From Optimality to Robustness: Reframing Product Portfolio Planning

In operations management, product portfolio planning (PPP) is often framed as a combinatorial optimization problem, where organizations strive to optimize their product offerings through various combinations of products and attribute levels. This approach typically operates within an ‘agree-on-assumptions’ paradigm, where a consensus on the likelihood of future states is first established and then used to define an optimal portfolio. The problem, however, is that PPP increasingly occurs in contexts of deep uncertainty, where stakeholders may disagree on the likelihood of alternative futures, the relationships between actions and their consequences, or the desirability of outcomes. In these contexts, the assumptions underpinning a normative best choice among a fixed set of decision alternatives seldom hold, making product portfolios vulnerable to unexpected business environment conditions. Within these contexts, a shift in PPP from a focus on optimality to an emphasis on robustness allows organizations to create product portfolios strategies that perform well across a wide range of plausible futures. In this paper, we build on the ‘agree-on-decisions’ paradigm to conceptually reframe PPP as a decision problem under deep uncertainty, introducing a novel approach — robust PPP — developed through design science research. This approach starts with many alternative portfolio strategies and stress-tests them against a wide range of plausible futures through simulation. The resulting database of simulation runs enables identification of portfolio vulnerabilities and informs the design of potential responses, rather than offering a single best choice based on a limited set of assumptions. We conceptually explore this paradigm shift, developing a foundation for future research and novel practices in PPP.