Managing Data Orchestration and Integration at Scale

Chapter 3. Examples of Data Orchestration at Scale in Complex Landscapes

Let’s now look at three examples of companies who have complex data orchestration requirements based on the business drivers. We will focus on the business scenario, the challenge, the solution and result, and how data orchestration was able to address this challenge. We are using these three examples for several reasons:

• Each covers a different industry. They represent manufacturing (automotive manufacturing), public sector utilities, and consumer products.

• Each of these industries is changing how they do business, heavily influenced by rapid advances in technology. Each industry requires data orchestration to meet the complex data challenges.

• Each scenario combines SAP and third-party and/or open source technologies, which is common with SAP customers.

• Each has a clear business outcome being addressed.

Example 1: Predict Quality in Manufacturing Industry

In this example, an automotive company’s business focus is on the quality management process for an assembly line of car components.

Business Scenario

Despite the advanced degree of automation in the assembly line, in fact, quality management is still a mostly manual process, where selection of failed parts can only be done manually after a full batch is processed. For this reason, the company installed pressure sensors on the molding press, and infrared cameras right after the press to take a snapshot of the temperature distribution ...