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Evolution of the Role of Measurement Systems in Industrial Decision Support

Evolution of the Role of Measurement Systems in Industrial Decision Support

Miroslaw Staron, Wilhelm Meding, Kent Niesel, Ola Söder
Copyright: © 2016 |Pages: 21
ISBN13: 9781466696396|ISBN10: 1466696397|EISBN13: 9781466696402
DOI: 10.4018/978-1-4666-9639-6.ch032
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MLA

Staron, Miroslaw, et al. "Evolution of the Role of Measurement Systems in Industrial Decision Support." Handbook of Research on Global Supply Chain Management, edited by Bryan Christiansen, IGI Global, 2016, pp. 560-580. https://doi.org/10.4018/978-1-4666-9639-6.ch032

APA

Staron, M., Meding, W., Niesel, K., & Söder, O. (2016). Evolution of the Role of Measurement Systems in Industrial Decision Support. In B. Christiansen (Ed.), Handbook of Research on Global Supply Chain Management (pp. 560-580). IGI Global. https://doi.org/10.4018/978-1-4666-9639-6.ch032

Chicago

Staron, Miroslaw, et al. "Evolution of the Role of Measurement Systems in Industrial Decision Support." In Handbook of Research on Global Supply Chain Management, edited by Bryan Christiansen, 560-580. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9639-6.ch032

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Abstract

Measurement data can be used for decision support in multiple ways – from one-time, manual data collection/presentation (reporting) through flexible business intelligence solutions to online, automated measurement systems. In centralized organizations, the measurement data is often collected through reporting, but the trends in modern organizations with empowered teams, globalized development, and needs to monitor continuously longer supply chains requires shift in the design and use of measurement systems. In this chapter, we present a study of evolving measurement systems at three companies with global businesses – Ericsson, Volvo Cars, and Axis Communications. The results of the study include the identification of the timeline of the evolution, distinct generations of measurement systems and information needs in the different phases of the evolution. The experiences show how to evolve centralized decision support systems to support global and distributed decision support.

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