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When planning production or remanufacturing capacity, it is necessary to use forecasts. In the case of production, demand must be forecasted; in the case of remanufacturing, core supply is also uncertain. In the remanufacturing literature, there are different methods to forecast product returns in the long term, mostly material-flow analysis, system dynamics simulation, and discrete-event simulation. All methods require various assumptions to be taken. The effects of the assumptions are rarely studied, although every assumption adds a source of error. In this paper, we examine which assumptions influence long-term forecasts for remanufacturing capacity planning the most. This can help researchers and practitioners to focus on the most influencing factors and neglect those that would only add complexity without adding value. We examine assumptions concerning new product sales, product composition, product lifetime, return rate, and return quality. Our use case are electric vehicle batteries in Germany from 2022 to 2032. We find that, for the examined period, product quality and return rate have the greatest influence on capacity planning. Assumptions on an age limit for cores or a certain remaining useful life as a quality gate significantly lower the product return quantities and the resulting demand for remanufacturing capacity. The product's lifespan also influences the results, first and foremost regarding the entry point into remanufacturing activities. Sales forecasts affect the results minorly if the timeframe of the forecast and the expected product lifespan are approximately the same.