The impact of high performance computing on technological innovation and scientific discovery is preponderant. Computer simulation, big data analysis, and generative artificial intelligence are often used in trailblazing products or groundbreaking findings. However, an appropriate provisioning of supercomputing resources for the entire spectrum of users is a daunting task. What should be the right hardware to satisfy future needs and demands? We set out to answer that question, particularly in academic environments, where funding schemes are based on availability of grants. The resulting machine, in those institutions, is usually a heterogeneous mix of several architectures and configurations. This paper presents a methodology to guide the next supercomputing purchase based on what is already available and what upcoming needs are anticipated. We use a collection of publicly-available benchmarks and applications to profile a machine. We then use a mathematical model, based on the current profile, to navigate the space of future configurations and suggest future investments. We applied our methodology to Kabré, a small but representative, compute cluster in an academic setting.