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Large-Scale Demand Forecasting for E-Commerce Retailer Using SAS® Forecast Studio: A Case Study

Started ‎04-19-2021 by
Modified ‎05-24-2021 by
Views 1,087
Paper 1180-2021

Ajay Kumar Mishra, Ph.D.; Mohak Narkhede; Chandu Saladi; Core Compete


A leading e-commerce retailer with multiple websites desired to create a systematic, scalable and science-driven product forecasting system. SAS Forecast Studio was configured on a cloud-based server for automated weekly forecasting of tens of thousands of products for the next six months. SAS was used for data insights, such as understanding distribution of product life and rate of sale, variability in demand and prices, as well as seasonality among the products. Pre-processing included creating demand history by adjusting order history for lost sales using inventory data. Price is an important predictor of demand for this retailer. We modeled holiday events using HPFEVENTS in SAS Forecast Server procedures. With a deep understanding of the patterns in the data, SAS Forecast Studio was configured for test runs to select independent variable form, fit criteria, reconciliation and hierarchy choices. The heterogeneity of the patterns ensured that ARIMA and UCM with predictor variables, as well as ESM, intermittent demand models and combined models emerged as winning models for a significant portion of products. In addition to several standard reports, elaborate accuracy reports were created, including forecast value added measurements. Steps were also added to identify exception forecasts and replace or modify them. The SAS Forecast Studio run was automated using an FSCREATE macro.

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Thank you for the presentation. As a web designer, I understand the need of introducing speed and automation in the forecasting process of analyzing trends and making predictions based on the order history. The company I’m currently working with, hired me to increase the performance of their website and make some design adjustments to assure user accessibility. As I was helping them migrate to magento 2 website, I realized that in order to improve web performance, I need to increase the page speed, which leads to predicting visitor behavior. I’m searching for the right tool to stop the website from losing visitors and to help the business grow.

Was the above the presentation what you were looking for?  You may want to look into SAS Visual Forecasting:


I usually do my forecast models through SAS ETS procedures so I can't help you there. SAS Viya had some visual forecasting methodology but I no longer have access to it at the moment.

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‎05-24-2021 08:30 AM
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