into the IoT's analytical capabilities, resulting into a list of 79 cross-disciplinary publications. Most researchers integrate the newly developed measuring devices with more traditional ICT infrastructures to either visualize the current way of operating, or to better predict the system's future state. The resulting health/condition monitoring systems seem to benefit production environments in terms of dependability and quality, while logistics operations are becoming more flexible and faster due to the stronger emphasis on prescriptive analytics (e.g., association and clustering). Further research should extend the IoT's perception layer with more context-aware devices to promote autonomous decision making, invest in wireless communication networks to stimulate distributed data processing, bridge the gap in between predictive and prescriptive analytics by enriching the spectrum of pattern recognition models used, and validate the benefits of the monitoring systems developed. © 2020 The Author(s)
Polychronou I.; Stoitsis G.; Papakonstantinou M.; Manouselis N.	2020	One of the significant challenges for the future is to guarantee safe food for all inhabitants of the planet. During the last 15 years, very important fraud issues like the 2013 horse meat scandal and the 2008 Chinese milk scandal have greatly affected the food industry and public health. One of the alternatives for this issue consists of increasing production, but to accomplish this, it is necessary that innovative options be applied to enhance the safety of the food supply chain. For this reason, it is quite important to have the right infrastructure in order to manage data of the food safety sector and provide useful analytics to Food Safety Experts. In this paper, we describe Agroknow s Big Data Platform architecture and examine its scalability for data management and experimentation. © 2020 Inderscience Enterprises Ltd.. All rights reserved.
Ali M.I.; Patel P.; Breslin J.G.	2019	Industry 4.0 is a recent trend of automation for manufacturing technologies and represents the fourth industrial revolution which transforms current industrial processes with the use of technologies such as automation, data analytics, cyber-physical systems, IoT, artificial intelligence, etc. The vision of Industry 4.0 is to build an end-to-end industrial transformation with the support of digitization. Data analytics plays a key role to get a better understanding of business processes and to design intelligent decision support systems. However, a key challenge faced by industry is to integrate multiple autonomous processes, machines and businesses to get an integrated view for data analytics activities. Another challenge is to develop methods and mechanisms for real-time data acquisition and analytics on-the-fly. In this paper, we propose a semantically interoperable framework for historical data analysis combined with real-time data acquisition, event detection, and real-time data analytics for very precise production forecasting within a manufacturing unit. Besides historical data analysis techniques, our middleware is capable of collecting data from diverse autonomous applications and operations in real time using various IoT devices, analyzing the collected data on the fly, and evaluating the impact of any detected unexpected events. Using semantic technologies we integrate multiple autonomous systems (e.g. production system, supply chain management and open data). The outcome of real-time data analytics is used in combination with machine learning models trained over historical data in order to precisely forecast production in a manufacturing unit in real time. We also present our key findings and challenges faced while deploying our solution in real industrial settings for a large manufacturing unit. © 2019 IEEE.
Guo X.; Yang Z.; Sun J.	2022	The COVID-19 pandemic and trade frictions impact the continuity of supply chain (SC) operations. In the volatile environment, big data analytics (BDA), a key technology for storing data and predictive analytics, has become an important tool for mitigating SC vulnerability. Based on the literature review, this paper identifies six influencing factors and four vulnerability drivers for mitigating vulnerability, and employs Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to a Classification (MICMAC) to explore the influence pathways that BDA mitigates SC vulnerability. The findings show that BDA can influence knowledge acquisition and strategy formulation by improving the forecasting capability of enterprises, which facilitates strategy implementation and ultimately mitigates vulnerability. Furthermore, with the support of BDA, resource redundancy addresses vulnerability from supply-side, higher production level and efficiency reduce vulnerability from demand-side, and rational SC design alleviates vulnerability from operation-side.  © 2022 IEEE.
