Orchestrating Resources for Big Data Analytics Implementation in Manufacturing SMEs: Insights Into Managerial Role and Engagement
| aut.relation.endpage | 33 | |
| aut.relation.issue | ahead-of-print | |
| aut.relation.journal | Production Planning and Control | |
| aut.relation.startpage | 1 | |
| aut.relation.volume | ahead-of-print | |
| dc.contributor.author | Dehe, Benjamin | |
| dc.contributor.author | Subasinghage, Maduka | |
| dc.contributor.author | Mirzaei, Maryam | |
| dc.date.accessioned | 2026-04-17T03:00:46Z | |
| dc.date.available | 2026-04-17T03:00:46Z | |
| dc.date.issued | 2025-09-24 | |
| dc.description.abstract | Big Data Analytics (BDA) offers transformative potential for Small and Medium Enterprises (SMEs), enabling enhanced performance, improved decision-making, innovation and business growth. Yet, manufacturing SMEs often face considerable constraints that hinder effective BDA implementation. This study adopts Resource Orchestration Theory (ROT) to explore how managers in manufacturing SMEs structure, bundle, and leverage resources to overcome these challenges and deploy BDA effectively. Using semi-structured interviews with 17 SMEs managers, we examine BDA deployment across supply chain operations guided by the SCOR model. The findings reveal key managerial roles and strategies, including approaches to selecting, configuring, and operationalising BDA solutions. This study contributes to theory by applying ROT to the underexplored context of BDA implementation in SMEs, highlighting the dynamic capabilities managers must develop to succeed. Practically, it provides actionable insights for SMEs managers navigating digital transformation in resource-constrained settings. The study proposes a roadmap to guide BDA adoption in manufacturing SMEs. | |
| dc.identifier.citation | Production Planning and Control, ISSN: 0953-7287 (Print); 1366-5871 (Online), Taylor and Francis Group, ahead-of-print(ahead-of-print), 1-33. doi: 10.1080/09537287.2025.2561966 | |
| dc.identifier.doi | 10.1080/09537287.2025.2561966 | |
| dc.identifier.issn | 0953-7287 | |
| dc.identifier.issn | 1366-5871 | |
| dc.identifier.uri | http://hdl.handle.net/10292/20939 | |
| dc.language | en | |
| dc.publisher | Taylor and Francis Group | |
| dc.relation.uri | https://www.tandfonline.com/doi/full/10.1080/09537287.2025.2561966 | |
| dc.rights | © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | 35 Commerce, Management, Tourism and Services | |
| dc.subject | 3503 Business Systems In Context | |
| dc.subject | 3507 Strategy, Management and Organisational Behaviour | |
| dc.subject | 3509 Transportation, Logistics and Supply Chains | |
| dc.subject | 46 Information and Computing Sciences | |
| dc.subject | 4609 Information Systems | |
| dc.subject | Data Science | |
| dc.subject | Generic health relevance | |
| dc.subject | 9 Industry, Innovation and Infrastructure | |
| dc.subject | Operations Research | |
| dc.subject | Big data analytics | |
| dc.subject | SMEs | |
| dc.subject | resource orchestration theory | |
| dc.subject | managers’ role | |
| dc.title | Orchestrating Resources for Big Data Analytics Implementation in Manufacturing SMEs: Insights Into Managerial Role and Engagement | |
| dc.type | Journal Article | |
| pubs.elements-id | 630597 |
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