DataHour: Personalizing Demand Planning Using Scenario Analysis

About the DataHour:

Demand Planning is a complicated, collaborative process wherein the understanding of future customer demand helps in deciding inventory, production, revenue, and services across the organization. While accurate forecasting is an important step towards it, the end decisions are about deciding between various plausible tradeoffs. Typically, such tradeoffs are evaluated in an ad-hoc manner at the best, and there is no one-size-that-fits-all solution in terms of which tradeoff to choose to operationalize.  

In this DataHour, Devavrat will discuss the recent technological advances that enable evaluation of feasible tradeoffs between multiple objectives using historical data with the help of scenario analysis so that teams working collaboratively can make such decisions that are right for them at that time, covering the following points in detail:

• Scenario analysis and how it can help evaluate possible tradeoffs
• Role of forecast accuracy in demand planning through scenario analysis
• End-to-end tools that incorporate data, enable accurate forecasts, perform scenario analysis and operationalize demand planning across organizations based on the chosen tradeoff.

Prerequisites: A strong interest in Data Science

Who is this DataHour for?

• Students & Freshers who want to build a career in the Data-tech domain.
• Working professionals who want to transition to the Data-tech domain.
• Data science professionals who want to accelerate their career growth

Note: E-certificates will be provided within 24 - 48 hours of the session only to those who have attended the entire webinar. Please make sure to join the zoom webinar with your correct name and email address to ensure that your certificate is properly credited to you.

Speaker:

Devavrat Shah

Andrew (1956) and Erna Viterbi Professor at MIT

Devavrat Shah is an Andrew (1956) and Erna Viterbi professor of Computer Science and AI at MIT since 2005 where he founded MIT’s Statistics and Data Science Center and currently directs Deshpande Center for Tech Innovation. Previously, he co-founded Celect, focused on inventory optimization using AI (acquired by Nike in 2019). Currently, he serves as the CTO of Ikigai Labs which he co-founded in 2019, with the mission of building a self-driving organization by empowering data business operators to make data-driven decisions with ease of spreadsheets. He received his B.Tech. degree from IIT Bombay and his Ph.D. degree from Stanford University, both in Computer Science. He is a Kavli Fellow of National Academy of Science. He has received paper awards from INFORMS Applied Probability Society, INFORMS Management Science and Operations Management, NeurIPS, ACM Sigmetrics and IEEE Infocom. He has received the Erlang Prize from INFORMS Applied Probability Society and Rising Star Award from ACM Sigmetrics. He has received multiple Test of Time paper awards from ACM Sigmetrics. He is a distinguished alumni of his alma mater IIT Bombay.

Connect with him on Linkedin and Website.