Accurately forecast products demand based on seasonality, weather, historical purchasing patterns, and other factors. Automate replenishment orders by forecasting demand per product and geography.
Business Benefits: Ensure high availability for customers while maintaining minimal stock risk. Improve Capacity Management and Optimise Staffing Levels per Store
Understand the Use-case under 5 minutes
Video (3 minutes)
Demand Sensing enables companies to maximise sales opportunities while avoiding excess inventory to reduce logistics expenses through more accurate and detailed demand forecasting
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Video (2.5 minutes)
Precisely forecast up to 97% of your inventory, as well as lower stock levels on some categories for up to 65% using AI based Demand Forecasting
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Video (2 minutes)
Features of Antuit.ai solutions for demand forecasting, how companies are in need of accurate forecasting, and how their solutions address many challenges.
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Get to know more Business and Technical details about the use-case (15-30 minutes)
More detailed introduction covering business and technical aspects
Article(16 minutes)
A good introduction to the topic covering multiple aspects: how it’s done, how to create your forecasts, forecasting in omnichannel retail, forecasting accuracy, picking your forecasting software, and more
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Article (17 minutes)
A great article covering many different business and technical aspects about AI-cased Demand Forecasting. Includes two case studies as well
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Article (17 minutes)
Details about applying Machine Learning in different aspects of Retail Demand Forecasting
VisitCase studies, Organizational Aspects, Return on Investment examples
Video (44 minutes)
Hear how antuit.ai and Target deployed a single AI-powered forecast to improve multiple business functions and the lessons learned.
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Case Study
Case Studies for Customers applying Amazon Forecast (Cloud AI Service), many of which are retailers applying it for Demand Forecasting problems
VisitMore details on the technical aspects of the use-case
Video(34 minutes)
Learn how to save millions of dollars building an inventory management solution powered by Amazon Forecast (AI Cloud Service)
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Video (13 minutes)
Explains the difference between trend and seasonality and provides a comparison of traditional and ML based time-series forecasting
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Technical resources that will help you implement the use-case (notebooks, tutorials..)
Github Repo
This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them
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Kaggle Notebook
The M5 dataset, generously made available by Walmart, involves the unit sales of various products sold in the USA. This Notebook attempts to build a Time Series Forecasting model, along with EDA & Feature Engineering
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Article, Repo
Comparative study of Demand Forecasting Methods for a Retail Store (XGBoost Model vs. Rolling Mean). Includes links to Github Repo and Feature Engineering article
VisitVideo (35 minutes)
learn how to build and deploy models, and measure business impact using Amazon Forecast
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Article (24 minutes)
an overview of common data science techniques and frameworks to create a demand forecast model
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Data Sets you can use to build Demos, POCs, or test Algorithms
The M5 dataset involves the unit sales of various products sold in the USA, organized in the form of grouped time series
This competition is provided as a way to explore different time series techniques on a relatively simple and clean dataset. You are given 5 years of store-item sales data, and asked to predict 3 months of sales for 50 different items at 10 different stores
Off-the-Shelf Products using AI for Demand Forecasting
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