the following contains aothors, year and abstrsct of papers create alitrature review for Predictive Data analytics for Supply chain demand forecasting

Pu C.; Ren J.; Xue B.	2021	A predictive control strategy based on the fuzzy neural network (FNN) for baking furnace is proposed. This method combines T-S fuzzy model with the RBF neural network to form a FNN. The temperature control system of anode baking furnace is taken as the object of simulation and predictive modeling. The T-S fuzzy RBF neural network (T-SFRBFNN) is used to model the off-line predictive control of the controlled system to provide online learning opportunities for the parameters of T-SFRBFNN. The self-compensating feedback correction method is used to adjust the output of the predictive model directly online to achieve the purpose of real-time control. The predictive control algorithm based on the FNN can establish an accurate multi-step predictive control model in the off-line state when the information of the controlled process is not fully understood and the precision of the model of the controlled object is not high. In online state, the predictive control algorithm can be calibrated by on-line feedback with self-compensation function. Meanwhile, the gradient descent method is used to adaptively adjust the parameters in the FNN model of the controller to realize the intelligent control of the controlled process. Simulation results show that the control method is simple, real-time and effective. It has good robustness and adaptability. It provides a theoretical basis for the producing high-quality carbon anode. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Birkel H.; Kopyto M.; Lutz C.	2020	The field of Predictive Analytics (PA) provides the possibility to utilize large amounts of data to improve forecasting, data-driven decision-making, and competitive advantage. Especially the transport logistics sector, which is characterized by high business-related uncertainties, time-sensitivity, and volatility, highly benefits from accurate resource and production planning. While success factors and framework conditions of applying PA are well-investigated on a theoretical SCM level, findings on internal and external challenges of transport logistics organizations remain scarce. Therefore, based on a multiple case approach, this study offers in-depth insights into six real-world cases of freight forwarders, ocean carriers, and air carriers. The results uncover both internal and external challenges. From the internal perspective, the biggest challenges are related to the technical implementation including the acquisition of globally generated, internal and external data and its harmonization. In addition, stakeholder management and target setting impede the development of PA. Regarding external challenges, relational and external conditions hamper the application. Therefore, especially actions of third-party institutions in terms of standardization and security enhancements are required. This study contributes to the existing literature in various ways as the systematic identification addresses real-world issues of PA in the neglected but crucial area of transport logistics, discussing urgent research needs and highlighting potential solutions. Additionally, the results offer valuable guidance for managers when implementing PA in transport logistics. © 2020 ACM.
Brintrup A.; Pak J.; Ratiney D.; Pearce T.; Wichmann P.; Woodall P.; McFarlane D.	2020	Although predictive machine learning for supply chain data analytics has recently been reported as a significant area of investigation due to the rising popularity of the AI paradigm in industry, there is a distinct lack of case studies that showcase its application from a practical point of view. In this paper, we discuss the application of data analytics in predicting first tier supply chain disruptions using historical data available to an Original Equipment Manufacturer (OEM). Our methodology includes three phases: First, an exploratory phase is conducted to select and engineer potential features that can act as useful predictors of disruptions. This is followed by the development of a performance metric in alignment with the specific goals of the case study to rate successful methods. Third, an experimental design is created to systematically analyse the success rate of different algorithms, algorithmic parameters, on the selected feature space. Our results indicate that adding engineered features in the data, namely agility, outperforms other experiments leading to the final algorithm that can predict late orders with 80% accuracy. An additional contribution is the novel application of machine learning in predicting supply disruptions. Through the discussion and the development of the case study we hope to shed light on the development and application of data analytics techniques in the analysis of supply chain data. We conclude by highlighting the importance of domain knowledge for successfully engineering features. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.