An early-stage decision-support framework for the implementation of intelligent automation

The constant pressure on manufacturing companies to improve productivity, reduce the lead time and progress in quality requires new technological developments and adoption.The rapid development of smart technology and robotics and autonomous systems (RAS) technology has a profound impact on manufacturing automation and might determine winners and losers of the next generation’s manufacturing competition. Simultaneously, recent smart technology developments in the areas enable an automation response to new production paradigms such as mass customisation and product-lifecycle considerations in the context of Industry 4.0. New paradigms, like mass customisation, increased both the complexity of the tasks and the risk due to smart technology integration. From a manufacturing automation perspective, intelligent automation has been identified as a possible response to arising demands. The presented research aims to support the industrial uptake of intelligent automation into manufacturing businesses by quantifying risks at the early design stage and business case development. An early-stage decision-support framework for the implementation of intelligent automation in manufacturing businesses is presented in this thesis.The framework is informed by an extensive literature review, updated and verified with surveys and workshops to add to the knowledge base due to the rapid development of the associated technologies. A paradigm shift from cost to a risk-modelling perspective is proposed to provide a more flexible and generic approach applicable throughout the current technology landscape. The proposed probabilistic decision-support framework consists of three parts:• A clustering algorithm to identify the manufacturing functions in manual processes from task analysis to mitigate early-stage design uncertainties• A Bayesian Belief Network (BBN) informed by an expert elicitation via the DELPHI method, where the identified functions become the unit of analysis.• A Markov-Chain Monte-Carlo method modelling the effects of uncertainties on the critical success factors to address issues of factor interdependencies after expert elicitation.Based on the overall decision framework a toolbox was developed in Microsoft Excel. Five different case studies are used to test and validate the framework. Evaluation of the results derived from the toolbox from the industrial feedback suggests a positive validation for commercial use. The main contributions to knowledge in the presented thesis arise from the following four points:• Early-stage decision-support framework for business case evaluation of intelligent automation.• Translating manual tasks to automation function via a novel clustering approach• Application of a Markov-Chain Monte-Carlo Method to simulate correlation between decision criteria• Causal relationship among Critical Success Factors has been established from business and technical perspectives.The implications on practise might be promising. The feedback arising from the created tool was promising from the industry, and a practical realisation of the decision-support tool seems to be desired from an industrial point of view.With respect to further work, the decision-support tool might have established a ground to analyse a human task automatically for automation purposes. The established clustering mechanisms and the related attributes could be connected to sensorial data and analyse a manufacturing task autonomously without the subjective input of task analysis experts. To enable such an autonomous process, however, the psychophysiological understanding must be increased in the future.</div

Innovation landscape and challenges of smart technologies and systems - a European perspective

Latest developments in smart sensor and actuator technologies are expected to lead to a revolution in future manufacturing systems’ abilities and efficiency, often referred to as Industry 4.0. Smart technologies with higher degrees of autonomy will be essential to achieve the next breakthrough in both agility and productivity. However, the technologies will also bring substantial design and integration challenges and novelty risks to manufacturing businesses. The aim of this paper is to analyse the current landscape and to identify the challenges for introducing smart technologies into manufacturing systems in Europe. Expert knowledge from both industrial and academic practitioners in the field was extracted using an online survey. Feedback from a workshop was used to triangulate and extend the survey results. The findings indicate three main challenges for the ubiquitous implementation of smart technologies in manufacturing are: i) the perceived risk of novel technologies, ii) the complexity of integration, and iii) the consideration of human factors. Recommendations are made based on these findings to transform the landscape for smart manufacturing

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)

Supplementary information files for 'Innovation Landscape and Challenges of Smart Technologies and Systems – A European Perspective'

Supplementary information files for 'Innovation Landscape and Challenges of Smart Technologies and Systems – A European Perspective'Abstract:Latest developments in smart sensor and actuator technologies are expected to lead to a revolution in future manufacturing systems’ abilities and efficiency, often referred to as Industry 4.0. Smart technologies with higher degrees of autonomy will be essential to achieve the next breakthrough in both agility and productivity. However, the technologies will also bring substantial design and integration challenges and novelty risks to manufacturing businesses. The aim of this paper is to analyse the current landscape and to identify the challenges for introducing smart technologies into manufacturing systems in Europe. Expert knowledge from both industrial and academic practitioners in the field was extracted using an online survey. Feedback from a workshop was used to triangulate and extend the survey results. The findings indicate three main challenges for the ubiquitous implementation of smart technologies in manufacturing are: i) the perceived risk of novel technologies, ii) the complexity of integration, and iii) the consideration of human factors. Recommendations are made based on these findings to transform the landscape for smart manufacturing.</div

A transformation of human operation approach to inform system design for automation

Design of automation system relies on experts’ knowledge and experience accumulated from past solutions. In designing novel solutions, however, it is difficult to apply past knowledge and achieve design right-first-time, therefore wasting valuable resources and time. SADT/IDEF0 models are commonly used by automation experts to model manufacturing systems based on the manual process. However, function generalisation without benchmarking is difficult for experts particularly for complex and highly skilled-based tasks. This paper proposes a functional task abstraction approach to support automation design specification based on human factor attributes. A semi-automated clustering approach is developed to identify key functions from an observed manual process. The proposed approach is tested on five different automation case studies. The results indicate the proposed method reduces inconsistency in task abstraction when compared to the current approach that relies on the experts, which are further validated against the solutions generated by automation experts

Supplementary information files for 'A variability taxonomy to support automation decision-making for manufacturing processes'

Supplementary information files for 'A variability taxonomy to support automation decision-making for manufacturing processes'Abstract:Although many manual operations have been replaced by automation in the manufacturing domain, in various industries skilled operators still carry out critical manual tasks such as final assembly. The business case for automation in these areas is difficult to justify due to increased complexity and costs arising out of process variabilities associated with those tasks. The lack of understanding of process variability in automation design means that industrial automation often does not realise the full benefits at the first attempt, resulting in the need to spend additional resource and time, to fully realise the potential. This article describes a taxonomy of variability when considering automation of manufacturing processes. Three industrial case studies were analysed to develop the proposed taxonomy. The results obtained from the taxonomy are discussed with a further case study to demonstrate its value in supporting automation decision-making.</div