Nihar Rathod
Shivanshu Rai
- Research Scholar, MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Mumbai, Maharashtra, India
- Research Scholar, MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Mumbai, Maharashtra, India
Abstract
Artificial intelligence machine learning (AIML) can play a significant role in enhancing demand forecasting in business. AIML is a programming language designed for creating chatbots and conversational agents, but its application extends beyond simple interactions. In the context of demand forecasting, AIML can be utilized to analyze historical data, customer interactions, and market trends. By implementing AIML algorithms, businesses can create intelligent models that learn from past demand patterns, customer behaviors, and external factors influencing demand. These models have the capability to evolve and enhance their accuracy with time, delivering more precise and up-to-date predictions of demand. AIML enables the automation of complex data analysis tasks, allowing businesses to process vast amounts of information efficiently. Additionally, AI- and ML-driven platforms can connect with diverse data outlets such as social media, customer input, and economic signals, facilitating the capture of immediate insights. This holistic strategy empowers companies to promptly adapt to shifts in market conditions and make well-informed choices when forecasting demand. In summary, leveraging AIML for demand forecasting enhances accuracy, agility, and responsiveness in business operations, ultimately leading to improved inventory management, customer satisfaction, and overall business performance
Keywords: Forecasting, demand and supply, inventory management, business, machine learning, artificial intelligence
[This article belongs to Journal of Computer Technology & Applications(jocta)]
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Journal of Computer Technology & Applications
| Volume | 15 |
| Issue | 01 |
| Received | February 29, 2024 |
| Accepted | March 20, 2024 |
| Published | April 5, 2024 |