Improving Sales Forecasting by Combining Key Account Managers’ Inputs and Models Such as SARIMA, LSTM, and Facebook Prophet

Abstract

Sales forecasting is important for a company to plan its production. The quality of its forecasts influences finances and the product availability. The impact of sales forecasts on a company may result on an immobilization of cash flow by causing a high stock level, which is the opposite of out-of-stock impact. The purpose of this study was to find a suitable model for predicting the best company sales forecasts that has a better accuracy or production plan. The proposed method includes an adjustment of the prediction model by including the key account managers’ expertise as qualitative forecasting method. This adjustment was analyzed using different time series forecasting techniques such as exponential smoothing, seasonal autoregressive integrated moving average and Facebook Prophet. These techniques were compared in parallel with neural network approaches such as long-short term memory. Comparisons were made using root mean square error and residual stock to determine whether the forecasts were too optimistic or pessimistic. The proposed model is dynamic. Adjustments of the qualitative inputs could directly influence the proposed values obtained using different quantitative methods.