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Purpose Manufacturers find bottom of the pyramid (BOP) markets challenging to serve due to low margins and highly localized needs. As such, residents in BOP markets often go without products commonly available in developed countries. Going without medical equipment may negatively affect healthcare services. This study develops a supply chain design strategy that supports the production of medical equipment by preserving variety flexibility at low volumes that stands to create new market opportunities for manufacturers and improve healthcare for residents in BOP markets. Design/methodology/approach The authors introduce a mass-customization model called options-based planning (OBP) which offers a framework to both leverage the efficiencies of high volume production models and provide products that are customized to local market needs. An empirical simulation, grounded in data collected from a large international manufacturer of magnetic resonance imaging (MRI) equipment, illustrates how an OBP production strategy will likely perform under BOP conditions and facilitate the delivery of healthcare equipment to BOP markets. Findings OBP provides a means for manufacturers to provide the customization necessary to serve fragmented BOP markets, while enabling higher production volume to make serving these markets more feasible. The empirical simulation reveals the relative benefits of OBP under conditions of forecast uncertainty, product complexity (number of design parameters) and different levels of responsiveness. Social implications Increased access to modern medical equipment should improve healthcare outcomes for consumers in BOP markets. Originality/value The MRI context in BOP markets serves to illustrate the value of the OBP model for manufacturers.

Purpose This study aims to introduce a new prescriptive model to aid both managers and researchers in partner selection for innovation-orientated collaboration. This framework demonstrates how prospective partner firms’ complementing bodies of knowledge and goal alignment interact to affect the success of a collaboration. Design/methodology/approach The authors use geometric modeling to represent the interrelationships among knowledge similarity/dissimilarity, goal congruence, knowledge complementarity (KC) and innovation in alliance formation. Using this model as a framework, the authors derive relationships among predictors of innovation success and determine how they affect the nature of partnerships under varying conditions of KC. Findings This research shows how innovation success is strongly determined by partner selection. Specifically, the authors examine the influence of KC and partner goals on three aspects of a potential research and development (R&D) alliance – the potential level of innovation outcome for the alliance, the boundaries of knowledge sharing and limitations arising from knowledge and goal incongruence and the nature of cooperation. Originality/value Although there is broad empirical support that innovation success is influenced by the similarity of R&D partners’ knowledge, further research is still needed to model the relationship more precisely between partner KC and goal alignment. The authors address this gap by developing a model that is both prescriptive and predictive of how innovation success can be achieved in the context of disparate but complementing knowledge and goal sets. The authors conclude with practical implications for practice and future research directions.

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