Tata Motors Research Articles

A Study on Stock Analysis of Automobile Industries

This study investigates the stock movement of automobile companies listed on the NIFTY50 index, focusing on five major firms: TVS Motor, MRF, Ashok Leyland, Maruti Suzuki, and Tata Motors. Using quarterly and annual data from January 2021 to December 2021, the analysis identifies key trends and volatility factors impacting stock prices in the automotive sector. Key findings suggest that fluctuations in stock prices are influenced by quarterly performance, industry trends, and macroeconomic conditions. Additionally, the report highlights the impact of the COVID-19 pandemic on the sector, with companies facing challenges such as reduced demand, increased costs, and supply chain disruptions. The study concludes that stock movements in this industry are volatile, urging investors to monitor these fluctuations closely before making investment decisions.

Financial Ratio Analysis of Tata Motors Limited

This report presents a comprehensive financial ratio analysis of Tata Motors Limited, focusing on its performance in the automobile industry. The analysis aims to evaluate the company's financial health and operational efficiency through various financial ratios. The findings are based on data collected during a summer internship at Tata Motors, providing insights into the company's strengths and weaknesses in the competitive market.

ACCELERATING MARKET DYNAMICS: THE IMPACT OF GST ON STOCK PRICES AND PROFITABILITY OF LEADING AUTOMOBILE COMPANIES

In India, GST introduced in July 2017 was a major event for the Economy. It subsequently impacted all the business sectors and companies operating within the economy. In such cases, it is important to gauge the impact of this new taxation system to analyze the outcome. This research explores the effect of GST on the net profits and stock prices of Maruti Suzuki, Tata Motors, Mahindra & Mahindra, Honda, Ashok Leyland, Bajaj Auto, TVS Motors, Toyota, Hero Motor Corp, and Ford Motors. For this purpose, pre-GST and post-GST analysis was conducted through a paired t-test using SPSS for 2 variables i.e., net profits and stock prices. I have gathered data from the NSE and financial reports of the companies for a comparable period of 7 years before and 7 years after the GST implementation It was found that there is no significant effect on the net profits of the selected companies. The trends in stock prices of companies, except TVS Motors seemed to be unaffected due to the introduction of GST.

Strategic Market Assessment for International Market Entry: A Comparative Analysis of Market Selection Criteria and Entry

This paper explores the strategic market assessment for international market entry, focusing on market selection criteria and entry strategies. It examines the factors influencing market selection, including economic stability, market size, competitive landscape, cultural compatibility, and regulatory environments. The study compares two primary international market entry strategies: alliances and acquisitions. It analyzes the benefits and challenges of each approach, particularly in managing risks and leveraging local market knowledge. The research also highlights the role of partnerships in emerging markets, where local expertise can be vital for successful market entry. Through case studies of companies such as Starbucks in China and Tata Motors' acquisition of Jaguar Land Rover, the paper illustrates how strategic market entry decisions can lead to sustainable competitive advantages. This comparative analysis offers insights for business leaders and policymakers aiming to expand into global markets.

Harnessing AI and Predictive Analytics: Transforming the Electric Vehicle Market in India

The electric vehicle (EV) market in India is undergoing a transformative phase, driven by growing environmental concerns, government incentives, and the rapid integration of advanced technologies such as Artificial Intelligence (AI) and Predictive Analytics. These technologies are proving essential in optimizing various aspects of EV development, including battery management, predictive maintenance, and customer experience personalization. This research article investigates the influence of AI and Predictive Analytics on the Indian EV market, highlighting their roles in improving operational efficiency, reducing costs, and enhancing safety and user satisfaction. Statistical data and case studies of leading Indian EV companies, such as Tata Motors, Ola Electric, and Ather Energy, illustrate the impact of these technologies on market growth and performance. Despite facing challenges like high initial costs and regulatory barriers, AI and Predictive Analytics are poised to play a pivotal role in the future expansion of the EV market in India. The findings suggest that with increased adoption and ongoing technological advancements, AI and Predictive Analytics will significantly contribute to the growth and sustainability of the EV sector in India. Keywords: Electric Vehicles (EVs), Artificial Intelligence (AI), Predictive Analytics, Battery Management Systems (BMS), Predictive Maintenance

Why Tata Nano Failed? From Ascent to Ashes: Unraveling the Tata Nano Saga – A Chronicle of Triumph, Tragedy, and Missteps

