IBF Town Hall: Future-Proofing Strategies: Unleashing the Potential of Predictive Analytics
Sowmya Kumar
Sr. Data and Applied Scientist - Azure Supply Chain

Sowmya is a passionate data science leader with industry experience developing machine learning models at scale in production from inception to business impact. She was the founding member of data science team at Microsoft Devices, building strategic data science roadmaps and successfully executing them for multiple years with strong business impact and now part of Data science at Azure Supply Chain. She developed deep learning and machine learning modules for solving the demand-supply inventory forecast, customer level forecasting, labor optimization problems that handles seasonality, transition/ new products, marketing-promotions etc. She has experience brining applied science into different aspects of Marketing, Supply Chain & Retail. Prior to Microsoft, she was at Starbucks and was part of data science team in marketing where she was part of building customer segmentation for targeting offers to drive revenue, A/B experimentation to drive revenue and customer engagement.

Patrick Bower
Sr. Director of Supply Chain

Patrick Bower has a wide area of expertise, including S&OP, Demand Planning, Inventory, Network Optimization, and Production Scheduling. A recognized expert on demand planning and S&OP, and a self-professed “S&OP geek” - Patrick was previously Practice Manager of Supply Chain Planning at the consulting firm, Plan4Demand where his client list included Diageo, Bayer, GlaxoSmithKline, Pfizer, Foster Farms, Cabot Industries and American Girl. Patrick’s experience encompasses tenures with Cadbury, Kraft Foods, Unisys, and Snapple. Patrick also worked for the supply chain software company - Numetrix, and was Vice President of R&D at Atrion International. He was also the recipient of IBF’s 2012 award for Excellence in Business Forecasting & Planning.

Andrew D. Schneider, ACPF
Head of Demand Planning & Analytics
Viega LLC

Andrew Schneider is a veteran supply chain and business science professional, with a 20 year career spanning journalism, sales, demand planning, analytics, emerging data sciences, strategy, and enterprise risk management. He is a thought leader for the IBF and a member of the esteemed advisory board, an ASCM award-winning fellow & certification holder, and an always-hungry learner of & contributor to the business planning field. He has vast experience across leadership & practitioner roles in CPG, Footwear & Apparel, Food & Beverage, and Medical Device sectors, along with systems implementation, COE deployment, and full supply chain transformation experience. Andrew is presently employed in a supply chain management role with Viega LLC, but also maintains his consultancy practice and is always available to network, consult, and serve. He is an IBF Advanced Certified Professional Forecaster (ACPF).

Rich Wagner
Founder, President & Chief Product Officer
Rich helps companies to look beyond their own walls for key external drivers of financial performance. Utilizing millions of global metrics from data powerhouses like Nielsen, NASDAQ and the Federal Reserve, Rich has uniquely positioned Prevedere as a complementary solution to existing forecasting platforms by correlating external economic factors to corporate performance. Under Rich’s leadership, Prevedere has been named a “Cool Vendor in Information Innovation” by Gartner and an FP&A Innovation Awards winner in Forecasting and Planning.
You don’t want to miss this thought-provoking discussion that explores how organizations can leverage predictive insights to anticipate trends, optimize processes, and make data-driven decisions that stand the test of time. Join the dialogue and discover how predictive analytics can be a cornerstone in shaping the future success of your organization.
You will learn:
  • Adding guardrails to your baseline forecasts for resilient planning
  • Leveraging AI for early warnings and adaptability in dynamic markets
  • A deep dive into different advanced predictive analytics models
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