GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. This solution addresses the challenges raised above by utilizing historical transaction data to train a demand forecasting model. Pricing of products in a competing group is also incorporated to predict cross-product impacts such as cannibalization.

A price optimization algorithm then employs the model to forecast demand at various candidate price points and takes into account business constraints to maximize profit. The solution can be customized to analyze various pricing scenarios as long as the general data science approach remains similar. The process described above is operationalized and deployed in the Cortana Intelligence Suite.

This solution will enable companies to ingest historical transaction data, predict future demand, and obtain optimal pricing recommendations on a regular basis. As a result, the solution drives opportunities for improved profitability and reductions in time and effort allocated to pricing tasks. This pattern requires creation of 1 HDInsight Cluster with 16 cores.

Ensure adequate HDInsight core quotas are available before provisioning. By default one subscription can create a maximum of 20 core cluster. The limit can be increased. Please consider deleting any unused HDInsight Cluster from your subscription. You may contact Azure Support if you need to increase the limit. This solution combines several Azure services to provide powerful advantages. Azure Blob Storage Store stores the weekly raw sales data.

Apache Spark for Azure HDInsight ingests the data and executes data preprocessing, forecasting modeling and price optimization algorithms. Finally, Data Factory orchestrates and schedules the entire data flow. The 'Deploy' button will launch a workflow that will deploy an instance of the solution within a Resource Group in the Azure subscription you specify.

demand forecasting and price optimization github

The solution includes multiple Azure services described below along with a web job that simulates data so that immediately after deployment you have a working end-to-end solution. This synthetic data is stored at Azure Blob Storagethat will be used in the rest of the solution flow. Spark on HDInsight is used to ingest and preprocess the raw data, build and retrain the demand forecasting models, and execute price optimization algorithms.

Finally, Power BI is used for results visualization, so that users can monitor the results of the sales, predicted future demand as well as recommended optimal prices for a variety of products sold in different stores.

Kaggle Mercari Price Suggestion Challenge (1 place) — Pawel Jankiewicz, Konstantin Lopuhin

All rights reserved. This information is provided "as-is" and may change without notice. Microsoft makes no warranties, express or implied, with respect to the information provided here. Third party data was used to generate the solution. You are responsible for respecting the rights of others, including procuring and complying with relevant licenses in order to create similar datasets.

Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Branch: master. Find file Copy path. Cannot retrieve contributors at this time. Raw Blame History. Summary Pricing is recognized as a pivotal determinant of success in many industries and can be one of the most challenging tasks.

Companies often struggle with several aspects of the pricing process, including accurately forecasting the financial impact of potential tactics, taking reasonable consideration of core business constraints, and fairly validating the executed pricing decisions.

Expanding product offerings add further computational requirements to make real-time pricing decisions, compounding the difficulty of this already overwhelming task. Description Estimated Provisioning Time: 36 Minutes Cortana Intelligence provides advanced analytics tools through Microsoft Azure — data ingestion, data storage, data processing and advanced analytics components — all of the essential elements for building a demand forecasting and price optimization solution.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Pricing is recognized as a pivotal determinant of success in many industries and can be one of the most challenging tasks. Companies often struggle with several aspects of the pricing process, including accurately forecasting the financial impact of potential tactics, taking reasonable consideration of core business constraints, and fairly validating the executed pricing decisions.

Expanding product offerings add further computational requirements to make real-time pricing decisions, compounding the difficulty of this already overwhelming task. This solution addresses the challenges raised above by utilizing historical transaction data to train a demand forecasting model.

Demand Forecasting and Price Optimization

Pricing of products in a competing group is also incorporated to predict cross-product impacts such as cannibalization. A price optimization algorithm then employs the model to forecast demand at various candidate price points and takes into account business constraints to maximize profit. The solution can be customized to analyze various pricing scenarios as long as the general data science approach remains similar. The process described above is operationalized and deployed in the Cortana Intelligence Suite.

This solution will enable companies to ingest historical transaction data, predict future demand, and obtain optimal pricing recommendations on a regular basis. As a result, the solution drives opportunities for improved profitability and reductions in time and effort allocated to pricing tasks. For a discussion of the analytical approach used in this solution, see the Solution Description in the Manual Deployment Guide. In this session, we provide more details about how the above analytical approach is operationalized in Cortana Intelligence.

