Browsing by Author "Zobia Zahid"

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    MAHMOOD TEXTILE MILLS LIMITED
    (Library Information Services, CUI Lahore, 2023) Zobia Zahid; CIIT/SP20-BBA-097/LHR; Ms. Naima Khurshid
    What does the word "intern" really mean? It means working for an organization to gain experience, information and training before going into the job market. Before looking for a job, internship experience is very important and valuable. Employers want to hire people with experience. A summer internship also gives you a chance to get work experience. It gives students the chance to grow intellectually and personally. When someone starts a new job, he will have to do the same boring, repetitive tasks that all new employees have to do. But this isn't always a bad thing because it teaches people the most basic lessons about taking care of themselves. I also had to write a report about what I did during my internship. I went to the Mahmood Group of Companies, which is backed by the most well-known name in Pakistan. Mahmood Group of Companies has been operational since 88 years in Pakistan. It has been in business therefore, my six-week internship at the Mahmood Textile Mill in Muzaffargarh started in July 2023. As an intern, I really enjoyed working there, and whenever I needed help, the people at Mahmood Mill supported me wholeheartedly in all aspects.This report's primary goal is to provide an overview of my first six-week internship in the marketing department of the Mahmood Group of Companies in Muzaffargarh. In a large and quickly expanding multinational corporation like Mahmood Group, six weeks is a very short time frame and it can be difficult to understand every aspect of marketing in that short of time. Nevertheless, I have learned a lot from Mahmood Group, including how to handle small transactions and stay in touch with customers, among other things. I’ve talked about what I've learned throughout my internship, which has focused on textile terminologies and the production process. I have managed to put what I have learned there in writing. This report will be an invaluable asset to my future professional aspirations, as it embodies all of my practical efforts. The Mahmood Group's primary business is yarn production, but they also work in fabric, solar equipment, power generation and other related fields. The Mahmood system, working style, and employee dedication are just exceptional. Mahmood is regarded as one of the top firms in Pakistan since it has consistently shrived for success. Mahmood Limited is a Pakistani firm that produces and markets denim-themed textiles and apparel, ranging from raw cotton to store-bought items. The company writes in addition to spinning, weaving and dying stitches. This report, which represents all of my practical efforts, will be a priceless
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    Predicting Carbon Emissions in Supply Chain Operations Using Regression Forecast Emissions Through Machine Learning
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Zobia Zahid; CIIT/FA24-RBA-048/LHR; Dr. Syed M Irfan Assistant Professor; LHR TP 10110
    Climate change and increasing carbon emissions have become one of the most important issues in the world and the operations of the supply chain, especially transportation, manufacturing, warehousing, packaging and energy consumption are among the significant sources of greenhouse gas emissions. With the growing complexity, globalization and data-intensive nature of supply chains, the previous method of estimating emissions has not served as a sufficient method of predicting future carbon emissions. This MBA project fills this gap by offering a machine-learning-based regression forecasting system to seek carbon emission in supply chain activities and thus aid in informing data-driven sustainability decision-making. The main aim of the proposed study is to create and test machine learning regression models that can be used to predict carbon dioxide (CO2) emissions using main supply chain functions effectively. The study is aimed at discovering the most important operation drivers of emissions, evaluating the predictive power of machine learning models compared to the deterministic models and showing how predictive analytics can aid in sustainable logistics planning and environmental regulations. The proposed study is based on the general framework of the global sustainability programs, ISO 14001:2015 environmental management standards and the changing climate and environment policies in Pakistan especially in the Punjab. The study has a quantitative and predictive approach methodologically with a hybrid modeling framework comprising of regression analysis and machine learning and time-series predictive methods. The data of the carbon emissions are further broken into the long run trend and the short-run residual data. Structural pattern of emission is captured using parametric and non-parametric regression models whereas short-term fluctuations are captured by using linear and nonlinear time-series models, such as autoregressive and neural-network-based models. Ensemble machine learning models like the Random Forest are introduced in order to deal with the nonlinearity and enhance predictability. The analysis is performed with Python and the performance was measured with the standard error indicators like MAE, RMSE and MAPE.