Journal of Advanced Research in Production and Industrial Engineering

Machine Learning Algorithm for Predicting Sales of Cardigans In Ludhiana District

Arti Lakhanpal Malhotra — 2026-01-22

Machine learning [ML] has become a transformative tool in business analytics, enabling firms to extract valuable insights, optimise operations, and enhance decision-making. Businesses widely use ML for supply chain management, fraud detection, customer segmentation, and sales forecasting. However, challenges such as poor data quality, high processing costs, integration constraints, and the need for skilled professionals often hinder its implementation. Addressing these challenges is crucial to unlocking the full potential of ML and driving efficiency, profitability, and innovation. This study applies ML techniques to forecast sales of jackets and blazers at DGN Clothing, Ludhiana, a major apparel manufacturer. By analysing historical sales data, seasonal trends, and pricing variations, this research explores how ML can help businesses optimise inventory, reduce losses, and improve profitability. The study evaluates Random Forest Regression to determine the most effective model for predicting sales trends. Performance metrics such as Mean Absolute Error (MAE), R-Squared (R²), and Root Mean Squared Error (RMSE) are used to assess accuracy. The results offer actionable insights into demand fluctuations, pricing strategies, and consumer purchasing behavior, allowing businesses to make data-driven decisions. By leveraging MLbased sales forecasting, retailers can enhance stock management, minimise overproduction, and boost revenue generation. This research underscores the importance of ML in modern business analytics, demonstrating its ability to streamline processes and provide a competitive edge in the evolving apparel industry.

Event Participation and Performance analysis Prediction

Nisha Arora — 2026-01-22

This study explores trends in participation and performance at PCTE Group of Institutes, Ludhiana, during the “Koshish 2024 Junior” cultural festival. By utilising machine learning methods, we assess student involvement in a variety of cultural activities to pinpoint the top-performing participants and comprehend the factors that influence student engagement. The research uses data gathered from the “Koshish 2024 Junior” festival, including participation logs from numerous events such as Solo Dance, Quiz, Debate, Photography, Rangoli, Group Dance, and others. We implement machine learning techniques, featuring classification models for performance evaluation and regression models for forecasting participation, to examine the connection between event type and overall performance. Additionally, this study investigates how machine learning methods can be utilised to gain insights into participation trends. Recognising these trends is essential for efficient event planning, resource management, and boosting engagement. We review historical participation data to uncover patterns and trends, applying predictive modelling to forecast performance levels. This methodology allows event organisers to make more informed decisions, aiding them in optimising schedules, allocating resources, and enhancing participant outreach strategies. Through this research, we seek to improve the accuracy of predictions related to cultural event participation, ultimately enriching the experience for both students and event organisers.

DOI: https://doi.org/10.24321/2456.429X.202502

Machine Learning-Based Sales Forecasting: Optimizing Product Demand Prediction

Pratiksha — 2026-04-27

Machine learning [ML] has become a transformative tool in business analytics, enabling firms to extract valuable insights, optimise oper ations, and enhance decision-making. Businesses widely use ML for supply chain management, fraud detection, customer segmentation, and sales forecasting. However, challenges such as poor data qual ity, high processing costs, integration constraints, and the need for skilled professionals often hinder its implementation. Addressing these challenges is crucial to unlocking the full potential of ML and driving efficiency, profitability, and innovation. This study applies ML techniques to forecast sales of jackets and blazers at DGN Clothing, Ludhiana, a major apparel manufacturer. By analysing historical sales data, seasonal trends, and pricing variations, this research explores how ML can help businesses optimise inventory, reduce losses, and improve profitability. The study evaluates Random Forest Regression to determine the most effective model for predict ing sales trends. Performance metrics such as Mean Absolute Error (MAE), R-Squared (R²), and Root Mean Squared Error (RMSE) are used to assess accuracy. The results offer actionable insights into demand fluctuations, pricing strategies, and consumer purchasing behavior, allowing businesses to make data-driven decisions. By leveraging MLbased sales forecasting, retailers can enhance stock management, minimise overproduction, and boost revenue generation. This research underscores the impor tance of ML in modern business analytics, demonstrating its ability to streamline processes and provide a competitive edge in the evolving apparel industry.

DOI: https://doi.org/10.24321/2456.429X.202503

Will AI Replace Human Jobs?

Saksham Nanda — 2026-04-27

The rise of Artificial Intelligence (AI) is both a marvel and a menace, a force of evolution and disruption. The question, “Will AI replace human jobs?” is not just about employment; it is about the essence of human ingenuity in a world increasingly dominated by algorithms. “Technology will not replace great teachers, but technology in the hands of great teachers can be transformational.” – George Couros. This quote, though about education, holds a universal truth—AI is a tool, not a replacement. It can automate, optimise, and accelerate tasks, but it lacks the creativity, emotional intelligence, and moral judgment that define human work. Consider the banking sector: AI-powered chatbots handle customer inquiries in seconds, reducing the need for large call centres. But can they replace the warmth of a financial advisor who understands the anxieties of a struggling family? Similarly, self-driving trucks may one day dominate highways, but will AI console a driver who loses their livelihood, or understand the pride of a long-haul journey? “Artificial intelligence is not a threat, rather an opportunity to evolve.” – Sundar Pichai. AI is not a thief of jobs, but a catalyst for change. While routine and repetitive tasks may vanish, new careers will emerge—AI ethics officers, automation supervisors, and creative technologists, among others. History repeats itself. During the Industrial Revolution, machines replaced manual labour, yet new industries flourished. The same will happen with AI. The key is adaptation, reskilling, and embracing the synergy between human intuition and machine precision. So, will AI replace jobs? Yes, but not work. It will replace tasks, but not the human spirit. The future is not AI versus humans—it is AI with humans.