Industry 4.0: Current practice and challenges in Malaysian manufacturing firms

Tay, S.I.; Alipal, J.; Lee, T.C.

doi:10.1016/j.techsoc.2021.101749

Cited by 54 publications

(40 citation statements)

References 34 publications

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“…This knowledge gap is more significant among small and medium enterprises (SMEs) (Predeep 2019), which constitute 98 per cent of all businesses in Malaysia. Lastly, SMEs are finding the transition to smart manufacturing costly (MIDA 2021; Tay et al 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The adoption of gig employment is expected to enhance the existing mass production, and address the shortages in skills and knowledge. Gig employment for machine management and data processing is expected to increase as employers address the shortages in those skills by recruiting short‐term employees (Tay et al 2021). Moreover, as manufacturing becomes smarter, new challenges will arise.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus, industries will require proactive employees (i.e. gig workers) who are able to resolve issues (Tay et al 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Gig employment in the Malaysian manufacturing industry: a cross‐sectional analysis

Keshminder

Mia

Nourani

et al. 2022

Asian-Pac Economic Lit

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Manufacturing is the primary engine of economic growth in Malaysia. This study uses data on 14,705 manufacturing firms in Malaysia to reveal that technology and labour‐intensive firms have significant negative and positive effects, respectively, on gig employment. Furthermore, firm size and growth are negatively associated with gig employment, while firm age has a positive association with such employment. Interestingly, the location variable indicated that firms in highly industrialised and relatively developed states in Malaysia (e.g. Selangor) are less inclined towards gig worker recruitment. This study provides an essential input to the dearth of literature on the gig economy, especially from the firm perspective. Also, it guides policymakers in designing industrial policies in line with changing employment trends, thereby reducing labour market disruptions.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Gig employment in the Malaysian manufacturing industry: a cross‐sectional analysis

Keshminder

Mia

Nourani

et al. 2022

Asian-Pac Economic Lit

View full text Add to dashboard Cite

show abstract

“…It is expected that the implementation of Industry 4.0 will bring unemployment [74][75][76][77][78][79] to the less qualified workforce, so these companies should be oriented towards alternatives to preserve the income and/or employment of this less favored workforce, reducing inequalities [74][75][76][77][78][79]. It is suggested that social stakeholders such as: Government [95,96], unions (employers and workers), banks, academic institutions, other organizations and institutions, as well as private companies [18,47,72] can join efforts to assist in this process.…”

Section: The Proposed Modelmentioning

confidence: 99%

Implementing Industry 4.0 through Cleaner Production and Social Stakeholders: Holistic and Sustainable Model

Sátyro

Contador

et al. 2021

Sustainability

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Although Industry 4.0 has received much attention in recent years due to the possibility of increasing companies’ productivity, the implementation process is complex. The aim of this study is to present a holistic model for implementing Industry 4.0 based on cleaner production as a fundamental tool for the development of production systems that meet the Sustainable Development 04026-002Goals (SDGs), and social stakeholders that cooperate with this implementation process, helping to develop sustainable infrastructure, processes and technologies to increase the sustainable transformation of these companies towards Industry 4.0. The method used was literature research, and the Delphi technique was used to ask specialists to contribute with their experience to evaluate and propose improvements to the model, in the form of a consensus. The model contributes to the Sustainable Development Goals 9, 12 and 15. This holistic and sustainable model is a contribution to theory and practice, helping executives, technicians, entrepreneurs and those involved with Industry 4.0 to base the implementation process in the needs and specificities of each company, avoiding the “one fits all” models, considering the peculiarities of each company and the complexity of the implementation process in a more efficient and collaborative digital production ecosystems base, seeking to reduce inequalities, through the joint effort of social stakeholders to find ways to restore and/or improve social harmony, impacted by Industry 4.0.

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“…It is undeniable that designing a plan in various sectors of life, such as making designs to implement a security system, education, production system, etc., is quite complicated to do if only relying on limited human capabilities. Therefore, these problems can be solved by using a 4.0 technology-based such as Artificial Intelligence (AI) or the Internet of Things (IoT) Tay et al (2021). Artificial Intelligence (AI) or Internet of Things (IoT) has great and effective capabilities in processing information about a design because they both refer to the big data, they have regarding a particular field so that the resulting data will be predictive, corrective, and in making a design.…”

Section: Introductionmentioning

confidence: 99%

How do we choose the appropriate deep learning method for our case? A systematic literature review

Saputra

Riza

Setiawan

et al. 2022

Preprint

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In the era of technology 4.0, there are many problems in multiple sectors of life that are difficult for humans to solve, ranging from issues in the education quality performance system, difficulties in disease diagnosis, problems in manufacturing systems, construction, food grading, quality control, Etc. Various efforts have been made to solve these problems, from the conventional method of manually retrieving data to obtain the best solution to using a big data-based approach with deep learning. Deep learning has successfully solved problems in various sectors, proving that using big data on deep learning algorithms gives significant results. This systematic review aims to review the studies that have been carried out on applying deep learning to solve or help problems in various sectors. This systematic review shows an overview of deep learning neural networks created in the completion process, the differences in the artificial intelligent methods used, and the advantages and disadvantages of deep learning in various models. It identifies challenges and recommendations for the future. The methods used in this systematic review include search strategies, selecting literature studies, and managing and extracting data. Based on the systematic review results, we know that Convolutional Neural Network (CNN) is the most widely used model for this deep learning algorithm to recognize the feature, along with the image-based data transformation strategy. Finally, deep learning has become very popular because it can transform various data types to get the desired result.

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Industry 4.0: Current practice and challenges in Malaysian manufacturing firms

Cited by 54 publications

References 34 publications

Gig employment in the Malaysian manufacturing industry: a cross‐sectional analysis

Gig employment in the Malaysian manufacturing industry: a cross‐sectional analysis

Implementing Industry 4.0 through Cleaner Production and Social Stakeholders: Holistic and Sustainable Model

How do we choose the appropriate deep learning method for our case? A systematic literature review

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