Digitalisation is changing the manufacturing landscape with promises to enhance industrial competitiveness with new technologies and business approaches. Various data-driven applications, enabled by digital technologies, can support process monitoring, production quality control, smart planning, and optimisation by making relevant data available and accessible to different roles in production. In this context, analytics is a relevant tool for improved decision-making for production activities since it entails extracting insights from data to create value for decision-makers. However, previous research has identified a lack of guidelines to manage the technological implementation needed for analytics. Furthermore, there are few studies in a real manufacturing setting that describe how companies are exploiting analytics. To address this gap, the purpose of this study is to investigate the implementation and use of analytics for production activities in the manufacturing industry. To fulfil the purpose of the study, the following research questions were formulated: RQ1: What does the adoption of analytics look like and what results can it bring to production activities of a manufacturing company? RQ2: What are the challenges and enablers for analytics adoption in production activities of a manufacturing company? This study was based on a literature review in addition to a single case study in a large multinational machinery manufacturing company. Data collection included observations and semi-structured interviews about three analytics use cases: for production performance follow-up, production disturbances tracking and production planning and scheduling. The first use case was based on the Design Thinking process and tools while the other two cases were narrower in scope and do not cover the development process in detail. Qualitative data analysis was the method used to examine the empirical and theoretical data. The empirical findings indicate that analytics solutions for production activities do not need to be sophisticated and characterised by high automation and complexity to bring meaningful value to manufacturing companies. The three analytics use cases investigated improved effectiveness and efficiency of production performance follow-up, production disturbances and production planning and scheduling activities. The main contributor to these benefits was a higher level of transparency of the factory manufacturing operations, which in turn aids collaboration, preventive decision-making, prioritization and better resource allocation. The identified challenges for analytics adoption were related to information system challenges and people & organization challenges. In other to address these challenges, this study suggests that manufacturing companies should focus on securing sponsorship from senior management and leadership, implementing cultural change to embrace fact-based decisions, training the existing workforce in analytics skills and empowering and recruiting people with digital skills. Moreover, it is recommended that manufacturing companies integrate information systems vertically and horizontally, link and aggregate data to deliver contextualised information to different roles and finally, invest in data-related Industry 4.0 technologies to capture, transfer, store, and process manufacturing data efficiently.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mdh-60061 |
| Date | January 2022 |
| Creators | Cupertino Ribeiro, Junia |
| Publisher | Mälardalens universitet, Innovation och produktrealisering |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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