Lu Z.X.; Chen J.C.; Ma S.M.	2021	Hydropower is a kind of clean energy with sustainable development. Hydropower generation has strong periodic regularity, so the prediction and control of power generation are particularly important. Real-time and accurate hydroelectric power generation forecasting is a key issue in the energy industry. In this paper, the time series analysis method of ARIMA (p, d, 0) model structure is used for data preprocessing, and the ARIMA (2, 1, 2) model was finally constructed by identification, model comparison, and testing. After comparison, it can be seen that compared with linear regression and exponential prediction models, the ARIMA model has good data fit and adequate extraction. The prediction method is simple and concise, and the short-term prediction is accurate, which can better adapt to the cyclicality of hydropower generation in all seasons. The model predicts the hydroelectric power generation of Guangxi in 2020 to be 5.3 billion kWh in May, 5.5 billion kWh in June, 41 kWh in July, 40 kWh in August, and 36 kWh in September. Based on this forecast, it can be seen that Guangxi's hydroelectric power generation will continue to develop in a sustainable trend, and its quantitative forecast results will provide Guangxi with the energy industry and provide useful references for production planning and industrial layout. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Stekelorum R.; Laguir I.; Lai K.-H.; Gupta S.; Kumar A.	2021	Recent years have seen a growing interest in the separate areas of responsible governance mechanisms and circular economy practices. However, the literature lacks evidence on supplier capabilities as a responsible governance mechanism pathway for buyer firms to improve their circular economy practices. Drawing on the natural resource-based view, and the dynamic capabilities perspective, this study examines suppliers’ supply chain ambidexterity and big data predictive analytics capabilities as enabling factors in support of buyer firms’ responsible governance mechanism for circular economy practices improvements. Using data from surveys conducted among 404 firms (202 focal firm-supplier dyads), the results suggest that the focal firm's supplier-selection mechanism significantly and positively influences its circular economy practices while the supplier-development mechanism has no such effect. We find further that the governance mechanisms vary in their effects on suppliers’ supply chain ambidexterity and subsequent big data analytics capabilities and circular economy practices improvements outcomes. In sum, our findings provide unique insights into the role of suppliers’ capabilities for responsible governance mechanisms to circular economy practices improvements from the perspective of the dyad. © 2021 Elsevier Ltd
Boone T.; Ganeshan R.; Jain A.; Sanders N.R.	2019	Forecasts have traditionally served as the basis for planning and executing supply chain activities. Forecasts drive supply chain decisions, and they have become critically important due to increasing customer expectations, shortening lead times, and the need to manage scarce resources. Over the last ten years, advances in technology and data collection systems have resulted in the generation of huge volumes of data on a wide variety of topics and at great speed. This paper reviews the impact that this explosion of data is having on product forecasting and how it is improving it. While much of this review will focus on time series data, we will also explore how such data can be used to obtain insights into consumer behavior, and the impact of such data on organizational forecasting. © 2018 International Institute of Forecasters
Tan N.D.; Yu H.C.; Long L.N.B.; You S.-S.	2022	Coffee has long become a familiar and famous drink all over the world. Due to supply chain disruptions, the coffee market is vibrant and changing rapidly. In this article, the time series data on world coffee price will be analysed by using data analytics such as Lyapunov exponent (LE), entropy, and Hurst exponent (HE). By denoising time series data using wavelet decomposition, the echo state network (ESN) model is presented for forecasting time series data. When the coffee commodity price is fluctuating and affecting to production planning and scheduling of supply chain management under market uncertainty, it is necessary to improve the accuracy and efficiency of the prediction method by employing the grey wolf optimisation (GWO) algorithm. By employing the hybrid technique, the predictive analytics models could efficiently estimate the actual prices. The GWO algorithm enhances a machining learning model’s performance for ensuring optimal forecasting with increased accuracy. Copyright © 2022 Inderscience Enterprises Ltd.
Ren L.; Meng Z.; Wang X.; Lu R.; Yang L.T.	2020	Product quality prediction, as an important issue of industrial intelligence, is a typical task of industrial process analysis, in which product quality will be evaluated and improved as feedback for industrial process adjustment. Data-driven methods, with predictive model to analyze various industrial data, have been received considerable attention in recent years. However, to get an accurate prediction, it is an essential issue to extract quality features from industrial data, including several variables generated from supply chain and time-variant machining process. In this article, a data-driven method based on wide-deep-sequence (WDS) model is proposed to provide a reliable quality prediction for industrial process with different types of industrial data. To process industrial data of high redundancy, in this article, data reduction is first conducted on different variables by different techniques. Also, an improved wide-deep (WD) model is proposed to extract quality features from key time-invariant variables. Meanwhile, an long short-term memory (LSTM)-based sequence model is presented for exploring quality information from time-domain features. Under the joint training strategy, these models will be combined and optimized by a designed penalty mechanism for unreliable predictions, especially on reduction of defective products. Finally, experiments on a real-world manufacturing process data set are carried out to present the effectiveness of the proposed method in product quality prediction.  © 2012 IEEE.
abovecontais authors, year of release and abstract of research papers summarise and write litrature review for Predictive Data analytics for Supply chain demand forecasting