Influencing factors for implementing automation in manufacturing businesses – a literature review

The latest developments in Robotics and Autonomous Systems (RAS) are expected to lead to a transformation of future production systems’ capabilities and productivity. While increased human-robot collaboration as well as higher degrees of autonomous systems within a manufacturing context will be essential to achieve the next breakthrough in both agility as well as productivity, they will pose significant new challenges for how production systems are planned and engineered to maximise the potential and minimise the risks of this new technology for manufacturing businesses. Therefore, a main focus of this review was on determining the critical success factors for the implementation of RAS and on gaining a deeper understanding of the current research focus. The research results lead to a broader discussion of the implications arising from future automation and human-robot collaboration which highlights the current limitation of decision making criteria considered in the current literature. The results of the review have been quantitatively verified with the use of the text mining tool WordSmith Tool (v7.0).

[1]  Birgit Vogel-Heuser,et al.  Industrie 4.0 in Produktion, Automatisierung und Logistik , 2014 .

[2]  Anna Granlund,et al.  Competitive Internal Logistics Systems through Automation , 2011 .

[3]  Siba Sankar Mahapatra,et al.  Multi-criteria decision making towards selection of industrial robot: Exploration of PROMETHEE II method , 2015 .

[4]  S. Vinodh,et al.  A hybrid MCDM approach for agile concept selection using fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS , 2016 .

[5]  David J. Adler,et al.  Life cycle cost and benefits of process automation in bulk pharmaceuticals , 1995 .

[6]  Richard Crowson Assembly processes : finishing, packaging, and automation , 2006 .

[7]  Nourredine Boubekri,et al.  Development of an expert system for industrial robot selection , 1991 .

[8]  Mao-Jiun J. Wang,et al.  A fuzzy multi-criteria decision-making approach for robot selection , 1993 .

[9]  Pedro Ferreira,et al.  Symbiotic assembly systems – A new paradigm , 2014 .

[10]  Chris A. McMahon,et al.  Uncertainty in Through-Life Costing–-Review and Perspectives , 2010, IEEE Transactions on Engineering Management.

[11]  R. Parameshwaran,et al.  An integrated fuzzy MCDM based approach for robot selection considering objective and subjective criteria , 2015, Appl. Soft Comput..

[12]  Hsu-Shih Shih,et al.  Incremental analysis for MCDM with an application to group TOPSIS , 2008, Eur. J. Oper. Res..

[13]  Jack R. Meredith,et al.  Implementing the automated factory , 1987 .