Nowadays, Industry 4.0 (I4.0) has become a trendy topic in manufacturing industries worldwide. The definition is far from being comprehensible for small players, and the practical uptake is ambiguous. Transnational companies are often at the top in deploying I4.0 features, learning primarily from their experimentation. Alternatively, small- to medium-sized enterprises (SMEs), given their less stable value chains and unsteady processes, tend to target most of their efforts on controlling disturbances and adopting solutions for deviation control. Such solutions can be features that set the path for SMEs to transition to I4.0. This paper aims to examine the reported degree of digitalization in implemented solutions in SMEs when handling deviations and analyze the integration of such solutions in their digital transformation process. Systematic literature review (SLR) is used to examine literature published up to and including January 2019. The results show a higher concentration on practical applications rather than on frameworks. Existing frameworks that focus on SMEs address particular elements of I4.0 rather than a gradual shift with a holistic view, increasing the deployment difficulty for SMEs. This paper identifies potential constraints in deployment if such a trend maintains for consecutive years.
Production system lifecycle includes phases ranging from concept pre-study to ramp-up and operations. Manufacturing companies often face challenges to reach operational performance targets during ramp-up time and operation phase. The design phase is considered crucial as major decisions related to the future production system are taken during this phase. There is an opportunity to utilize the production system design phase to improve the operational performance during both the ramp-up and operation phase. This research aims to identify the critical factors of the design process that affect the performance in the ramp-up and operational phase. A case study was conducted in a pharmaceutical company where a completed project of launching a new production line for a new product was followed in retrospect. Data were collected by conducting interviews with different members involved in the project and the production team on the shop floor. By qualitative data analysis, critical factors affecting the project´s operational performance were identified; such as level of internal technical competency; involvement level of future line manager, operator and project sponsor within the project team; project team´s competency; pre-study of the business case; time pressure to complete the project; expertise of product and process; organization's continuous improvement culture; and relationship with the supplier.
Through visualization, mapping techniques help manufacturing organizations prioritize and guide improvement strategies. For this reason, mapping of the value chain is applied as a method of progress toward lean manufacturing. The purpose of this paper is to illustrate the essence of the material and information flow chart (MIFC) approach, known as value stream mapping (VSM) in the West, to provide a different perspective and understanding and to identify its manner of integration with measurement systems. Metrics complement mapping tools allow the tracking of various stages of an organization's lean journey and continuous improvement (CI). While the time dimension is predominant in performance metrics in lean environments, these metrics do not link the economic factor directly to improvements. The research comprises a case study in which lessons are learned from tool placing and metric determination. Empirical research included critical case sampling and semi-structured interviews, and data were analyzed to compare the conventional Western understanding of VSM with that of a Japanese supplier that learned the principles directly from the source and applied their own version of MIFC. An understanding of the tool based on core knowledge will enable organizations to reevaluate their current measurement systems and choose more suitable ones.
The industry transition towards digital transformation opens the possibilities to utilize data for enhancing sustainability in industrial operations and build capabilities towards resilient and circular operations, i.e., shift towards industry 5.0. This paper explores how data-driven decision-making (DDDM) can enable sustainable and resilient supply chain operations within the manufacturing industry. A series of in-depth interviews were conducted with experts, researchers, and company representatives across the manufacturing industry and universities in Sweden. The findings show a consensus among companies, researchers, and literature about the potential of data utilization for sustainability purposes; however, in most cases, the complete transformation towards data-driven has not happened yet. Companies have uncertainty about what data is needed rather than its lack. Reliability & validity of data become essential to exploit the potential of the data organizations already possess. Based on the literature and interview data, a conceptual model is proposed, including three identified parameters connected to DDDM, 1) data and IT infrastructure, 2) current operations, and 3) an improved triple bottom line performance. The model captures the interconnections between such parameters, depicting the benefits and challenges of DDDM and its relation to more sustainable and resilient supply chain operations within the manufacturing industry. In a data-driven approach, real-time analysis of complex & extensive amounts of data gives unlimited possibilities to improve manufacturing operations through decision-making.
This paper proposes a conceptual implementation model for small and medium enterprises (SMEs) to follow as part of their digitalization implementation. It can later be translated into a practical step by step guide for SMEs to practice during their digital transformation. The model is based on gradually developing industrial capabilities that can influence production processes performance. The model development was based on a critical literature review and a real case industry application. The case data served as direct feedback to the model to assess both the model validity and the actual SMEs needs. The capabilities included in the model are proved to directly influence the performance positively. In comparison with existing models and frameworks, this model envisions the company a full digital shift by proposing an achievable sequence which SMEs in a resource-efficient way could start deploying in compliance with their business needs. SMEs can utilize the capabilities as a foundation for a system that supports continuous improvement in the whole factory.
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