Product Master Data Collection: Assessment and Development
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Master data is cleansed and rationalized data about, for example products, suppliers or customers, which is shared in an enterprise-wide system. In large companies, master data with the right quality need to be in place at the right time for enabling smooth operations. Master data management (MDM) is the solution for achieving this. The first part of master data creation in MDM is the collection of source data.
The purpose with the thesis is to present Ericsson’s problems with their collection of product master data and to create guidelines for product master data collection (PMDC) that can be used in large companies that have a centralized MDM.
The study was made as an interpretive case study at Ericsson by interviews of employees related to the PMDC and through analysis of PMDC data. A literature study covering Master Data Management, Lean Information Management, Lean Six Sigma and Human-Computer Interaction was also made.
The process of collecting product master data varied among different parts of Ericsson. When the case study was analyzed and the theory was applied, a lot of improvement potential was found in Ericsson’s processes. Their process is not “lean” and the tool used for data collection showed a lot of usability issues.
Lean Six Sigma is providing a good toolkit to work with PMDC improvement. The collected data is the only value of the PMDC process. This is created from users’ manual input, which lead to the conclusion, that to get an increased master data quality, a user-centric approach in PMDC improvement projects is necessary. It is also important to collect data about the process to fully understand it to be able to improve it further.
Place, publisher, year, edition, pages
2012. , 40 p.
Examensarbete INDEK, 2012:17
data collection, human-computer interaction, lean information management, performance metrics, product master data
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-92218OAI: oai:DiVA.org:kth-92218DiVA: diva2:512740
Subject / course
Industrial Economics and Management
Master of Science in Engineering - Mechanical Engineering
Tarandi, Väino, Professor
Important parts of the thesis are not published due to confidentiality. However, conclusions and guidelines are still valid. Please contact the authors for additional information.2012-03-292012-03-292012-03-29Bibliographically approved