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Demand forecasting is different from traditional forecasting because it is a process of forecasting multiple time series collectively. It is challenging to implement models that can generalise and perform well while forecasting many time series altogether, based on accuracy and scalability. Moreover, there can be external influences like holidays, disasters, promotions, etc., creating drifts and structural breaks, making accurate demand forecasting a challenge. Again, these external features used for multivariate forecasting often worsen the prediction accuracy because there are more unknowns in the forecasting process. This paper attempts to explore effective ways of leveraging the exogenous regressors to surpass the accuracy of the univariate approach by creating synthetic scenarios to understand the model and regressors’ performances. This paper finds that the forecastability of the correlated external features plays a big role in determining whether it would improve or worsen accuracy for models like ARIMA, yet even 100% accurately forecasted extra regressors sometimes fail to surpass their univariate predictive accuracy. The findings are replicated in cases like forecasting weekly docked bike demand per station every hour, where the multivariate approach outperformed the univariate approach by forecasting the regressors with Bi-LSTM and using their predicted values for forecasting the target demand with ARIMA.