Please use this identifier to cite or link to this item:
http://hdl.handle.net/10125/60022
Analytics Use Cases for Mass Customization – A Process-based Approach for Systematic Discovery
Item Summary
Title: | Analytics Use Cases for Mass Customization – A Process-based Approach for Systematic Discovery |
Authors: | Wache, Hendrik Dinter, Barbara Kollwitz, Christoph |
Keywords: | Business Intelligence, Business Analytics and Big Data: Innovation, Deployment and Management Organizational Systems and Technology Analytics Use Cases, Business Analytics, Mass Customization, Work System Method |
Date Issued: | 08 Jan 2019 |
Abstract: | Nowadays, mass customization (MC) is shaped by the application of digital technologies like computer-aided design, computer aided manufacturing, and distribution planning. Within a MC process, various data is created, which can be used to gain knowledge about past and future business activities by means of modern data analytics methods. The paper at hand applies design science research and presents a process-based approach for identifying potential analytics use cases for MC. For this purpose, a generic MC process is derived from previous literature and a systematic analysis is carried out using the work systems method. The resulting artifact offers a differentiated view on customers, products, activities, participants, technologies, and information as well as on the information flows within the MC process. It enables manufacturers to identify valuable opportunities for analytics and to optimize current MC processes. Furthermore, it can be used to develop a systematic process for the discovery and evaluation of analytics use cases and novel business models in the future. |
Pages/Duration: | 10 pages |
URI: | http://hdl.handle.net/10125/60022 |
ISBN: | 978-0-9981331-2-6 |
DOI: | 10.24251/HICSS.2019.708 |
Rights: | Attribution-NonCommercial-NoDerivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/ |
Appears in Collections: |
Business Intelligence, Business Analytics and Big Data: Innovation, Deployment and Management |
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