NOLEGEIN-Journal of Operations Research & Management <p><strong>NOLEGEIN-Journal of Operations Research &amp; Management </strong>is a peer reviewed journal and provides a platform to discuss new issues in the area of Decision theory and Operations models . The journal also seeks to advance the quality of research by publishing papers introducing or elaborating on Applications of operations research and Manufacturing &amp; operations theory. It's a biannual journal, started in 2018.</p> en-US (Journal Manager) (Admin) Sat, 22 Jan 2022 12:42:56 +0000 OJS 60 Validation of n-variable Linear Regression Model <p>Purpose: The researcher generalizes regression of three variables to n-variable by induction method <br>with the help of cofactor of a matrix so that it assist to the predictor and estimator to estimate and <br>predict dependent variable with the help of independent variables as applied in the relationship <br>between dependent variable GDP and independent variables agriculture, industry, and service sector <br>by using the n-variable regression model at initial condition. This seems a highly applied tool. So, its <br>verification for validity is most and it is attemped here. Design/methodology/approach: To verify the <br>n-variable linear model, both the methods are applied for the same data and estimated the best fit of <br>the line and checked after applying the correlation coefficient matrix method and multiple regression <br>equation using deviations from mean, zero order correlation coefficients, and standard deviation, do <br>the fluctuation existed in the line or not, is estimated. Findings/result: The estimation of dependent <br>variable with the help of independents variables can be estimated through the regression model found <br>to be valid. Originality/value: It is pure research and a useful tool for estimation for multiple <br>variables has been developed and validated.</p> Anjay Kumar Mishra, Krishna Kumar Chaudhary Copyright (c) 2022 NOLEGEIN-Journal of Operations Research & Management Mon, 24 Jan 2022 00:00:00 +0000 Impact of COVID-19 on Social Media Marketing in Tamil Nadu <p>The present research paper is focusing on quantifying the impact of COVID-19 on social media<br>marketing in Tamil Nadu which raises the COVID-19 pandemic has accelerated the adaptation of<br>social media marketing. The primary data on the field are collected through questionnaires and<br>secondary data were collected through internet and journals. A sample of 117 respondents is<br>collected from social media users, out of which, most of the respondents are of 21–30 age group. It<br>was founded that there is an increase in social media usage during lockdown. Thus, the study<br>concludes that there is a high increase of purchasing through social media during COVID-19<br>pandemic in Tamil Nadu which was not much before the happening of COVID-19 pandemic.</p> Joel Jebadurai, Ramya E. Copyright (c) 2022 NOLEGEIN-Journal of Operations Research & Management Sun, 23 Jan 2022 00:00:00 +0000 Operations Research Management of Logistics and Supply Chain: Its Issues and Directions of Scope <p>To improve business performance, companies use a variety of business improvement strategies.<br>Logistics and supply chain management have long been viewed as critical factors in a company’s<br>ability to gain a competitive advantage. In fact, since the early 1980s, logistics and supply chain<br>management have gotten a lot of attention. However, supply chain management is a difficult topic to<br>grasp, and many authors have emphasised the significance of explicit definitional constructions and<br>conceptual frameworks in supply chain management. This paper includes a tutorial on current<br>research in logistics and supply chain operations management. The scope of our related research<br>articles is outlined in this paper by first explaining the concept of logistics and supply chain<br>management. The primary goal of this paper is to present a variety of current subjects in this field as<br>well as instances of how these studies contribute from distinct research perspectives.</p> Abhijeet Sinha, Ankita Sharma Copyright (c) 2022 NOLEGEIN-Journal of Operations Research & Management Sun, 23 Jan 2022 00:00:00 +0000 Forecasting Stock Market Returns using Artificial Neural Networks: Novel approach <p>In recent years, there has been an increase in the amount of literature on artificial neural network applications in the business and finance realms. In reality, the field of stock return forecasting has received a lot of attention. This is owing to the fact that monetary benefits will be large if artificial neural network applications succeed. Many research have shown that various types of artificial neural network topologies can be successfully used to predict stock returns. This study examines and evaluates various neural network research approaches that have been utilised to forecast stock returns in various journal papers. Modeling methodologies and literature suggestions are also collated and discussed. Artificial neural networks are an emerging and promising computational technique that will continue to be a difficult tool for future research, according to the findings.