The following chart describes the solution architecture. The final results are visualized in Power BI Dashboard. The whole process is scheduled weekly, with data movement and scheduling managed by Azure Data Factory.

A parallel version of the price optimization algorithm is implemented on Spark. Utilizing RDD. The snapshot below shows the Power BI dashboard that visualizes the results of demand forecasting and price optimization solution.

This solution template contains materials to help both technical and business audiences understand our demand forecasting and price optimization solution built on Cortana Intelligence. In this repository you will find a folder labeled Solution Overview for Business Audiences. This folder contains:. For more information on how to tailor Cortana Intelligence to your needs, connect with one of our partners.

For technical problems or questions about deploying this solution, please post in the issues tab of the repository. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Retail industry solutions for product price optimization using the Cortana Intelligence Suite with end-to-end walkthrough.

Python Branch: master. Find file. Sign in Sign up. Go back.This solution addresses the challenges raised above by utilizing historical transaction data to train a demand forecasting model. Pricing of products in a competing group is also incorporated to predict cross-product impacts such as cannibalization. A price optimization algorithm then employs the model to forecast demand at various candidate price points and takes into account business constraints to maximize profit.

The solution can be customized to analyze various pricing scenarios as long as the general data science approach remains similar. The process described above is operationalized and deployed in the Cortana Intelligence Suite. This solution will enable companies to ingest historical transaction data, predict future demand, and obtain optimal pricing recommendations on a regular basis.

As a result, the solution drives opportunities for improved profitability and reductions in time and effort allocated to pricing tasks. This pattern requires creation of 1 HDInsight Cluster with 16 cores. Ensure adequate HDInsight core quotas are available before provisioning. By default one subscription can create a maximum of 20 core cluster. The limit can be increased. Please consider deleting any unused HDInsight Cluster from your subscription. You may contact Azure Support if you need to increase the limit.

This solution combines several Azure services to provide powerful advantages. Azure Blob Storage Store stores the weekly raw sales data. Apache Spark for Azure HDInsight ingests the data and executes data preprocessing, forecasting modeling and price optimization algorithms. Finally, Data Factory orchestrates and schedules the entire data flow. The 'Deploy' button will launch a workflow that will deploy an instance of the solution within a Resource Group in the Azure subscription you specify.

The solution includes multiple Azure services described below along with a web job that simulates data so that immediately after deployment you have a working end-to-end solution. This synthetic data is stored at Azure Blob Storagethat will be used in the rest of the solution flow. Spark on HDInsight is used to ingest and preprocess the raw data, build and retrain the demand forecasting models, and execute price optimization algorithms.

Finally, Power BI is used for results visualization, so that users can monitor the results of the sales, predicted future demand as well as recommended optimal prices for a variety of products sold in different stores. All rights reserved. This information is provided "as-is" and may change without notice. Microsoft makes no warranties, express or implied, with respect to the information provided here.

Third party data was used to generate the solution. You are responsible for respecting the rights of others, including procuring and complying with relevant licenses in order to create similar datasets.

Skip to content. Branch: master. Create new file Find file History. Latest commit.Solution Idea. If you'd like to see us expand this article with more information implementation details, pricing guidance, code examples, etclet us know with GitHub Feedback! Pricing is pivotal for many industries, but it can be one of the most challenging tasks.

Companies often struggle to accurately forecast the fiscal impact of potential tactics, fully consider core business constraints, and fairly validate pricing decisions once they've been made. As product offerings expand and complicate the calculations behind real-time pricing decisions, the process grows even more difficult.

This solution addresses those challenges by using historical transaction data to train a demand-forecasting model in a retail context.

It also incorporates the pricing of products in a competing group to predict cannibalization and other cross-product impacts. A price-optimization algorithm then uses that model to forecast demand at various price points and factors in business constraints to maximize potential profit.

By using this solution to ingest historical transaction data, predict future demand, and regularly optimize pricing, you'll have the opportunity to save time and effort around the process and improve your company's profitability. Download an SVG of this architecture. You may also leave feedback directly on GitHub.

