demand signal repository (DSR)

What is demand signal repository (DSR)?

A demand signal repository (DSR) is a database that aggregates sales and demand data at the merchant's point of sale (PoS). DSR systems support data querying by capturing, cleaning and harmonizing large data sets.

The aim of a DSR is to allow business users to identify which products are selling, how often and where -- even if these users don't have technical knowledge. Organizations can use DSR systems to improve their supply chains and become more responsive to market trends and customer needs.

DSR repositories also help lay the foundation for an effective information architecture for enterprises and their supply chains. They support many predictive analytics applications related to demand and supply processes.

Understanding demand signal repositories

A DSR is essentially a centralized data warehouse that integrates and cleans demand data. It also stores, organizes and harmonizes the attributes for this data. This may include the following:

• POS data
• Wholesaler data
  • Electronic data interchange (EDI)
  • Inventory turnover and movement
  • Promotion data
• Customer loyalty data

This data is useful for consumer goods and electronics manufacturers, and many other types of companies that can use it for demand sensing to serve retailers and consumers more effectively.

Companies also use this data with decision support technologies for a variety of use cases, including:

• Category management
• Shopper insights analysis
• Demand planning and forecasting
• Inventory replenishment

By cleaning and synchronizing data with POS, syndicate scanner and internal data related to shipping, replenishments, etc., DSR helps companies get a clearer picture of their retail performance. They can then use this information to improve, grow and become more profitable.

How demand signal repository works

Typically, two activities are involved in the efficient functioning of DSRs:

1. Storing the information in a specific format and database
2. Performing predictive analytics to use the data

The information is stored in the repository in a specific format to allow for easy retrieval. It also enables users to easily navigate through the database to garner crucial information about what's selling, in what quantity, and when and where. Employing this data and analyzing it further enables users to more effectively identify and source market opportunities, understand demand performance, and take steps to reduce and even eliminate out-of-stock situations.

The data gathered from the DSR can be used to perform predictive analysis. This way, both current and potential impacts can be identified from various perspectives -- such as sales, marketing, branding and demand planning -- by considering the POS within the organization's data model.

In the larger scheme, DSRs support many business functions, including sales and marketing and promotion and event management, as well as category management, supply chain management and inventory management.

Common features of a demand signal repository

A typical DSR utilizes demand data and delivers insights that enable organizations to become more demand-driven and drive more profitable growth. A DSR can capture and harmonize large volumes of data -- often at the day-to-day, store and stockkeeping unit (SKU) level -- thanks to features including the following:

• a scalable data model;
• a library of facts, measures and attributes;
• retailer or manufacturer hierarchies and calendars to display data;
• category management reporting templates and scorecard dashboards;
• customizable dashboards and reports; and
• alerting capability based on user-defined thresholds or goals.

The need for demand signal repositories

For companies where inventory plays a crucial role in business operations and profitability, static data received from customers or the sales department is of limited use for making inventory-related decisions. This is especially true in highly complex business sectors with many products or unpredictable demand levels.

In such cases, automation technologies, real-time data and processing algorithms are required. Here's where DSR helps business users make better decisions to reduce demand latency, improve inventory management and boost profitability.

DSR can help generate crucial insights that reflect current -- and thus, more useful -- market realities. This provides greater visibility, which enables organizations to get greater control over their inventories. They can analyze monthly demand data and use those insights to avoid overstocks, out-of-stocks or dead stock. DSRs can also assess new-product launches and decide where to fill product gaps based on current and future demand.

Benefits of demand signal repositories

DSR offers many benefits, especially when it is complemented by demand signal analytics (DSA) solutions. DSA refers to a mix of visual analytics and predictive analytics to access data in DSRs with minimal latency. Manufacturers can collaborate with their retailer/customer networks more effectively by integrating POS data with company-specific attributes. This data can help drive improvements related to:

• demand forecast accuracy;
• detection of product category changes;
• evaluation of new-product information;
• lower inventory levels;
• lower safety stock levels; and
• trade promotions.

A DSR also enables product companies to:

• get a holistic view of demand activity;
• reduce the frequency of out-of-stock goods;
• predict which products are trending toward stockouts;
• detect replenishment issues;
• identify higher or lower anticipated lift during promotions;
• reduce expenses related to expedited freight; and
• lower costs and increase revenue with improved demand planning.

When tied into supply chain processes and POS systems, DSR and DSA provide crucial business intelligence. Companies can utilize this intelligence to gain complete transparency into the supply chain to optimize processes, uncover new market opportunities and identify gaps that require immediate attention.

See also 7 risk mitigation strategies to protect business operations, 6 top business benefits of real-time data analytics and what the role of demand shaping is in supply chain planning.