The importance of early phase cost estimation is presented and argued.Most appropriate literature cost models to the issue are identified and reviewed.The suitability of literature cost models is assessed and discussed.Relevant cost drivers are identified.A cost estimation outline is proposed to support assembly cost prediction. The aim of this research is to support early phase cost estimation for assembly systems design and automation decision. During this phase, various alternatives of assembly systems with different automation levels can be generated. The alternatives generation is performed using available information on product design, assembly sequences, and planned production information. The issue is to assess given alternatives, identify, and opt for the most appropriate one. Several criteria have to be considered in this decision. The economic aspect represents one of the most preponderant, including cost and profitability prediction. The importance and challenges of this complex issue are highlighted in this paper with feedbacks from manufacturers and the literature. The literature in the field is reviewed, presented, and analyzed. For this sake, a selection of cost models is performed covering a wide chronological range, journals, and fields including assembly and manufacturing models. Classification techniques of cost estimation works are presented and exploited in the proposed review. It is used to filter and discuss models suitability for the defined assembly automation decision issue. The most appropriate models are more thoroughly reviewed and discussed. Useful literature costing techniques, features, and relevant cost drivers are also identified. They cover multiple aspects as production information, resources features or performances. Finally, the review findings are illustrated by a cost estimation outline proposal to support early phase cost prediction for assembly systems design and automation decision.
['A. Salmi', 'P. David', 'Elodie Blanco', 'Joshua D. Summers']
A review of cost estimation models for determining assembly automation level