Parimesh Panda

Background

About Parimesh Panda

A Supply Chain aficionado with more than a decade of pragmatic expertise across Healthcare, Aerospace, Renewable Energy, and Consumer Packaged Goods & Retail industries, spanning across new / active products, end of life products, to aftermarket service value streams. Parimesh honed the supply chain core principles across Plan, Source, Make, Deliver and Return through General Electric's Operations Management Leadership Program (OMLP) and Corporate Audit Staff (CAS)- Supply Chain Track Program by practicing Lean Six Sigma practices, and earned his Lean Six Sigma - Black Belt and CPIM certifications. The experience of setting up Integrated Business Planning processes for 5+ large scale organizations, piqued his realization for the need of deep-technology powered supply chain systems for accelerated consensual decision making among CSCO, CFO and Commercial Operations teams. In close quarters with CSCO, CFO and Commercial Operations office bearers, he reliably - quantitatively & qualitatively assesses the end-to-end supply chain risk portfolio by using Generative AI & Agentic AI systems and explainably recommends short / medium / long-term AI roadmap with real AI intervention opportunities and projected ROI realization models. Further, he assists CSCO team members in realizing the bullwhip effect of ever-evolving demographic constraints by modeling the supply chain network design through tools like causal loop diagrams.As a Responsible AI Product Manager for more than half a decade, he has developed & deployed sustainable products leveraging Traditional Machine Learning, Generative AI, and Agentic AI with focus on line-of-balance simulations, rough cut capacity planning, demand sensing, demand forecasting, multi-echelon inventory optimization, heijunka based production planning, last mile delivery network optimization, aftermarket demand for unscheduled / scheduled maintenance & repair operations, APS material planning attributes recommenders based on ETO / MTO / MTS strategies, and days-of-supply simulations. He was recognized among 40 under 40 - Top Data Scientists in 2023 for his thoughtful contributions to global AI innovations ubiquitously beneficial for supply chain teams. Some of his transformative industrialization of research efforts - Application of Clustering for Computationally Light Short-Term Demand Forecasting have been felicitated as Top Research Papers at internationally renowned Machine Learning conferences.