Browsing by Author "Dr. Syed M Irfan"

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    Harnessing Artificial Intelligence for Enhanced Logistics and Warehouse Efficiency
    (library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Faizan CIIT/SP24-RBA-023/LHR; Dr. Syed M Irfan; LHR TP 9817
    The report examines how AI is transforming retail logistics and warehousing via a case study done on two leading retail chains of Pakistan, Imtiaz Super Market and Al-Fatah. With the onslaught of competition in retailing and so many customers’ demands to meet today, AI is becoming a game changer for retail where operational efficiency and cost savings are hallmarks and customer experience is king. The report evaluates two main types of AI, Generative AI, used for predictions, layout design and strategic planning and Assignable AI, which makes it possible to automate tasks, carry out inventory management and monitor logistics in real time. Both Imtiaz and Al-Fatah have made strides in investing in digital tools such as ERP systems and e- commerce platforms, but their AI maturity level differs. Imtiaz has begun using AI for demand forecasting, inventory alerts and rudimentary customer service chatbots, but much of its logistics and warehousing operations are done manually. Al-Fatah, on the other hand, has ventured into centralized inventory, warehouse automation is restrained, and digital coordination is more structured, therefore it is slightly more prepared in AI readiness. This report highlights several common challenges faced by both companies, which include the absence of real-time data infrastructure, expensive AI deployment, limited in-house expertise and the scarcity of localized AI tools that capture Pakistan’s market landscape. But it also looks at many green-field opportunities like smart warehouse lay outing, AI powered delivery route optimization, predictive inventory management, or smarter customer service through conversational chatbots. By analyzing extensively in parallel, the report offers actionable insights and a roadmap to progress to the next level of AI for both organizations. It suggests a strategic adoption of AI in phases, beginning with the use of low-cost tools like machine learning for predictive forecasting before moving onto automated warehousing and logistics management systems. There is also a focus on developing in-house AI capabilities, investing in the training of staff, and entering partnership with local technology companies.
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    Predictive Analytics for Reverse Logistics: Forecasting Return Volumes in E-Commerce
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Malaika Fareed; CIIT/FA24-RBA-032/LHR; Dr. Syed M Irfan; LHR TP 10107
    The returns increase the logistics, warehousing, and customer service strain besides increasing the operating costs. It explores the potential of prediction analytics to improve the efficiency of reverse logistics, based on the case Study of Call Courier, in the Pakistani e-commerce industry. The project aims at knowing and predicting the trends of the product returns in order to aid in enhanced operation planning, cost control and customer satisfaction. First Chapter indicates high rate of expansion of the e-commerce and the ensuing problems to deal with products returns. The role of the reverse logistics, which deals with the processing of the returned goods, is now the very important part of the supply chain management because the returns rates directly influence the profitability and the use of resources. The issue this research will seek to solve is the absence of proper forecasting models that could forecast product returns and their business impacts on the logistics companies such as Call Courier. Quantitative research methodology is adopted, which is based on a ten-year synthetic dataset of the shipment operations of Call Courier. The variables in the data were the shipment identifiers, geographic characteristics, cash-on-delivery (COD) behavior, cost measures, and the return flags. The dataset was subjected to data preprocessing and feature engineering to clean, standardize the dataset and add time-based and distance-based features. The descriptive analytics indicated that the rates of returns were more in non-metro and remote locations, which was attributed primarily to failures in delivering and COD non-collection but in metro regions, there was higher success in delivery.