Skip to main content. Exit focus mode. Is this page helpful? Yes No. Any additional feedback? Skip Submit. Send feedback about This page. This page. Submit feedback. There are no open issues. View on GitHub.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. In this section, we provide more details about the analytical approach taken in the solution. The price optimization approach and algorithm used in this solution follows the method described by Ferreira et al.

The following chart provides a high-level illustration of the analytical approach taken in this solution.

Demand Forecasting and Price Optimisation

Raw data is cleaned and aggregated after ingestion into the pipeline. Demand forecasting models are trained and retrained regularly on the processed datasets. The price optimization algorithm will use the demand forecasting model to predict future demand at candidate price points within feasible ranges, and solve optimization problems specifically mixed integer programming problems to obtain the optimal prices.

A demand forecasting model is built on the features mentioned above. The model's performance is evaluated through mean absolute percentage error MAPE. The model is retrained monthly, since continuously-acquired transaction data can be used to improve the demand forecasting accuracy and, consequently, the price optimization results.

A competing group contains similar products that are competing against each other on the market, thus price changes on one product will affect the sales of the other products in the same competing group.

Price changes for products from different competing groups are assumed to have no impact on one another's sales. In this solution, prices are optimized by competing group: a single optimization problem is solved to determine optimal prices for all products in each competing group cf. Ferreira et al. While different companies could vary a lot on how to define the competing group, this solution takes the definition of competing groups as sets of products sold in the same department and at the same store.

As a result, different stores could price the same product differently in the solution, which corresponds to the reality that companies could have different pricing or promotional strategies for different stores depending on various store attributes such as average traffic, store location, and store tier. Companies may elect to tailor these constraints to their own business rules to reflect the differentiated pricing strategies they prefer for specific brands, departments, or stores.

A experiment is employed to evaluate the effect of price optimization strategy on profit. Stores are paired based on similarity and then divided into "treatment" and "control" groups.

Stores in the treatment group accept the prices recommended by the optimization algorithm, whereas stores in the control group use the company's previous pricing strategy which, in this demo scenario, is a randomized pricing strategy. The profit gain of the optimization approach can be estimated from the difference in profit between the treatment and control groups.

Different companies may also follow different pricing change schedules, but as a starting example, this solution implements weekly price optimization. Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Branch: master. Find file Copy path. Cannot retrieve contributors at this time. Raw Blame History. Demand Forecasting and Price Optimization Solution Analytical Approach In this section, we provide more details about the analytical approach taken in the solution.Solution Idea. If you'd like to see us expand this article with more information implementation details, pricing guidance, code examples, etclet us know with GitHub Feedback!

Pricing is recognized as a pivotal determinant of success in many industries and can be one of the most challenging tasks.

demand forecasting and price optimization github

Companies often struggle with several aspects of the pricing process, including accurately forecasting the financial impact of potential tactics, taking reasonable consideration of core business constraints, and fairly validating the executed pricing decisions.

Expanding product offerings add further computational requirements to make real-time pricing decisions, compounding the difficulty of this already overwhelming task. This solution addresses the challenges raised above by utilizing historical transaction data to train a demand forecasting model. Pricing of products in a competing group is also incorporated to predict cross-product impacts such as cannibalization.

A price optimization algorithm then employs the model to forecast demand at various candidate price points and takes into account business constraints to maximize profit. The solution can be customized to analyze various pricing scenarios as long as the general data science approach remains similar.

The process described above is operationalized and deployed in the Cortana Intelligence Suite. This solution will enable companies to ingest historical transaction data, predict future demand, and obtain optimal pricing recommendations on a regular basis. As a result, the solution drives opportunities for improved profitability and reductions in time and effort allocated to pricing tasks.

Download an SVG of this architecture.

demand forecasting and price optimization github

The Cortana Intelligence Suite provides advanced analytics tools through Microsoft Azure - data ingestion, data storage, data processing and advanced analytics components - all of the essential elements for building a demand forecasting and price optimization solution. This solution combines several Azure services to create powerful advantages.