The Tata Nano launched in 2008 was initially marketed as the world's cheapest car, designed to provide an affordable transportation solution for the masses in India. It was a revolutionary concept that aimed to bring mobility to a wider segment of the population. Ratan Tata, the former Chairman of Tata Sons, envisioned the Nano as a symbol of innovation and social impact. Under his leadership, Tata Motors endeavored to create a compact, economical car that could transform the way people in India commuted. The global ambition of the Nano was to showcase India's engineering capabilities and demonstrate how a low-cost, frugal design approach could result in a groundbreaking product. Ratan Tata's vision extended beyond the domestic market, aiming to position the Nano as a viable option for consumers worldwide. However, despite the initial hype and anticipation surrounding the Nano, various factors contributed to its failure in the Indian market. Issues such as quality concerns, safety perceptions, marketing challenges, and changing consumer preferences played a significant role in the Nano's inability to meet sales expectations and establish a sustainable presence in the automotive industry. Ratan Tata made a roapmap to sell Minimum of 3 lacs of Tata Nano car in the first Years, but the reality is Tata Nano couldn’t cross the selling of 3 lacs units even after 8 years, before its get retired. Keywords: Anticipation, Envisioned, Groundbreaking, Innovation, Revolutionary

Critically Examining the Role of CSR Initiatives by Corporations in the Economic Independence of Women in Rural Areas

The study aims to critically examine the role of Corporate Social Responsibility (CSR) initiatives in fostering the economic independence of women in rural areas. The research assesses the impact, effectiveness, and challenges associated with these initiatives through an extensive review of existing literature and an analysis of CSR practices adopted by five Indian corporations. Exploratory methodology is employed based on secondary data collected from various reputable sources such as online journals, articles, and official websites. Focusing on the societal responsibilities of businesses, the study delves into the specific CSR measures undertaken by corporations to empower women economically in rural India. Detailed case studies of initiatives by Hindustan Unilever Limited, ITC, Godrej and Boyce, Tata Motors, and HDFC Bank provide insights into the programs implemented, their reach, and their impact. These initiatives encompass diverse strategies such as skill development, self-help groups, mentorship, employment opportunities, micro-credit facilitation, and technological literacy to enhance women’s economic empowerment. This research also identifies and discusses various challenges hindering their effective implementation besides acknowledging the positive impact of these CSR initiatives. Traditional gender norms, lack of cultural understanding, inadequate engagement, and inefficient evaluation mechanisms are among the identified barriers that impede the success of these initiatives. The findings underscore the importance of comprehensive strategies tailored to local contexts and suggest closer collaboration between corporations, NGOs, government agencies, and the community to ensure the success of CSR programs. The study concludes by emphasizing the significance of CSR in advancing women’s economic independence, breaking gender stereotypes, and fostering a sustainable future. The study put forth aims to address the identified challenges and enhance the efficacy of CSR initiatives targeting rural women, contributing to their empowerment and socio-economic development.

Implication of Different Data Split Ratios on the Performance of Models in Price Prediction of Used Vehicles Using Regression Analysis

Introduction; Artificial intelligence (AI) and Machine Learning have become buzzwords lately due to technological changes and data quality testing, especially in shape and finish analysis. Lots of research has been conducted for linear regression algorithms to predict the price in different sectors for share stock, rental properties, prices of used cars etc. This study provides suitable data split ratio for optimum cost estimation based on linear regression model. In present days there is an increasing demand for having own car for every middle class family therefore this have given opportunity to motor vehicle business to offer wide range of used vehicle for re-sale especially companies like Maruti Suzuki, Tata motors & Mahendra motors in Indian motor vehicle industries. Therefore, it is important to know the current value of your car before spending your hard-earned money on any item. Objective; The objective of this paper is finding appropriate value of cars in Metropolitans or even in state capitals. Features like model, mileage, AC, seating capacities, fuel type automatic will be taken into account when doing this. This estimate is designed to help customers find the right options to suit their needs. Methods; We have used a linear regression model to estimate the value of the respective car. Results; For doing this price prediction in this paper using liner regression we have tried to find the optimum accuracy of model by varying data split ratio for training and test data set and concluded with the result that 80/20 ratio is the best ratio with optimum model accuracy for business domain analysis with labelled data set. Conclusion; The findings underscore the importance of careful consideration when selecting a data split ratio for price prediction models in the used vehicle market. The insights gleaned from this study can inform future research and contribute to the development of more accurate and reliable regression models in similar domains.

