Forecasting on Harshvardhan

Enterprise-Scale Machine Learning for Demand Forecasting

Sat, 18 Oct 2025 00:00:00 +0000

Our HP Inc. forecasting framework was featured in Foresight: The International Journal of Applied Forecasting (Issue 79, 2025) and recognized as a finalist at the International Institute of Forecasting’s Foresight Conference. 🔗 PDF

Print Demand Forecasting with Machine Learning at HP Inc.

Wed, 04 Jun 2025 00:00:00 +0000

HP Inc. replaced manual and statistical forecasting with a machine learning (LightGBM) model to improve demand prediction accuracy across 18,000+ print products. The model has been deployed enterprise-wide, with demonstrated business value and principles for scaling ML in large organizations. 🔗 PDF

From Data to Decisions: Enterprise Demand Forecasting with Machine Learning

Sat, 31 May 2025 00:00:00 +0000

My Ph.D. dissertation (University of Tennessee, 2025) develops a machine-learning-driven demand forecasting framework implemented at HP Inc., improving forecast accuracy by 34% and reducing inventory by 28%. 🔗 PDF

From Data to Decisions: The Story Behind My PhD

Tue, 22 Apr 2025 00:00:00 +0000

On March 31, 2025, I successfully defended my dissertation: “From Data to Decisions: Enterprise Demand Forecasting with Machine Learning.” My work is rooted in generalizable research at the intersection of machine learning, operations research, and organizational decision-making, grounded through a real-world implementation at HP Inc.

Final accepted draft of my dissertation is available here.

What is my dissertation about?

Demand forecasting has a rich intellectual and practical history. Ancient texts like the Indian Arthashastra (350 BCE) and Chinese Han Dynasty archives both emphasized blending qualitative judgment with quantitative grain records to estimate demand. Fast forward to the industrial age, companies like Ford and Chrysler pioneered judgmental forecasting to support assembly lines. In the 1960s, statisticians like Box, Jenkins, Holt, and Winters developed foundational time-series methods like ARIMA and exponential smoothing, which still serve as industry baselines.

Artificial Intelligence and Data Sciences in Real-world Business

Thu, 07 Sep 2023 13:00:00 +0000

A quick rundown of my doctoral research presented at the BA Forum 2023

HP Internship: A Year and a Half in the Fast Lane

Mon, 21 Aug 2023 00:00:00 +0000

During my 15-month internship at HP Inc., I dove into machine learning forecasting, tackling challenges from SKU-level predictions to data management. Collaborating with the SPaM team, utilizing innovative tools, and embracing HP’s culture of innovation and failure, I emerged with invaluable skills, insights, and memories.

ML Forecasting at HP Inc.

Mon, 21 Aug 2023 00:00:00 +0000

Creating ML demand forecast for print products at HP Inc. using LightGBM and pushing it to production for wide adoption.

Supply Chain Analytics at HP Inc.

Mon, 31 Oct 2022 00:00:00 +0000

Forecasting Global Print Demand Using Machine Learning