Research on AI demand forecasting on new product launches published by Springer Nature

Distributed by EIN Presswire

A peer-reviewed industry survey highlighting gaps in traditional demand forecasting and the role of AI in new product launch planning.

COLORADO SPRINGS, CO, UNITED STATES, January 26, 2026 /EINPresswire.com/ — Supply chain transformation and AI leader Dileep Rai published a peer-reviewed research paper with Springer Nature titled “Challenges in Demand Forecasting for New Product Launches: An Industry Survey.” The study appears in Data Science and Applications, Volume 3 of Springer’s Networks and Systems Lecture Notes (LNNS 1723).

The research focuses on one of the most persistent supply chain and business planning challenges: forecasting demand for new product launches (NPIs), for which little or no historical data exists. Based on an industry-wide survey across multiple sectors, the paper identifies structural, technological and organizational limitations that reduce forecast accuracy early in the product life cycle.

Traditional forecasting models and ERP-based planning systems are often optimized for mature products with stable demand patterns. The study highlights the difficulties of these approaches in NPI scenarios due to data scarcity, rapid market changes, cross-functional misalignment and limited use of external signals.

“New product launches take place against a backdrop of extreme uncertainty, yet many organizations continue to apply forecasting methods designed for stable demand,” said Dileep Rai, author of the study. “This research explains why these models fail and how AI-based, probabilistic, and scenario-based forecasting approaches can better support early decision-making.” »

Key themes explored in the paper include:

Limitations of forecasts based on historical data for new products

Gaps between business strategy, data availability and planning systems

The role of AI, machine learning and external signals in improving preliminary forecasts

Practical recommendations for combining human judgment with advanced analytics during NPIs

The paper was accepted through a rigorous peer-review process at the 6th International Conference on Data Science and Applications (ICDSA 2025) and selected for publication by Springer Nature, a globally recognized academic publisher. Springer’s LNNS series is widely used by researchers and practitioners and is indexed in major academic databases.

This publication builds on Rai’s broader work in AI-driven demand forecasting, digital supply chains and enterprise-wide analytics, with real-world applications in industries such as publishing, healthcare, aerospace and manufacturing.

The full chapter is available via Springer Nature at:
https://doi.org/10.1007/978-3-032-10783-1_33

About Dileep Rai

Dileep Rai is a supply chain technology and AI transformation professional with extensive experience in delivering cloud ERP solutions, advanced analytics and forecasting at scale. His work focuses on improving demand planning, new product introduction performance, and data-driven decision systems through the application of AI and machine learning.

Ajay Narayan
IEEE
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