Recruitment And Selection-With Reference to TATA Motors

Recruitment And Selection-With Reference to TATA Motors

IMPACT OF GREEN INNOVATION (GI) PERFORMANCE ON COMPETITIVE ADVANTAGE OF FIRMS IN INDIA

Green Innovation (GI) is increasingly recognized as a critical factor in enhancing the competitive advantage of firms globally, and this trend is evident in India as well. This article explores how the performance of Green Innovation contributes to a firm’s competitive edge in the Indian context. Key drivers such as regulatory compliance, market demand, cost-reduction opportunities, and corporate social responsibility (CSR) mandates are examined. The article highlights various GI performance metrics, including resource efficiency and green product development, and discusses how these can translate into competitive advantages such as brand reputation, differentiation, cost leadership, and access to new markets. Case studies of Indian companies like Tata Motors, ITC Limited, and Mahindra & Mahindra demonstrate the real-world impact of GI on business success. Finally, the challenges that firms face in implementing Green Innovation, including high initial costs and supply chain constraints, are considered.

A Study on Impact of Intangible Resources on a Company Profitability with Reference of Tata Motors

A Study on Impact of Intangible Resources on a Company Profitability with Reference of Tata Motors

A Study on Human Resource Audit with Reference to Tata Motors Limited

A Study on Human Resource Audit with Reference to Tata Motors Limited

Financial Strategies of Attracting and Retaining Talent at Tata Motors

For businesses like Tata Motors, talent has become the most precious asset in today's intensely competitive automotive sector. This abstract explores the financial tactics used by Tata Motors to draw in and hold on to elite personnel, which in turn ensures long-term success and growth for the company. Tata Motors understands how important it is to provide competitive pay packages in order to draw in the top personnel available. The company maintains its wage structures in line with industry norms by carefully examining the market and benchmarking. Tata Motors also uses performance-linked incentives, which match organizational and individual objectives to reward excellence and produce outcomes. Tata Motors aims to establish a financially satisfying work environment that inspires employees to achieve by combining competitive compensation with performance bonuses. Apart from monetary compensation, Tata Motors prioritizes offering extensive benefits to its employees. These benefits include retirement plans, health insurance, and a host of additional advantages aimed at improving the general happiness and well-being of its employees. Tata Motors promotes a culture of dedication and loyalty among its workers by putting employee wellbeing first, which increases retention rates. In addition, Tata Motors makes large investments in learning and development programs to cultivate talent and promote career advancement. Through educational opportunities, skill-building workshops, and training programs, the organization provides its employees with the tools and information they need to succeed in their positions. Tata Motors fosters a culture of innovation and adaptation in addition to improving individual performance by enabling staff members to pursue ongoing professional development.

Comparative Analysis of Stock Price Prediction Accuracy: A Machine Learning Approach with ARIMA, LSTM, And Random Forest Models

This research investigates the comparative effectiveness of three distinct predictive models – ARIMA (Auto Regressive Integrated Moving Average), LSTM (Long Short-Term Memory), and Random Forest – in forecasting stock prices. Focusing on Tata Motors and Infosys stocks, historical data spanning a significant timeframe is collected using the finance library. These models are trained on a diverse set of features including open, close, high, and low prices to capture the underlying market dynamics. The evaluation of model performance is centred on their ability to forecast stock prices over varying prediction horizons. Metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) are utilized to quantify the accuracy and reliability of the predictions. Through rigorous analysis, this study provides insights into the strengths and limitations of each model, offering valuable guidance to investors and market analysts. The findings underscore the significance of selecting appropriate predictive models in financial forecasting and contribute to advancing the understanding of predictive modelling techniques in stock market analysis. Additionally, the research delves into the implications of long-term dependencies in stock price forecasting, shedding light on the challenges and opportunities inherent in predicting market trends over extended periods.

Deep Learning Models for Stock Market Forecasting: A Comprehensive Comparative Analysis

This study presents a comprehensive comparative analysis of deep learning models for stock market forecasting using data from two prominent stock exchanges, the National Stock Exchange (NSE) and the New York Stock Exchange (NYSE). Four deep neural network architectures—Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN)—were trained and tested on NSE data, focusing on Tata Motors in the automobile sector. The analysis included data from sectors such as Automobile, Banking, and IT for NSE and Financial and Petroleum sectors for NYSE. Results revealed that the deep neural network architectures consistently outperformed the traditional linear model, ARIMA, across both exchanges. The Mean Absolute Percentage Error (MAPE) values obtained for forecasting NSE values using ARIMA were notably higher compared to those derived from the neural networks, indicating the superior predictive capabilities of deep learning models. Notably, the CNN architecture demonstrated exceptional performance in capturing nonlinear trends, particularly in recognizing seasonal patterns within the data. Visualizations of predicted stock prices further supported the findings, showcasing the ability of deep learning models to adapt to dynamic market conditions and discern intricate patterns within financial time series data. Challenges encountered by different neural network architectures, such as difficulties in recognizing certain patterns within specific timeframes, were also analyzed, providing insights into the strengths and limitations of each model.

