reformat the following according to following key words: Data Analytics, Supply Chain, Big Data, Supply chain management, Demand Forecasting

Predictive data analytics has become increasingly important in supply chain demand forecasting, as it offers various benefits such as improved operational efficiency, cost reduction, and enhanced decision-making. The application of big data analytics and machine learning technologies has revolutionized the way supply chain management activities are conducted, enabling organizations to better anticipate and react to changes in market dynamics. This literature review focuses on recent research findings that demonstrate the applicability and effectiveness of predictive data analytics in supply chain demand forecasting.

In recent studies, researchers have highlighted the potential of big data and predictive analytics in improving the social and environmental sustainability of supply chains (Dubey et al., 2019). Implementing big data solutions and making use of real-time key performance indicators (KPIs) of the supply chain enables companies to evaluate their operations within a dynamic system (Dev et al., 2019).

Furthermore, advancements in analytics and machine learning have led to the development of novel approaches such as Poly-linear Regression with Augmented Long Short Term Memory Neural Network (PLR-ALSTM-NN) (Ahmed et al., 2022). This model, based on deep learning, has been shown to predict future financial market trends more effectively than traditional neural networks and machine learning tools.

Another area of interest is the integration of information and communication technologies (ICT) with big data analytics and blockchain technologies to create next-generation agricultural marketing systems in India (Shrivastava & Pal, 2019). The proposed framework comprises functionalities such as Market Intelligence, Food Supply Chain, and One Stop Mobile App, with the aim of facilitating better decision-making for farmers and improving overall supply chain efficiency.

The use of machine learning algorithms for stock price prediction has also been investigated (Wang, 2021). Although predicting stock prices remains a challenging task due to the presence of numerous influencing factors, the application of high-level machine learning models such as auto-ARIMA, Prophet, and LSTM demonstrates the potential for more accurate predictions.

Moreover, the implementation of digital twins in supply chain networks enables data-driven, real-time decision-making and improved resilience (Kulaç et al., 2022). By providing a digital replica of a supply chain system, digital twins can help organizations monitor operations, perform predictive analytics, and optimize their processes.

Galli et al. (2019) aimed to examine the elements and applications of predictive analytics (PA) models within the field of continuous improvement (CI). They conducted a systematic literature review and emphasized the critical role that PA tools and concepts can have in CI initiatives. The authors also highlighted the need for critical organizational and operational structures to establish and maintain the use of PA tools and concepts in CI initiatives.

Gong et al. (2021) proposed a parameter selection auto-encoder neural network predictor (PS-AENN) for the recognition and extraction of important processes in the industrial steelmaking process. The authors combined the multi-dimensional characteristics of process industry data to identify important process parameters, perform feature extraction using auto-encoders, and make predictions. Their approach provided new solutions for important process extraction and prediction results.

Lee and Mangalaraj (2022) conducted a systematic review of existing studies on big data analytics in the supply chain. They presented a framework for analyzing the literature from both organizational and technical perspectives. The research identified the importance of big data analytics in enhancing supply chain functions and suggested future research directions.

Sodero et al. (2019) explored the social process of big data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM). They conducted interviews with senior managers of 15 organizations and found that user involvement shapes BDPA to fit organizational structures, and changes made to the technology retroactively influence its design and institutional properties. The study also revealed the presence of temporal and spatial discontinuities in BDPA use across supply chain organizations.

Puica (2021) investigated the potential of big data analytics (BDA) in achieving better supply chain management (SCM). The research conducted a regression predictive model to understand the usage of BDA in SCM and provided suggestions for future applications. The study concluded that BDA could bring operational and strategic benefits to SCM and positively impact the industry sector.


Lu et al. (2021) focused on the hydropower industry and the importance of accurate forecasting in power generation. They adopted the time series analysis method of ARIMA for data preprocessing and found that the ARIMA model has good data fit and adequate extraction compared to linear regression and exponential prediction models. Their study showed that hydropower generation will continue to develop sustainably.

Stekelorum et al. (2021) examined the role of suppliers’ supply chain ambidexterity and big data predictive analytics capabilities in supporting buyer firms’ responsible governance mechanism for circular economy practices improvements. Their findings suggest that the focal firm’s supplier-selection mechanism significantly and positively influences its circular economy practices.

Boone et al. (2019) reviewed the impact of the explosion of data on product forecasting and how it is improving. Their study focused on time series data and explored how such data could be used to obtain insights into consumer behavior and the impact of such data on organizational forecasting.

Tan et al. (2022) analyzed the time series data on world coffee prices using data analytics techniques such as Lyapunov exponent, entropy, and Hurst exponent. They presented an echo state network model for forecasting time series data and employed the grey wolf optimization algorithm to improve the accuracy and efficiency of the prediction method.

Ren et al. (2020) proposed a data-driven method based on wide-deep-sequence model to provide a reliable quality prediction for industrial processes with different types of industrial data. Their study focused on product quality prediction and used the joint training strategy to combine and optimize models for reliable predictions.

Several other studies also focused on sustainable supply chain management, emergency situations, and the implementation of the latest technology in supply chains. These studies emphasize the importance of predictive data analytics and big data in enhancing various sectors’ performance and forecasting capabilities.

Lu et al. (2021) focused on the hydropower industry and the importance of accurate forecasting in power generation. They adopted the time series analysis method of ARIMA for data preprocessing and found that the ARIMA model has good data fit and adequate extraction compared to linear regression and exponential prediction models. Their study showed that hydropower generation will continue to develop sustainably.

Stekelorum et al. (2021) examined the role of suppliers’ supply chain ambidexterity and big data predictive analytics capabilities in supporting buyer firms’ responsible governance mechanism for circular economy practices improvements. Their findings suggest that the focal firm’s supplier-selection mechanism significantly and positively influences its circular economy practices.

