scholarly journals A Comprehensive Study on Demand Forecasting Methods and Algorithms for Retail Industries

2021 ◽  
Vol 23 (06) ◽  
pp. 409-420
Author(s):  
Udbhav Vikas ◽  
◽  
Karthik Sunil ◽  
Rohini S. Hallikar ◽  
Pattem Deeksha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Demand Forecasting ◽  
Performance Comparison ◽  
Supply Side ◽  
Learning Approaches ◽  
Time Series Regression ◽  
On Demand ◽  
Forecasting Methods ◽  
Comprehensive Study

Without a doubt, demand forecasting is an essential part of a company’s supply chain. It predicts future demand and specifies the level of supply-side readiness needed to satisfy the demand. It is imperative that if a company’s forecasting isn’t reasonably reliable, the entire supply chain suffers. Over or under forecasted demand would have a debilitating impact on the operation of the supply chain, along with planning and logistics. Having acknowledged the importance of demand forecasting, one must look into the techniques and algorithms commonly employed to predict demand. Data mining, statistical modeling, and machine learning approaches are used to extract insights from existing datasets and are used to anticipate unobserved or unknown occurrences in statistical forecasting. In this paper, the performance comparison of various forecasting techniques, time series, regression, and machine learning approaches are discussed, and the suitability of algorithms for different data patterns is examined.

scholarly journals A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

2021 ◽  
pp. 002073142110174
Author(s):  
Md Mijanur Rahman ◽  
Fatema Khatun ◽  
Ashik Uzzaman ◽  
Sadia Islam Sami ◽  
Md Al-Amin Bhuiyan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Language Processing ◽  
Probabilistic Models ◽  
Health Care Systems ◽  
Learning Approaches ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

scholarly journals Review for the Solar Radiation Forecasting Methods Based on Machine Learning Approaches

2021 ◽  
Vol 1964 (4) ◽  
pp. 042065
Author(s):  
U Hemavathi ◽  
Ann C V Medona ◽  
V Dhilip Kumar ◽  
R Raja Sekar
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Learning Approaches ◽  
Forecasting Methods

Impact of Auto-regressive (AR) Process in Bullwhip Analysis in a Multi-location Supply Chain Network

2019 ◽  
Vol 6 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Prabhat Mittal
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Network Structure ◽  
Bullwhip Effect ◽  
Supply Chain Network ◽  
Risk Pooling ◽  
On Demand ◽  
Forecasting Methods ◽  
Auto Regressive ◽  
Ar Process

The present study is an attempt to quantify the Bullwhip Effect (BWE) -the phenomenon in which information on demand is distorted in moving up a supply chain. Assuming that the retailer employs an order-up-to level policy with auto-regressive process (AR), the paper investigates the influence of forecasting methods on bullwhip effect. Determining the order-up-to levels and the orders for the retailers’ demands in an isolated manner neglects the correlation of the demands and the relevant risk pooling effects associated with the network structure of the supply chains are disregarded. It is illustrated that the bullwhip effects are significantly reduced with consideration of potential correlation between the retailers’ demand.

scholarly journals Significance Of Multilayer Perceptron Model For Early Detection Of Diabetes Over Ml Methods

2021 ◽  
Vol 23 (08) ◽  
pp. 148-160
Author(s):  
Dr. V.Vasudha Rani ◽  
◽  
Dr. G. Vasavi ◽  
Dr. K.R.N Kiran Kumar ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Multilayer Perceptron ◽  
Predictive Analytics ◽  
Early Stage ◽  
Feature Selection Method ◽  
Health Condition ◽  
Performance Comparison ◽  
Learning Approaches ◽  
Learning Models ◽  
Machine Learning Models

Diabetes is one of the chronicdiseases in the world. Millions of people are suffering with several other health issues caused by diabetes, every year. Diabetes has got three stages such as type2, type1 and insulin. Curing of diabetes disease at later stages is practically difficult. Here in this paper, we proposed a DNN model and its performance comparison with some of the machine learning models to predict the disease at an earlystage based on the current health condition of the patient. An artificial neural network (ANN) is a predictive model designed to work the same way a human brain does and works better with larger datasets. Having the concept of hidden layers, neural networks work better at predictive analytics and can make predictions with more accuracy. Novelty of this work lies in integration of feature selection method used to optimize the Multilayer Perceptron (MLP) to reduce the number of required input attributes. The results achieved using this method and several conventional machines learning approaches such as Logistic Regression, Random Forest Classifier (RFC) are compared. The proposed DNN method is proved to show better accuracy than Machine learning models for early stage detection of diabetes. This paper work is applicable to clinical support as a tool for making predecisions by the doctors and physicians.

scholarly journals Demand forecasting and information platform in tourism

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 247-252 ◽  
Author(s):  
Yue Li ◽  
Qi-Jie Jiang
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Information Processing ◽  
Information Asymmetry ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Information Platform ◽  
On Demand ◽  
Processing Ability ◽  
Tourism Supply Chain

AbstractInformation asymmetry and the bullwhip effect have been serious problems in the tourism supply chain. Based on platform theory, this paper established a mathematical model to explore the inner mechanism of a platform’s influence on stakeholders’ ability to forecast demand in tourism. Results showed that the variance of stakeholders’ demand predictions with a platform was smaller than the variance without a platform, which meant that a platform would improve predictions of demand for stakeholders. The higher information-processing ability of the platform also had other effects on demand forecasting. Research on the inner logic of the platform’s influence on stakeholders has important theoretical and realistic value. This area is worthy of further study.

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