Purpose: The purpose of this paper is to highlight possible hidden risks when allocating multi-skilled human resources to teams working in a multi-project environment. Are allocation strategies maximizing the use of skills for each project, the only way to improve the chances of all projects being successful? What are the risks in this strategy? What are the available alternatives? Design/methodology/approach: Simulation was used for different allocation strategies to evaluate, using two different metrics, the staffing of human resources in different projects. Three categories of companies were studied, and for each typology, virtual companies were created and several scenarios of collaborators, projects and tasks were simulated to evaluate the staffing process. Findings: It is shown that for different simulations, different allocation strategies and metrics are possible for evaluation and that there is no golden rule of staffing in organizations with multiple projects and with multiple skills collaborators. The staffing is very much dependent on the context of the company. Practical implications: The numerical method provides general managers with a useful tool to enable a better distribution of staff collaborators in teams handling multiple projects that require multi-skilled human resources. This method can also be used to evaluate training needs and hiring strategies, as it presents an overview of all human resources skills and motivations. Originality/value: For academics, the methodology developed enables the study of characteristics of human resources, skills and motivations, which are interesting for team formation. To practitioners, the numerical method is a practical tool for staffing in multiple skills and multiple projects. This tool can also diagnose each company situation regarding current collaborators’ skills and motivations, serving as a tool for training and for hiring.

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Competences,Decision making,Organizational effectiveness,Product development,Simulation,Team management

• Economics and Business - Social Sciences