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Purpose The purpose of this paper is to present research work based on the authors’ conceptual framework reported in the VRAP Conference 2013. It is related with an efficient method to obtain an optimal part build orientation for additive manufacturing (AM) by using AM features with associated AM production knowledge and multi-attribute decision-making (MADM). The paper also emphasizes the importance of AM feature and the implied AM knowledge in AM process planning. Design/methodology/approach To solve the orientation problem in AM, two sub-tasks, the generation of a set of alternative orientations and the identification of an optimal one within the generated list, should be accomplished. In this paper, AM feature is defined and associated with AM production knowledge to be used for generating a set of alternative orientations. Key attributes for the decision-making of the orientation problem are then identified and used to represent those generated orientations. Finally, an integrated MADM model is adopted to find out the optimal orientation among the generated alternative orientations. Findings The proposed method to find out an optimal part build orientation for those parts with simple or medium complex geometric shapes is reasonable and efficient. It also has the potential to deal with more complex parts with cellular or porous structures in a short time by using high-performance computers. Research limitations/implications The proposed method is a proof-of-concept. There is a need to investigate AM feature types and the association with related AM production knowledge further so as to suite the context of orientating parts with more complex geometric features. There are also research opportunities for developing more advanced algorithms to recognize AM features and generate alternative orientations and refine alternative orientations. Originality/value AM feature is defined and introduced to the orientation problem in AM for generating the alternative orientations. It is also used as one of the key attributes for decision-making so as to help express production requirements on specific geometric features of a desired part.

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International audienceCompetition from low wage countries and the adoption of free market strategies have forced manufacturing firms to recognise and implement productivity enhancement strategies. This research defines a holistic sustainability index embedding several performance indices. The aim of this study was to establish a relevant framework that would assess the current situation of an industry through aggregation of environmental, social, economical as well as manufacturing variables. The proposition has its roots in trends and gaps in the sustainability literature of manufacturing industries and is based on the analytic hierarchy process (AHP) method. A list of indicators measuring the industry performance based on an AHP scoring methodology is proposed. The next stages include grouping industries according to common deficiencies across the four dimensions and establishing a cooperation framework. The food manufacturing industry is the main target in this study and will benefit from adopting sustainable long-term policies. By recognising the importance of social–environmental sustainability and taking the initiative to pursue it, profits will grow as a positive effect of such policies. The added value is twofold: (1) coupling all sustainability dimensions, often addressed in silos and (2) integrating manufacturing indicators which enable the analysis of interrelationships with sustainability