2 research outputs found
A variability taxonomy to support automation decision-making for manufacturing processes
Although many manual operations have been replaced by automation in the manufacturing domain, in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
A variability taxonomy to support automation decision-making for manufacturing processes
Although many manual operations have been replaced by automation in the manufacturing domain, in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