A differentiated approach for justification of advanced manufacturing technologies

Iakymenko, Natalia; Alfnes, Erlend; Thomassen, Maria Kollberg

doi:10.1007/s40436-016-0150-9

Cited by 11 publications

(4 citation statements)

References 33 publications

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“…There hybird models often focus on strategic and ecnomic analysis [8,10,15]. There is also limited empirical evidence to support whether they are actually used by practitioners [11] and their usability has not been analysed. One empirically tested model for automation decision is the "Lean Automation Handbook" [13].…”

Section: Decision Models For Automation Investmentsmentioning

confidence: 99%

“…There exists a vast array of literature on decision models for advanced manufacturing technologies including automation technologies [see for example 9,10,11], but the majority of the decision models are conceptual and not tested emprically. The existing empirically tested models either focus on qualiative analysis [see for example 12,13] or quantitative analysis of ecomony [11]. Only few of them take an overall production system perspective [13].…”

Section: Introductionmentioning

confidence: 99%

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Guide for Automation of Low Volume Production

Löfving

Almström

Jarebrant

et al. 2020

Advances in Transdisciplinary Engineering

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There is a remaining need from both academia and practitioners, to gain further knowledge about the decision making process for automation of low volume production. This paper includes insights of drivers for automation, the development of a guide for low volume production and the outcome of using the guide. The research in this study is based on both empirical data and theoretical considerations. The empirical data was collected in five case studies and a questionnaire. This paper is part of a research project with the main objective to develop knowledge about how flexible automation may contribute to improvements in efficiency, ergonomics, quality and production economics in different industries with low volume production. One of the results in the project was a comprehensive guide, developed, refined and improved in an iterative collaborative process, where tools and parts of the guide were tested and verified by five manufacturing case companies. The paper describes briefly the development process of the guide and content. The requirements of the guide derived from literature, case companies, questionnaire as well as industrial experts. The resulting guide can be used in several ways, depending on the requirements of the application. The guide includes guiding principles, a decision model for the analysis of the company, choice of automation and facts about automation. In the end of the project, four companies had invested or decided to invest in different types of automation.

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Section: Decision Models For Automation Investmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Guide for Automation of Low Volume Production

Löfving

Almström

Jarebrant

et al. 2020

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

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“…This is because reproduction costs of products manufactured by intelligent machines are not volume sensitive (Ramesh, Jyothirmai, and Lavanya 2013). Intelligent automation requires a strategy in order to enhance its adoption (Iakymenko, Alfnes, and Thomassen 2016;Kirk and Tebaldi 1997;Molina et al 2005). Firstly, the goal of intelligent automation is dynamic reconfiguration as a means of responding to changing specifications and achieving mass customisation at the cost of mass production (Molina et al 2005;Phaithoonbuathong et al 2010;Renna and Ambrico 2015;Willis 1998).…”

Section: Manufacturing Automationmentioning

confidence: 99%

Agile manufacturing: an evolutionary review of practices

Gunasekaran

Yusuf

Adeleye

et al. 2018

International Journal of Production Research

126

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Academics and practitioners have long acknowledged the importance of agile manufacturing and related supply chains in achieving firm sustainable competitiveness. However, limited, if any, research has focused on the evolution of practices within agile manufacturing supply chains and how these are related to competitive performance objectives. To address this gap, we reviewed the literature on an agile manufacturing drawing on the evolution of manufacturing agility, attributes of agile manufacturing, the drivers of agile manufacturing, and the identification of the enabling competencies deployable for agile manufacturing. Our thesis is that agile manufacturing is at the centre of achieving a sustainable competitive advantage, especially in light of current unprecedented market instability coupled with complex customer requirements. In this regard, the emphasis which agile manufacturing places on responsive adaptability would counter the destabilising influence of competitive pressures on organisations performance criteria. We have identified five enabling competencies as the agility enablers and practices of agile manufacturing, that is, transparent customisation, agile supply chains, intelligent automation, total employee empowerment and technology integration, and further explored their joint deployment to create positive multiplier effects. Future research directions were also provided with respect to the operationalisation of the five identified enablers and the potential for emergent technologies of big data, blockchain, and Internet of Things to shape future agile manufacturing practices.

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“…Empirically, most of the evidence shows that investments in new technologies drive firms to enhance results (see, for instance, Cozzarin 2016;Dean and Snell 1996;Gordon and Sohal 2001); however, contrary to what is expected, some evidence shows mixed outcomes (Boyer et al 1997;Koc and Bozdag 2009). Different failures in the introduction phase of MTs have been identified; these might include, for example, an unsuitable technological choice for given production processes, inappropriate implementation procedures, or misalignment between the selected technology and the business strategies of the company (Fulton and Hon 2010;Iakymenko, Alfnes, and Thomassen 2016). In spite of these contradictory results, what is also seen is that manufacturing companies continually invest in technology.…”

Section: Manufacturing Technologies Product-process Strategy and Performancementioning

confidence: 99%

Product/process definition, technology adoption and workforce qualification: impact on performance

Bello‐Pintado

García‐Marco

Zouaghi³

2018

International Journal of Production Research

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This paper analyses the impact of manufacturing technologies (MTs) and workers' qualifications on labour productivity and flexibility, taking into account the product-process (P-P) strategy adopted by the company. This allows for a discussion about the well-known P-P matrix initially proposed by Hayes and Wheelwright (1994) in order to evaluate options of production systems. The empirical analysis is performed by means of a panel of data of 13 years for the Spanish manufacturing industry, which includes a total of 7,741 observations. The results indicate a complementary effect between technology and skills to overcome the trade-offs of production systems.

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A differentiated approach for justification of advanced manufacturing technologies

Cited by 11 publications

References 33 publications

Guide for Automation of Low Volume Production

Guide for Automation of Low Volume Production

Agile manufacturing: an evolutionary review of practices

Product/process definition, technology adoption and workforce qualification: impact on performance

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