</p> <p>&nbsp;</p> <p>Neural Network and Convolutional Neural Network (<strong>Artificial Intelligence)</strong> is used for detection of Forecasting Stock Market Return. It is observed through empirical experiments that the selected input variables were effective to predict stock market returns. The forecasting stock market is used by Neural Network and the Convolutional Neural Network to detect the return percentile. In this research we will describe the prediction of stock return by performing Artificial Intelligence on Jupiter simulation tool by using Implement the neural network. This research work is proposed for for casting and prediction of stock market return on the basis of artificial intelligence based data set and values for used as prediction value.<strong>&nbsp; </strong></p> <p>&nbsp;</p> <p>&nbsp;We are executing the research on the basis of last year’s data set of stock market returns both positive and negative so that we will apply the prediction model using artificial intelligence techniques and generate for casting report of stock market retunes for all particular client those who want to investment.</p> Trapti Tak, Manish Sharma Copyright (c) 2022 NOLEGEIN-Journal of Operations Research & Management Sat, 22 Jan 2022 00:00:00 +0000 A Study on Impact of Working Capital—With Reference to Tata Motors Pvt. Ltd. <p>The organisation's operating income reveals more about the financial position of the business than other assumptions. It mentions to you what can be left over if an organization raises all its temporary assets, and uses themto care for its temporary liabilities. The data needed for the study are based on secondary data. The required information is collected from Tata Motors LTD's annual reports which include balance sheets, company P&amp;L accounts from 2016-20. An active capital study is used to evaluate long-term and short-term resources a company tapes to meet its financial needs. The scope of the study is limited to the sources of Tata vehicles. (Formerly known as Tata motors Motors Ltd.) taken over the years under study i.e. 2016-20. Tata motors Net Profit Ratio is showing negative profits for all years except 2015-2016. Events are expected because from the past two years it shows the decline of the Net Profit Ratio. Tata motors Gross Profit Margin for Tata motors is rising sharply due to increased sales. Marine profits for Tata motors are declining and show a negative profit because there is an increase in the price of copper.</p> <p>&nbsp;</p> M. Bhumika, Khudsiya Zeshaan Copyright (c) 2021 NOLEGEIN-Journal of Operations Research & Management Sat, 22 Jan 2022 00:00:00 +0000 Formulation of Operational Research and Optimization in an Automobile Engine Assembly Line <p>Depending upon the priorities of the management, primary focus on reducing either cycle time or<br>total energy consumption, suitable models could be selected. The proposed models are useful to<br>reduce the total energy consumption and cycle time in robotic assembly lines. It is observed that the<br>computation time for the time based model is less compared to energy based model. Manufacturing<br>industries give importance to the reduction of energy consumption due to the increase in energy cost<br>and to create an eco-friendly environment. Assembly line is considered to be one of the cost intensive<br>systems. Robots are recently being used to perform the assembly tasks instead of manual labor. There<br>is a requirement of efficiently balancing the assembly line by allocating equal amount of work to<br>workstations and assignment of best fit robot to perform the tasks allocated to those workstations.<br>Waste reduces the efficiency of the process and also demands require special equipment. The<br>industries need to focus on optimization of process by shorten the time between the customer order<br>and the product build due to shipment by addressing sources of waste. Establishment and use of a<br>process that is most efficient is always be the prime concern but due to some or the other reason they<br>are not able to do so. Practically it is not feasible to obtain ideal efficiency because there is something<br>which is lost in every process which is Waste or error. Workstation Stability and Continuous<br>Improvement Mapping is the techniques used for reducing manufacturing wastes and increase in<br>efficiency, for such process several parameters are being identified in an industry also evaluations<br>are done on an every monthly basis and comparisons are followed with the pre-determined standards<br>to identify the scope of improvement to increase the efficiency and reduce waste.</p> Rahul Kumar, Manjeet Bohat Copyright (c) 2021 NOLEGEIN-Journal of Operations Research & Management Mon, 09 Aug 2021 00:00:00 +0000