Tata Motors Limited: strategic journey towards electric vehicle

Learning outcomes The case study has been prepared for management students/business executives to understand electric vehicle (EV) business, business environment, industry competition and strategic planning and strategy implementation. Case overview/synopsis The size of the Indian passenger vehicle market was valued at US$32.70bn in 2021; it was projected to touch US$54.84bn by 2027 with a Compound Annual Growth Rate (CAGR) of more than 9% during the period 2022–2027. The passenger vehicle industry, a part of the overall automotive industry, was expected to grow at a rapid pace, as the Indian economy was rising at the fastest rate. However, the Government of India (GoI) had put a condition on the growth scenario by mandating that 100% of vehicles produced would be EVs by 2030. Tata Motors (TaMo), a domestic player in the market, had been facing a challenging competitive environment. Although it had been incurring losses, it had successfully ventured into the EV business. TaMo had taken advantage of the first mover by creating an electric mobility business vertical to enable the company to deliver on its aspiration of providing innovative and competitive e-mobility solutions. TaMo leadership had been putting efforts to scale up the electric mobility business, thus, contributing to GoI’s plan for electric mobility. Shailesh Chandra, president of electric mobility business, had a big task in hand. He had to scale up EV production and sales despite insufficient infrastructure for charging and shortages of electronic components for manufacturing. Complexity academic level The case study has been prepared for management students/business executives for strategic management class. It is recommended that the case study is distributed in advance so that the students can prepare well in advance for classroom discussions. Groups will be created to delve into details for a specific question. While one group will make their presentation, the other groups will question the solution provided and give suggestions. Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.

A Comparative Study of Financial Statements of Tata Motors and Mahindra & Mahindra Ltd. Company for the financial years 2021-22 and 2022-23

India is the world’s third largest car market. Indian Auto sector is still far behind other Asian countries. Prices of vehicles in India continued to rise due to rise in the cost of inputs. Home grown brands of cars in India have performed very well in Indian markets. If such brands have to remain competitive in market they have to focus on electric vehicles. Tata Motors and Mahindra & Mahindra Ltd. is working hard to capture electric vehicle market in India. Tata Company has incurred a loss in the year 2021-22 but made profit in the year 2022-23 but Mahindra & Mahindra is a profit making company in both the years. Liquidity condition of Mahindra & Mahindra is also better than Tata Motors. It would be preferable to buy Mahindra & Mahindra Shares in long as well as short run but it would be preferred that Tata Motors shares could be bought for the long run. Keywords: Auto sector, financial analysis, accounting ratios, Quantitative data, Future prospects

Fundamental and Financial Performance Analysis of Selected Automobile Companies: A Comparative Study on Tata Motors, Ashok Leyland, Olectra Greentech, Force Motors and SML Isuzu

Fundamental and Financial Performance Analysis of Selected Automobile Companies: A Comparative Study on Tata Motors, Ashok Leyland, Olectra Greentech, Force Motors and SML Isuzu

Tesla: route to India, route in India

Research methodology The case has been developed by using secondary sources of information. Case overview/synopsis Tesla’s much-awaited foray into the burgeoning Indian electric vehicle (EV) marketplace had hit the “high import tariff” roadblock. Discussions ensued and finally, Elon Musk, the CEO of Tesla and the Indian Government found common ground. The moot point of Tesla’s entry mode was resolved. Musk announced Tesla’s plan to set up an EV supply chain and manufacturing facility in the host country. This case discusses factors affecting location decision, market entry modes and international corporate-level strategies. Tata Motors sold affordable cars and was miles ahead in the EV race in India. Musk had to align Tesla’s India strategy with the company’s global strategy to woo the price-sensitive Indian consumers. What were the options available to him? This case examines different business-level strategic options that could help Tesla drive in the fast lane in India. Complexity academic level The case can be used in international strategy course at graduate level. It can also be used in a session on international marketing in marketing management course.

Study the fluctuation of share prices of Maruti-Suzuki and Tata Motors during Indian festival season

Study the fluctuation of share prices of Maruti-Suzuki and Tata Motors during Indian festival season