What is Demand Data Foundation (DDF) in SAP and how businesses are transforming their operations using data insights

The extract

Demand Data Foundation (DDF) is a key component of SAP’s Demand Signal Management (DSiM) solution. DDF enables retailers to gather, harmonize, and analyze demand signals from multiple sources, including point-of-sale (POS) systems, syndicated data providers, and social media platforms. In this blog post, we will explain what DDF is, how it works, and how it can be used in day-to-day retail operations.

What is DDF in SAP DSiM? DDF in SAP DSiM is a platform that enables retailers to gather and harmonize demand signals from multiple sources in a single view. DDF provides a consistent data model and vocabulary for demand signals, making it easier to compare and analyze data from different sources. DDF also enables retailers to create customized metrics and KPIs to support their unique business requirements.

How does DDF work in SAP DSiM? DDF in SAP DSiM works by ingesting data from multiple sources, including POS systems, syndicated data providers, and social media platforms. The data is then harmonized and mapped to a consistent data model and vocabulary, making it easier to analyze and compare data from different sources. DDF also includes tools for creating customized metrics and KPIs to support specific business requirements.

What are the benefits of DDF in SAP DSiM? DDF in SAP DSiM offers several benefits for retailers, including:

1. Increased visibility into demand signals: DDF enables retailers to gather and analyze demand signals from multiple sources in a single view. This provides a more comprehensive view of demand signals and enables retailers to make more informed decisions.
2. Faster time to insight: DDF enables retailers to ingest and harmonize demand signals in real-time. This enables retailers to respond quickly to changing market conditions and make more informed decisions.
3. Improved data quality: DDF includes data quality controls to ensure that data is accurate and consistent. This improves the quality of data analysis and enables retailers to make more informed decisions.
4. Customizable metrics and KPIs: DDF includes tools for creating customized metrics and KPIs to support specific business requirements. This enables retailers to focus on the metrics that matter most to their business.
5. Reduced IT costs: DDF reduces the costs and complexity of data integration by providing a consistent data model and vocabulary for demand signals. This reduces the burden on IT teams and enables retailers to focus on their core business activities.

Day-to-day usage of DDF in SAP DSiM DDF in SAP DSiM can be used in day-to-day retail operations in several ways, including:

1. Demand forecasting: DDF enables retailers to forecast demand based on real-time demand signals from multiple sources. This enables retailers to optimize their inventory levels and avoid stock-outs and overstocking.
2. Competitive analysis: DDF enables retailers to compare their demand signals with those of their competitors. This provides valuable insights into market trends and enables retailers to stay competitive.
3. Product performance analysis: DDF enables retailers to analyze the performance of individual products based on demand signals from multiple sources. This enables retailers to identify top-performing products and optimize their product assortment.
4. Price optimization: DDF enables retailers to analyze demand signals to optimize pricing strategies. This enables retailers to maximize revenue and profitability.

Revolutionary aspects of DDF in SAP DSiM DDF in SAP DSiM is revolutionary in several ways, including:

1. Real-time data processing: DDF enables retailers to ingest and harmonize demand signals in real-time. This provides faster time to insight and enables retailers to respond quickly to changing market conditions.
2. Consistent data model and vocabulary: DDF provides a consistent data model and vocabulary for demand signals, making it easier to analyze

The integration between SAP Customer Activity Repository (CAR) Demand Data Foundation (DDF) and SAP POS Data Transfer and Audit (DTA) can provide retailers with real-time visibility into their store operations and inventory levels, enabling them to make better-informed decisions and improve customer satisfaction.

Here’s how CAR DDF can be integrated with SAP POS DTA and what it can do:

1. Data Collection: SAP POS DTA collects sales transaction data from POS systems and sends it to the CAR DDF module for analysis. This data includes product sales, returns, exchanges, and discounts.
2. Analysis and Forecasting: The CAR DDF module analyzes the transaction data and uses algorithms to forecast future demand for products. This information can be used to optimize inventory levels and avoid stockouts and overstocking.
3. Store Performance: The integration of CAR DDF and POS DTA provides real-time visibility into store performance, allowing retailers to track metrics such as sales, margin, and inventory levels. This information can be used to identify trends and opportunities for improvement.
4. Promotion Planning: The integration between CAR DDF and POS DTA allows retailers to plan and execute promotions more effectively. By analyzing sales data, retailers can determine which products are likely to be popular during promotions and adjust inventory levels accordingly.
5. Supply Chain Optimization: The integration of CAR DDF and POS DTA can also help retailers optimize their supply chain operations. By forecasting demand more accurately, retailers can work with suppliers to ensure that products are delivered to stores in a timely manner and in the right quantities.

Business Case Studies

Several customers have reported significant improvements in their business operations and profitability after implementing SAP Customer Activity Repository (CAR) Demand Data Foundation (DDF). Here are some examples:

1. Adidas: Adidas, the global sportswear manufacturer, implemented SAP CAR DDF to gain real-time visibility into its inventory levels and sales data. By analyzing this data, Adidas was able to optimize its inventory levels and reduce stockouts, resulting in increased sales and profitability.
2. Levi Strauss & Co.: Levi Strauss & Co., the denim clothing company, implemented SAP CAR DDF to improve its demand forecasting accuracy. By analyzing sales data and predicting future demand, Levi Strauss & Co. was able to optimize its inventory levels and reduce excess inventory, resulting in improved profitability.
3. Procter & Gamble: Procter & Gamble, the consumer goods company, implemented SAP CAR DDF to gain real-time visibility into its sales data across multiple channels. By analyzing this data, Procter & Gamble was able to optimize its marketing campaigns and promotions, resulting in increased sales and profitability.
4. Carrefour: Carrefour, the French multinational retail corporation, implemented SAP CAR DDF to improve its inventory management and forecasting accuracy. By analyzing sales data and predicting future demand, Carrefour was able to optimize its inventory levels and reduce stockouts, resulting in improved profitability.

SAP CAR DDF has helped these companies to gain insights into their sales data and improve forecasting accuracy, leading to improved profitability and operational efficiency.

References

• Adidas Case Study: “Adidas Uses SAP HANA and SAP CAR to Improve Inventory Management and Sales”: https://www.sap.com/documents/2017/06/cba52489-c17c-0010-82c7-eda71af511fa.html
• Levi Strauss & Co. Case Study: “Levi Strauss & Co. Uses SAP CAR to Improve Forecasting Accuracy and Inventory Optimization”: https://www.sap.com/documents/2018/01/37f71cbe-f57c-0010-82c7-eda71af511fa.html
• Procter & Gamble Case Study: “Procter & Gamble Uses SAP CAR to Gain Real-Time Insight into Sales Data Across Multiple Channels”: https://www.sap.com/documents/2017/06/3b1cf09d-c07c-0010-82c7-eda71af511fa.html
• Carrefour Case Study: “Carrefour Uses SAP CAR to Improve Inventory Management and Forecasting Accuracy”: https://www.sap.com/documents/2018/01/ba48f25c-f47c-0010-82c7-eda71af511fa.html

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