Using a Learning Factory Approach to Transfer Industrie 4.0 Approaches to Small- and Medium-sized Enterprises

Wank, Andreas; Adolph, Siri; Anokhin, Oleg; Arndt, Alexander; Anderl, Reiner; Metternich, Joachim

doi:10.1016/j.procir.2016.05.068

Cited by 56 publications

(44 citation statements)

References 4 publications

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“…Seven relevant and representative contributions in this field without an explicit focus on SME but a rather general applicability could be identified. These gave a good overview of relevant process steps [7,14,27,28,41,55,57,60]. The idea of systematically tackling the DT with procedure models has become a common way in the respective research domain (see below).…”

Section: Models For Digital Transformation In Smesmentioning

confidence: 99%

“…Through the literature review on SME specific procedure models, 15 relevant contributions could be identified [10,18,19,23,25,31,33,35,38,42,46,47,49,55,58]. The identified articles differ in various ways and focus on different aspects.…”

Section: Models For Digital Transformation In Smesmentioning

confidence: 99%

“…If any, the collaboration with external entities is sometimes mentioned [18,19,23,42]. Only two publications of support units shortly mention their abstract steps to aid digitalization efforts in specific domains [33,55].…”

Section: Models For Digital Transformation In Smesmentioning

confidence: 99%

“…While being free of charge, these help SMEs to increase their awareness of digital innovation potentials in workshops, teach the fundamental skills in training and support the implementation of selected innovations in projects [e.g. 33,55].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Supporting Digital Transformation in Small and Medium-sized Enterprises: A Procedure Model Involving Publicly Funded Support Units

Barann¹,

Hermann²,

Cordes³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Digital Transformation (DT) considers the continuous digitalization process of a company, which uses digital and data-driven innovation to improve existing processes, change distinct business model (BM) elements, or reinvent its BM entirely. Large companies position themselves at the frontline of the DT, while small and medium-sized enterprises (SMEs) are challenged by resource constraints and missing guidance on realizing benefits of DT. This situation threatens SMEs as big players increasingly utilize DT to enter markets that have been traditionally reserved to SMEs. Extant research on the ways SMEs can effectively participate in DT is limited. Against this background, this article builds upon prior research and a focus group discussion to propose a procedure model, which enables DT in SME by involving publicly funded support units. These units help SMEs in understanding and structuring the potentials of digital and data-driven innovation.

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Section: Models For Digital Transformation In Smesmentioning

confidence: 99%

Section: Models For Digital Transformation In Smesmentioning

confidence: 99%

Section: Models For Digital Transformation In Smesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Supporting Digital Transformation in Small and Medium-sized Enterprises: A Procedure Model Involving Publicly Funded Support Units

Barann¹,

Hermann²,

Cordes³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…Huberty (2015) and Quinton and Simkin (2016) motivate the fact that replacing paper and manual processes with softwarebased solutions allows to automatically and quickly collect data that can be mined to better understand the rick causes and the process performance. Finally, Iivari et al (2016) and Wank et al (2016) highlight the importance of the user interfaces which a digitalization process has to be equipped with, where real-time reports and dashboards on digital-process performance allow managers to address problems before they become critical.…”

Section: State Of the Artmentioning

confidence: 99%

Toward Industry 4.0 With IoT: Optimizing Business Processes in an Evolving Manufacturing Factory

et al. 2019

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Research advances in the last decades have allowed the introduction of Internet of Things (IoT) concepts in several industrial application scenarios, leading to the so-called Industry 4.0 or Industrial IoT (IIoT). The Industry 4.0 has the ambition to revolutionize industry management and business processes, enhancing the productivity of manufacturing technologies through field data collection and analysis, thus creating real-time digital twins of industrial scenarios. Moreover, it is vital for companies to be as "smart" as possible and to adapt to the varying nature of the digital supply chains. This is possible by leveraging IoT in Industry 4.0 scenarios. In this paper, we describe the renovation process, guided by things2i s.r.l., a cross-disciplinary engineering-economic spin-off company of the University of Parma, which a real manufacturing industry is undergoing over consecutive phases spanning a few years. The first phase concerns the digitalization of the control quality process, specifically related to the company's production lines. The use of paper sheets containing different quality checks has been made smarter through the introduction of a digital, smart, and Web-based application, which is currently supporting operators and quality inspectors working on the supply chain through the use of smart devices. The second phase of the IIoT evolution-currently ongoing concerns both digitalization and optimization of the production planning activity, through an innovative Web-based planning tool. The changes introduced have led to significant advantages and improvement for the manufacturing company, in terms of: (i) impressive cost reduction; (ii) better products quality control; (iii) real-time detection and reaction to supply chain issues; (iv) significant reduction of the time spent in planning activity; and (v) resources employment optimization, thanks to the minimization of unproductive setup times on production lines. These two renovation phases represent a basis for possible future developments, such us the integration of sensor-based data on the operational status of production machines and the currently available warehouse supplies. In conclusion, the Industry 4.0-based ongoing digitization process guided by things2i allows to continuously collect heterogeneous Human-to-Things (H2T) data, which can be used to optimize the partner manufacturing company as a whole entity.

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Autonomous and IoT‐driven intralogistics for Industry 4.0 warehouses: A thematic analysis of the literature

Grover,

Ashraf

2024

Transp. j.

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Eyeing superior operational performance, transportation, and warehousing management, researchers have turned their much‐needed attention to autonomous and IoT‐driven intralogistics systems. Despite its potential, a systematic evaluation of its overall business needs and the criteria for its success at each stage of adoption is missing in the literature. Using the business analysis framework, augmented by the technology adoption model, this study seeks to provide the business context for adopting autonomous and IoT‐driven intralogistics by identifying business requirements and critical success factors for such systems. We thematically analyze 85 recent research articles on autonomous and IoT‐driven intralogistics systems to identify business requirements that are linked to the mission of maximizing operational profit for the warehousing and storage industry. Then, using the identified business requirements as a base, we thematically analyze those 85 research articles again to identify critical success factors at different stages of technology adoption, namely information, analysis, acquisition, and utilization. We use the findings to develop propositions for future researchers. These findings provide a foundation for developing empirical, descriptive, and normative research on adopting and managing these systems for the warehousing and storage industry.

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Using a Learning Factory Approach to Transfer Industrie 4.0 Approaches to Small- and Medium-sized Enterprises

Cited by 56 publications

References 4 publications

Supporting Digital Transformation in Small and Medium-sized Enterprises: A Procedure Model Involving Publicly Funded Support Units

Supporting Digital Transformation in Small and Medium-sized Enterprises: A Procedure Model Involving Publicly Funded Support Units

Toward Industry 4.0 With IoT: Optimizing Business Processes in an Evolving Manufacturing Factory

Autonomous and IoT‐driven intralogistics for Industry 4.0 warehouses: A thematic analysis of the literature

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