Boone et al. (2019) reviewed the impact of the explosion of data on product forecasting and how it is improving. Their study focused on time series data and explored how such data could be used to obtain insights into consumer behavior and the impact of such data on organizational forecasting.

Tan et al. (2022) analyzed the time series data on world coffee prices using data analytics techniques such as Lyapunov exponent, entropy, and Hurst exponent. They presented an echo state network model for forecasting time series data and employed the grey wolf optimization algorithm to improve the accuracy and efficiency of the prediction method.

Ren et al. (2020) proposed a data-driven method based on wide-deep-sequence model to provide a reliable quality prediction for industrial processes with different types of industrial data. Their study focused on product quality prediction and used the joint training strategy to combine and optimize models for reliable predictions.

Several other studies also focused on sustainable supply chain management, emergency situations, and the implementation of the latest technology in supply chains. These studies emphasize the importance of predictive data analytics and big data in enhancing various sectors’ performance and forecasting capabilities.


Kamble et al. (2020) highlight the increasing use of emerging technologies like the internet of things, blockchain, and big data in agriculture supply chains. They propose an application framework for practitioners to develop data-driven, sustainable agriculture supply chains. Dubey et al. (2019) stress the positive impact of big data analytics capability (BDAC) on trust and collaborative performance between civil and military organizations engaged in disaster relief operations. The study claims that a flexible orientation of organizational culture bolsters the effect of BDAC on performance.

Brandtner et al. (2021) reveal the potential of big data in predicting future supply chain behavior and decision-making impacts. Some of the current applications include enhancing transparency in logistics and SCM, improving demand planning, and supporting supplier quality management. Sathyan et al. (2021) discuss how a combined approach of big data analytics and fuzzy decision-making can improve demand forecasting in the automotive industry. Their model achieved an accuracy rate of 95.5% in demand predictions.

Sharma et al. (2022) discuss the role of big data analytics in helping companies combat crises like the COVID-19 pandemic. The study finds that although the current adoption rate of BDA in the Indian industry is not statistically significant, plans for future adoption of BDA for crisis management are growing. Cadden et al. (2021) emphasize the impact of big data analytics and dynamic capability development in enhancing supply chain agility, particularly in turbulent and competitive environments.

Cadden et al. (2021) explore the influence of turbulent and competitive environments on big data analytics capabilities and supply chain agility. The study analyzed survey data from 201 UK manufacturers and found that firms should leverage the volume, velocity, and variety facets of big data to enable sensing and creative search capabilities in turbulent environments. In competitive environments, firms should scale back on time-consuming search capabilities and focus on the velocity aspects of big data to enhance supply chain agility. The study also emphasizes the importance of strategic alignment and predictive forecasting analytics in developing big data and dynamic capability in supply chain management.

Moro Visconti and Morea (2019) examine whether big data can improve the quality and timeliness of information in infrastructural healthcare Project Finance (PF) investments. Big data-driven business models are compared to standard healthcare Public-Private Partnership (PPP) investments. The study finds that public and private partners who network big data and interoperable databases can boost value co-creation, reduce risk, and make better informed decisions.

Furstenau et al. (2022) investigate the resilience capabilities supported by digital technologies (DT), including big data analytics, in healthcare supply chains (HSC). The study identified 14 DTs, which supported organizational capabilities of resilient HSC, such as long-term collaborative planning and strategic alliances. A framework was developed that describes what and how technology adoption improves HSC resilience.

Bag and Arnesh (2019) discuss the importance of big data and predictive analytics in humanitarian supply chain operations, with a focus on managing disaster risks. The study identifies key barriers to the adoption of big data and predictive analytics and develops a contextual understanding using Fuzzy Total Interpretive Structural Modeling (TISM). The authors note that the removal of bottom-level barriers is necessary for achieving sustainability in humanitarian supply chain operations.

Kumar et al. (2021) explore the benefits and applications of various types of analytics, such as descriptive, predictive, and prescriptive analytics, in the supply chain management process. The study also discusses strategies to integrate analytics within the SCM curriculum to prepare future supply chain analysts and reduce the gap between academia and industry perspectives.


Pu et al. (2021) propose a predictive control strategy based on a fuzzy neural network (FNN) for baking furnace control. The authors use the temperature control system of an anode baking furnace as their simulation and predictive modeling subject, employing a T-S fuzzy RBF neural network (T-SFRBFNN) for offline predictive control. The FNN-based predictive control algorithm provides an accurate multi-step predictive control model with self-compensating feedback correction for real-time control. This approach is reported to be simple, real-time, effective, and adaptable, and it provides a theoretical basis for producing high-quality carbon anodes.

Birkel et al. (2020) focus on the challenges faced by transport logistics organizations in adopting predictive analytics. They present a multiple case study approach covering six cases related to freight forwarders, ocean carriers, and air carriers. The study uncovers various internal and external challenges, such as technical implementation, data acquisition and harmonization, stakeholder management, and external conditions affecting the adoption of predictive analytics in transport logistics. The authors emphasize the need for actions by third-party institutions to address issues like standardization and security enhancements.

Brintrup et al. (2020) present a practical case study on using data analytics for predicting first-tier supply chain disruptions in an Original Equipment Manufacturer (OEM). They outline a three-phase methodology, including exploratory data analysis to select and engineer potential features, development of performance metrics for evaluating success, and experimental design to analyze the success rate of various algorithms and parameters on selected feature spaces. The study reports an 80% accuracy in predicting late orders by adding engineered features, particularly “agility,” to the data. The authors highlight the importance of domain knowledge for successfully engineering features and the novel application of machine learning in predicting supply disruptions.