Prioritising Low Cost Digital Solutions Required by Manufacturing SMEs: A Shoestring Approach

Schönfuß, Benjamin; McFarlane, Duncan; Salter, Liz; Silva, Lavindra de; Ratchev, Svetan

doi:10.1007/978-3-030-27477-1_22

Cited by 16 publications

(17 citation statements)

References 8 publications

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Section: Predictive Data Analyticsmentioning

confidence: 99%

“…It is shown in Figure 14 that fault prediction received major research attention in the periods 2018-2019 [66] and 2020-2022 [27]. In the period 2018-2019, accuracy improvement, predictive maintenance, and encapsulation of dynamicity [3,66] are stated as the future research directions (Figure 17). Following this research, path degradation prediction [40], resource availability prediction [41], and machine availability prediction [24] were implemented in the period 2020-2022 (Figure 14) In this period, resilience of manufacturing systems, accuracy improvement [54], fault prediction [41], and detailed encapsulation of dynamicity [67] were stated as the future research directions.…”

Section: Predictive Data Analyticsmentioning

confidence: 99%

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Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview

2022

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The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have recently been published. However, there is still a lack of a systematic literature review on different aspects of ML-based DT in the manufacturing industry from a bibliometric and evolutionary perspective. Therefore, the proposed study is mainly aimed at reviewing DT in the manufacturing industry to identify the contribution of ML, current methods, and future research directions. According to the findings, the contribution of ML to this domain is significant. Additionally, the results show that the latest ML technologies are being used in the DT domain; neural networks have evolved based on application-specific requirements. The total number of papers and citations per paper on ML-based DT is increasing. The relevance of ML in DT has increased over time. The current trend is to use ML-based DT for data analytics. Additionally, there are many unfilled gaps; certain gaps include industrial applications of DT, synchronisation with real-time data through sensors, heterogeneous data management, and benchmarking.

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Section: Predictive Data Analyticsmentioning

confidence: 99%

Section: Predictive Data Analyticsmentioning

confidence: 99%

Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview

2022

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“…To identify the barriers to adopt analytics solutions (RQ1), it is essential to study the needs of SMEs. High-priority analytics solution areas are identified by starting from the work presented in Schönfuß et al (2020), who studied the digitalisation requirements of manufacturing SMEs in the UK and provided a catalogue of (general) digital solution areas. Based on this catalogue, six analytics solution areas are identified, which we deemed to require data analysis or ML: (1) capacity monitoring of human and machine resources, (2) gathering and analysis of product or customer demand, (3) cost modelling of disruptions and changes, (4) predictive maintenance, (5) automated quality inspection, and (6) automated bottleneck identification in operations.…”

Section: Sme Requirementsmentioning

confidence: 99%

“…Therefore, developing low-cost analytics solutions for manufacturing SMEs pose specific challenges that need to be addressed: (1) there is a lack of approaches which aim to minimise the expenditure of the modelling and data preparation phase of developing a ML solution, (2) a methodology is needed for analysing the requirements of SMEs to identify appropriate methods for low-cost analytics solutions, and (3) besides monitoring applications, which constitute the majority of current analytics approaches, SMEs require low-cost analytics solutions in areas that are underrepresented thus far (Schönfuß et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Towards low-cost machine learning solutions for manufacturing SMEs

et al. 2021

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Machine learning (ML) is increasingly used to enhance production systems and meet the requirements of a rapidly evolving manufacturing environment. Compared to larger companies, however, small- and medium-sized enterprises (SMEs) lack in terms of resources, available data and skills, which impedes the potential adoption of analytics solutions. This paper proposes a preliminary yet general approach to identify low-cost analytics solutions for manufacturing SMEs, with particular emphasis on ML. The initial studies seem to suggest that, contrarily to what is usually thought at first glance, SMEs seldom need digital solutions that use advanced ML algorithms which require extensive data preparation, laborious parameter tuning and a comprehensive understanding of the underlying problem. If an analytics solution does require learning capabilities, a ‘simple solution’, which we will characterise in this paper, should be sufficient.

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“…Manufacturing leaders are already taking a lead in their Industry 4.0 journeys as they have the sufficient infrastructure and resources to continue improvements [9]. However, the majority of small and medium enterprises (SMEs) in manufacturing sector who represent the backbone of the manufacturing industry have less mature journeys [21][22][23] Several industrial reports and research papers suggest that the lack of clear strategy and vison available to SMEs are amongst the top challenges for Industry 4.0 adoption [6,15,16,[21][22][23][24][25][26]. The lack of clear strategy subsequently highlights (i) lack of awareness of industrial digital technologies associated with Industry 4.0 [16,27] (ii) lack of real-world applications demonstrating the benefits and the potential of the available opportunities [15,28] (iii) the complexity of Industry 4.0 technologies and various terminologies used to describe it making it difficult to understand [29] (iv) lack of trusted advice and the know-how of integration [17,21] and (v) lack of prioritized knowledge of where to start and how to apply it to align with the business strategy [9,24].…”

Section: Introductionmentioning

confidence: 99%

Six-Gear Roadmap towards the Smart Factory

et al. 2021

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The fourth industrial revolution is the transformation of industrial manufacturing into smart manufacturing. The advancement of digital technologies that make the trend Industry 4.0 are considered as the transforming force that will enable this transformation. However, Industry 4.0 digital technologies need to be connected, integrated and used effectively to create value and to provide insightful information for data driven manufacturing. Smart manufacturing is a journey and requires a roadmap to guide manufacturing organizations for its adoption. The objective of this paper is to review different methodologies and strategies for smart manufacturing implementation to propose a simple and a holistic roadmap that will support the transition into smart factories and achieve resilience, flexibility and sustainability. A comprehensive review of academic and industrial literature was preformed based on multiple stage approach and chosen criteria to establish existing knowledge in the field and to evaluate latest trends and ideas of Industry 4.0 and smart manufacturing technologies, techniques and applications in the manufacturing industry. These criteria are sub-grouped to fit within various stages of the proposed roadmap and attempts to bridge the gap between academia and industry and contributes to a new knowledge in the literature. This paper presents a conceptual approach based on six stages. In each stage, key enabling technologies and strategies are introduced, the common challenges, implementation tips and case studies of industrial applications are discussed to potentially assist in a successful adoption. The significance of the proposed roadmap serve as a strategic practical tool for rapid adoption of Industry 4.0 technologies for smart manufacturing and to bridge the gap between the advanced technologies and their application in manufacturing industry, especially for SMEs.

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Prioritising Low Cost Digital Solutions Required by Manufacturing SMEs: A Shoestring Approach

Cited by 16 publications

References 8 publications

Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview

Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview

Towards low-cost machine learning solutions for manufacturing SMEs

Six-Gear Roadmap towards the Smart Factory

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