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

New product launches face extreme uncertainty; many forecasting systems still rely on historical-demand. This research shows how AI-driven approaches can better support early-stage planning.”

— Dileep Kumar Rai

COLORADO SPRINGS, CO, UNITED STATES, January 26, 2026 /EINPresswire.com/ – Dileep Rai, a supply chain and AI transformation leader, has 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 Lecture Notes in Networks and Systems (LNNS 1723) series.

The research focuses on one of the most persistent challenges in supply chain and business planning: forecasting demand for new product launches (NPIs), where little to 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 during the early product life cycle.

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

“New product launches operate under extreme uncertainty, yet many organizations continue to apply forecasting methods designed for steady-state demand,” said Dileep Rai, author of the study. “This research outlines why those models fall short and how AI-driven, probabilistic, and scenario-based forecasting approaches can better support early decision-making.”

Key themes explored in the paper include:

Limitations of historical-data-driven forecasting for new products

Gaps between business strategy, data availability, and planning systems

The role of AI, machine learning, and external signals in improving early-stage forecasts

Practical recommendations for combining human judgment with advanced analytics during NPIs

The paper was accepted through a rigorous peer-review process as part of 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-enabled demand forecasting, digital supply chains, and enterprise-scale analytics, with real-world applications across industries such as publishing, healthcare, aerospace, and manufacturing.

The full chapter is available through 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 delivering large-scale cloud ERP, advanced analytics, and forecasting solutions. 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 +1 425-306-4131 email us here

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AI Demand Forecasting Research on New Product Launches Published by Springer Nature

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