Batch Derivation Reduces Need for Data Mining

  • by Siddhartha Mathur, Sr. Consultant, Bristlecone Inc (July 2009)
  • July 21, 2009
Learn how semiconductor industries are making use of batch derivation functionality to reduce time and effort spent on searching information through their supply chain. Examine a business scenario and use it as a guide for configuring SAP’s batch derivation.
Key Concept
Semiconductor manufacturing companies are moving from their traditional in-house manufacturing model to a fabless semiconductor model. A fabless semiconductor manufacturing model is based on outsourcing the semiconductor manufacturing processes to key vendors, while at the same time keeping research and design in-house. The design specifications are passed on to the business partners who do the manufacturing for a fabless semiconductor company. Then they transfer the semi-finished goods to the next business partner. The supply chain is managed by the fabless semiconductor company.
Many industries want to have visibility of their inventory at the batch level. Having visibility helps them to segregate the quantity and specifications of their inventory in the supply chain or with the customer. For example – a chemical company might want to segregate the inventory for finished goods produced with formula X from the same finished goods produced by formula Y. Similarly, a semiconductor company might want to segregate its micro-processor product with a high frequency from a micro-processor with a lower frequency. Others need to manage inventory at the batch level because of legal requirements, defect tracking, call back activities, or production tracking.

Batch derivation, also popularly known as batch inheritance, is a part of SAP’s Batch Management module and is integrated with SAP materials management (MM), quality management (QM), production planning/process integration (PP/PI), sales and distribution (SD), and Managerial Accounting (CO) modules. Batch derivation is used to transfer and copy batch master and characteristics values downstream in the supply chain.

Usually businesses decide to derive characteristics values which help them to make business decisions. Through batch derivation functionality they can get consolidated information at the finished goods level. This eases the data mining task for the user, who otherwise would have to search for the information throughout the supply chain. It also reduces the need to write complicated reports, which would search the database for information and add to the system load.

In this article, I will show how batch derivation works at the test/finished goods level and how you can copy batch characteristics values over into test/finished goods batch characteristics from a package assembly material’s batch characteristics. Finally, I will demonstrate how batch characteristics – fab code and foundry lot number – take part in batch derivation. Refer to the sidebar at the end of this article to understand the data used to demonstrate the functionality.

Siddhartha Mathur

Siddhartha Mathur is a senior consultant in a supply chain management practice with a focus in the manufacturing vertical. His consulting experience spans more than five years, and he also has more than two years of domain experience. He has comprehensive industry expertise and consulting experience in the hi-tech and automotive industries. Siddhartha holds a master’s degree in business administration majoring in operations and systems. He has an engineering background at the undergraduate level. He has been awarded certifications by APICS in Certified Supply Chain professional (CSCP) and Certified Production and Inventory Management (CPIM).

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