Browsing by Author "Muhammad Faizan CIIT/SP24-RBA-023/LHR"

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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.