In agile supply chains, dependencies in demand for products (in particular correlations) as well as substitution among products, vary substantially, and, due to uncertainty in market acceptance, a substantial share of the portfolio item demands follow bimodal distributions. Typically, advanced heuristics and major simplifying assumptions on these dependencies are needed to reduce the complexity to an appropriate level for analytical solutions of models. By applying a single-period stochastic model to the multi-item substitutable newsvendor problem, we demonstrate that simplifying assumptions on distributions and dependencies can lead to rather poor solutions, and as a consequence, numerical models – despite their obvious inability to produce general data-independent results – have an important role to play in assortment planning. By using a brand name sportswear assortment problem, we show that even when technology and supply chain flexibility allows for continuous information and production updates, the underlying distributional and dependency assumptions used in the planning models are crucial. We note, though, that the value of substitution is high and compensates, to some extent for the lack of information. We have found that the expected profit can drop by as much as 30% when simplifications are applied.