Re-Distributed Manufacturing and the Impact of Big Data: A Consumer Goods Perspective
| Autor: | Matthias Friedrich Tepel, Mohamed Zaki, Babis Theodoulidis, Andy Neely, Philip Shapira |
|---|---|
| Přispěvatelé: | Zaki, Mohamed [0000-0003-0264-2691], Neely, Andrew [0000-0001-8220-5242], Apollo - University of Cambridge Repository |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Engineering
Strategy and Management Big data 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Industrial and Manufacturing Engineering ResearchInstitutes_Networks_Beacons/manchester_institute_of_innovation_research Personalization digital factory personalisation big data 0502 economics and business digital manufacturing Marketing redistributed manufacturing Digital factory 021103 operations research business.industry 05 social sciences Engineering and Physical Sciences Computer Science Applications Social research customisation Work (electrical) Research council Manchester Institute of Innovation Research Digital manufacturing business 050203 business & management |
| Zdroj: | Zaki, M, Theodoulidis, B, Shapira, P, Neely, A & Tepel, M F 2019, ' Re-Distributed Manufacturing and the Impact of Big Data: A Consumer Goods Perspective ', Production Planning & Control . https://doi.org/10.1080/09537287.2018.1540068 |
| DOI: | 10.1080/09537287.2018.1540068 |
| Popis: | Digitalisation and the growth of big data promise greater customisation as well as change in how manufacturing is distributed. Yet, challenges arise in applying these new approaches in consumer goods industries that often emphasise mass production and extended supply chains. We build a conceptual framework to explore whether big data combined with new manufacturing technologies can facilitate redistributed manufacturing. Through analysis of 24 consumer goods industry cases using primary and secondary data, we investigate evolving manufacturing configurations, their underlying drivers, the role of big data applications, and their impact on the redistribution of manufacturing. We find some applications of redistributed manufacturing concepts, although in other cases existing manufacturing configurations are leveraged for high volume consumer goods products through big data analytics and market segmentation. The analysis indicates that the framework put forward in the paper has broader value in organising thinking about emerging interrelationships between big data and manufacturing. The work was supported by the Engineering and Physical Sciences Research Council (EPSRC) and the Economic and Social Research Council (ESRC) through The Network in Consumer Goods, Big Data and Re-Distributed Manufacturing (RECODE) hosted at Cranfield University under grant number EP/M017567/1. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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