Abstract
Starch-containing foods such as bread, pastries, and cakes are usually baked at a
moderately high temperature in an oven. When these products are later exposed to room
temperature, the associated gelatinized starch begins to harden which causes
retrogradation and molecular realignment. Due to this circumstance, manufacturers
need to have a fairly accurate estimate of products demand in order to determine the
precise amount of baking powder and additives for use in their production so as not to
incur losses in their business arising from the stale and consequentially unsalable
products. This research was therefore focused on selecting the best forecasting model
using a prominent confectionery firm in Abeokuta, Ogun State, Nigeria as a case study.
The study was based on 24-week operational period sales data collected from the
company. The moving average model and the exponential smoothing model were the
two forecasting models considered in this research. The data obtained was thoroughly
reviewed and the results of the forecasting models were compared. The most effective
model was the exponential smoothing model as it produced the lowest mean absolute
percentage error on the average of 3.7347 for the cumulative days of sales under review
as against the 15.1713 for the moving average model. However, the exponential
smoothing model was considered the best forecasting model for minimizing forecasting
error in this study.Starch-containing foods such as bread, pastries, and cakes are usually baked at a
moderately high temperature in an oven. When these products are later exposed to room
temperature, the associated gelatinized starch begins to harden which causes
retrogradation and molecular realignment. Due to this circumstance, manufacturers
need to have a fairly accurate estimate of products demand in order to determine the
precise amount of baking powder and additives for use in their production so as not to
incur losses in their business arising from the stale and consequentially unsalable
products. This research was therefore focused on selecting the best forecasting model
using a prominent confectionery firm in Abeokuta, Ogun State, Nigeria as a case study.
The study was based on 24-week operational period sales data collected from the
company. The moving average model and the exponential smoothing model were the
two forecasting models considered in this research. The data obtained was thoroughly
reviewed and the results of the forecasting models were compared. The most effective
model was the exponential smoothing model as it produced the lowest mean absolute
percentage error on the average of 3.7347 for the cumulative days of sales under review
as against the 15.1713 for the moving average model. However, the exponential
smoothing model was considered the best forecasting model for minimizing forecasting
error in this study.