On-Demand Webinar

Using Downstream Data to Improve Forecast Accuracy 

Learn how to improve forecast accuracy by leveraging big data from causal factors to outliers.

About the webinar

Whether you’re in retail, consumer goods, manufacturing or services, all demand planners face the same challenges around creating accurate forecasts and managing their business planning. Join our planning experts as they discuss how to improve forecast accuracy by leveraging big data and analytics to incorporate outliers and causal factors. Explore visual and demand driven analyses and learn how these approaches can improve efficiency & accuracy, reduce inventory and waste and drive revenue for your organization.

Join us to learn:

  • How to discern the differences of downstream data between causal factors and outliers 
  • How your company will benefit from forecast accuracy
  • How machine learning and automation can be a part of your forecasting strategy and improve efficiency
  • How to figure out your next steps to approach

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About the Experts


Pat Valente
Data Science Solutions Specialist, SAS Canada

By leveraging best-in-class software and modern data mining techniques, Pat helps businesses visualize the potential of their data in order to surface relevant insights from it. Pat holds an academic background in Economics and Statistics along with over 20 years of business experience. Pat can easily understand and address data challenges in large organizations, and he knows how to help create value at every step of the analytics lifecycle from data discovery through to business strategy. As a recognized industry expert, Pat has delivered numerous webinars and workshops focusing on how SAS solutions can aid businesses in their analytic growth.


Edward Katz
 Principal Analytics Consultant, CT Global Solutions

Before joining CT Global Solutions, Edward (Ed) Katz previously worked for SAS Institute in Global Forecasting and Supply Chain Practice as a Principal Analytics Consultant for almost 20 years. He has worked with a wide array of clients in multiple industries including (but not limited to) Nestle, Toyota Automotive, Shoppers Drug Mart, Lockheed Martin, BNSF Rail, The Gap and many more. His focus was on Machine Learning applied to forecasting in high volume/demand planning and optimization environments. Prior to joining SAS, Ed was a partner and co-founder of a manufacturing and distribution Enterprise and Supply Chain Analytics software company called DAI which eventually merged with Infor Software.

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