Designing cost-effective, AI-powered demand forecasting models for the manufacturing sector

5^th International Congress on AI and Machine Learning

December 09-10, 2024 | Dubai, UAE

Haroon Rashid

CANPACK US LLC, USA

Scientific Tracks Abstracts: J Comput Sci Syst Biol

Abstract :

Accurate demand forecasting is crucial for efficient manufacturing operations, directly influencing inventory management, production scheduling, and cost control. Traditional forecasting methods often fall short in capturing the complexities of modern market dynamics. This study explores the application of cost-effective, AI-powered demand forecasting models to transform manufacturing processes by providing real-time, data-driven insights. Leveraging machine learning algorithms, these models analyze historical sales data, market trends, and external factors such as seasonality, economic shifts, and consumer behavior. AI-based solutions have demonstrated substantial success in real-world scenarios. For instance, a global food manufacturer improved demand forecast accuracy by 8% and identified an additional $30 million in gross margin through increased order fill rates after implementing AI-driven forecasting tools. Similarly, a consumer-goods manufacturer achieved significant annual savings by reducing forecasting errors, with each percentage point reduction potentially saving at least $10,000 monthly in inventory costs. Affordable AI tools, such as Microsoft's Azure AI platform and Google's Vertex AI, offer scalable solutions, making such advancements accessible even to mid-sized manufacturers. This research delves into designing cost-effective AI models specifically for manufacturing, emphasizing how these technologies optimize resources, reduce waste, and enhance customer satisfaction. A comparative analysis of traditional methods versus AI-powered approaches highlights a 20–50% increase in forecast accuracy with AI, leading to substantial savings in storage and production costs. The study also examines challenges, including data quality issues and the initial investment required, while proposing mitigation strategies such as phased implementation and leveraging open-source AI tools.

Biography :

Haroon Rashid, is a distinguished expert in supply chain management and logistics, with over two decades of professional experience in diverse manufacturing and trading environments. Serving as a Regional Purchasing Manager at CANPACK USA, Haroon has been instrumental in implementing strategic procurement and logistics solutions that drive efficiency, reduce costs, and enhance operational resilience. His areas of expertise include global logistics management, inventory optimization, and sustainable procurement practices, underpinned by a commitment to continuous improvement and ethical business standards. Haroon's academic credentials are equally impressive, holding dual MBAs in Global Business Management and Operations Management, alongside a Global Master Certificate in Integrated Supply Chain Management and CILT from APICS. He is an active member of several professional bodies, including APICS, and has received numerous awards for his contributions to supply chain excellence. His research and professional pursuits focus on advancing innovative supply chain methodologies, making his insights particularly valuable in addressing contemporary industry challenges.