需求預測 + 價格最佳化

Blob 儲存體
Data Factory
HDInsight
Web Apps

解決方案構想 Solution Idea

如果您想要瞭解如何使用詳細資訊、實行詳細資料、定價指引或程式碼範例來擴充本文,請讓我們知道 GitHub 意見反應!If you'd like to see us expand this article with more information, implementation details, pricing guidance, or code examples, let us know with GitHub Feedback!

Pivotal 許多產業的定價,但它可能是最具挑戰性的工作之一。Pricing is pivotal for many industries, but it can be one of the most challenging tasks. 公司通常難以精確地預測潛在策略的會計影響,請完全考慮核心業務限制,並在完成後完全驗證定價決策。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. 當產品供應專案擴大並使計算的時間變得更複雜時,此程式就會變得更困難。As product offerings expand and complicate the calculations behind real-time pricing decisions, the process grows even more difficult.

此解決方案使用歷程記錄交易資料,在零售環境中定型需求預測模型,以解決這些挑戰。This solution addresses those challenges by using historical transaction data to train a demand-forecasting model in a retail context. 它也會納入競爭群組中產品的定價,以預測食和其他跨產品的影響。It also incorporates the pricing of products in a competing group to predict cannibalization and other cross-product impacts. 價格優化演算法接著會使用該模型來預測不同價格點的需求,以及商務條件約束中的因素,以將潛在收益最大化。A price-optimization algorithm then uses that model to forecast demand at various price points and factors in business constraints to maximize potential profit.

藉由使用此解決方案來內嵌歷史交易資料、預測未來的需求,並定期將定價優化,您將有機會節省流程的時間和精力,並改善貴公司的獲利。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.

架構Architecture

架構圖表會 下載此架構的SVGArchitecture Diagram Download an SVG of this architecture.

元件Components

  • Azure Data Lake Storage: Data Lake Store 儲存每週的原始銷售資料(HDInsight 上的 Spark 會讀取)。Azure Data Lake Storage: Data Lake Store stores the weekly raw sales data, which is read by Spark on HDInsight.
  • HDInsight上的 Spark 會內嵌資料,並執行資料前置處理、預測模型及價格優化演算法。Spark on HDInsight ingests the data and executes data preprocessing, forecasting modeling, and price-optimization algorithms.
  • Data Factory 會處理重新訓練模型的協調流程與排程。Data Factory handles orchestration and scheduling of the model retraining.
  • Power BI 將銷售結果視覺化、預測的未來需求,以及在不同商店銷售之各種產品的建議最佳價格。Power BI visualizes sales results, the predicted future demand, and the recommended optimal prices for a variety of products sold in different stores.

後續步驟Next steps