Demand Forecasting + Price Optimization

Azure Blob Storage stores the weekly raw sales data. Apache Spark for Azure HDInsight ingests the data and executes data preprocessing, forecasting modeling and price optimization algorithms. Finally, Data Factory orchestrates and schedules the entire data flow.

The 'Deploy' button will launch a workflow that will deploy an instance of the solution within a Resource Group in the Azure subscription you specify. The solution includes multiple Azure services described below along with a web job that simulates data so that immediately after deployment you can see data flowing through the end-to-end pipeline. For post deployment instructions and more details on the technical implementation, please see the instructions here.

You may also leave feedback directly on GitHub. Skip to main content. Exit focus mode. Is this page helpful? Yes No. Any additional feedback?

demand forecasting and price optimization github

Skip Submit. Send feedback about This page. This page. Submit feedback. There are no open issues. View on GitHub.Pricing is recognised as a pivotal determinant of success in many industries and can be one of the most challenging tasks. Companies often struggle with several aspects of the pricing process, including accurately forecasting the financial impact of potential tactics, taking reasonable consideration of core business constraints and fairly validating the executed pricing decisions.

Expanding product offerings add further computational requirements to make real-time pricing decisions, compounding the difficulty of this already overwhelming task. This solution addresses the challenges raised above by utilizing historical transaction data to train a demand forecasting model. Pricing of products in a competing group is also incorporated to predict cross-product impacts such as cannibalization.

A price optimisation algorithm then employs the model to forecast demand at various candidate price points and takes into account business constraints to maximise profit. The solution can be customised to analyse various pricing scenarios as long as the general data science approach remains similar. The process described above is operationalised and deployed in the Cortana Intelligence Suite. This solution will enable companies to ingest historical transaction data, predict future demand and obtain optimal pricing recommendations on a regular basis.

As a result, the solution drives opportunities for improved profitability and reductions in time and effort allocated to pricing tasks. The Cortana Intelligence Suite provides advanced analytics tools through Microsoft Azure - data ingestion, data storage, data processing and advanced analytics components - all of the essential elements for building a demand forecasting and price optimisation solution.

This solution combines several Azure services to create powerful advantages. Azure Blob Storage stores the weekly raw sales data. Apache Spark for Azure HDInsight ingests the data and executes data preprocessing, forecasting modeling and price optimisation algorithms. Finally, Data Factory orchestrates and schedules the entire data flow. The 'Deploy' button will launch a workflow which will deploy an instance of the solution within a Resource Group in the Azure subscription you specify.

The solution includes multiple Azure services described below along with a web job that simulates data so that immediately after deployment you can see data flowing through the end-to-end pipeline. For post deployment instructions and more details on the technical implementation, please see the instructions here. All rights reserved. This information is provided "as-is" and may change without notice. Microsoft makes no warranties, express or implied, with respect to the information provided here.

Third party data was used to generate the Solution. You are responsible for respecting the rights of others, including procuring and complying with relevant licences in order to create similar datasets. Accurately forecasting spikes in demand for products and services can give a company a competitive advantage. This solution focuses on demand forecasting within the energy sector. The Demand Forecasting for Shipping and Distribution Solution uses historical demand data to forecast demand in future periods across varios customers, products and destinations.

For instance, a shipping or delivery company wants to predict the quantities of the different products its customers want delivered at different locations at future times. A company can use these forecasts as input to an allocation tool that optimises operations, such as delivery vehicles routing, or to plan capacity in the longer term.

In today's highly competitive and connected environment, modern businesses can no longer survive with generic, static online content. Furthermore, marketing strategies using traditional tools are often expensive, hard to implement and do not produce the desired return on investment.

These systems often fail to take full advantage of the data collected to create a more personalised experience for the user. Demand Forecasting and Price Optimisation.

Technical details and workflow The simulation data is generated hourly by newly deployed Azure Web Jobs. This synthetic data is stored at Azure Blob Storage, that will be used in the rest of the solution flow.

Spark on HDInsight is used to ingest and preprocess the raw data, build and retrain the demand forecasting models and execute price optimisation algorithms.

Azure Data Factory orchestrates and schedules the entire data flow.