All manufacturing organizations across all industries face a common problem of balancing supply and demand. This problem is further accentuated in the context of fast-moving consumer goods organizations due to the high demand for product availability. With much research on solving this problem, the demand planning process has been presented as a way to improve the reliability of forecasting accuracy to improve company's performance.
In this paper, we will evaluate the effectiveness of a demand planning process of a fast-moving consumer goods company in the international food sector. The methodology used is a case study with representative data analysis. The author's experience of working with this company enabled him to analyze the results obtained in a relevant way and to draw conclusions measuring the impact on the performance of the supply chain of the company.
Therefore, in order to assess the effectiveness of the demand planning process, the objective of the case study is to compare the accuracy of forecasts generated by statistical models with those produced as a result of a demand planning process through some indicators. As a result, the best performance was found following the demand planning process. However, a more detailed analysis of the results obtained under the different adjustment scenarios described divergent results, indicating that the demand planning process is not always the best performer.
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[...] Does the demand planning process of a fast-moving consumer goods company have an impact on the performance of the supply chain and therefore on the overall performance of the company? In this paper, we will apply a case study as a methodology to examine the efficiency of a demand planning process by comparing the effectiveness of a forecast based mainly on statistical models with that combining a more developed process thanks to a demand planning process. Almost all companies in the fast-moving consumer goods industry use processes to develop forecasts based on statistical forecast models that determine future demand. [...]
[...] Thus, the demand planning process appears to have effects that vary with different adjustment scenarios. Indeed, the improvement in forecast accuracy occurs only when the forecast is adjusted downward as a result of the demand planning process with a significant improvement of for all SKU categories of company X. The adjustment scenario of not changing the forecast determined by the statistical model during the demand planning process only impacts a part of the SKU categories B and bringing no improvement to the forecast. [...]
[...] Then this model is used to generate future values of the series, in other words to create forecasts. Time series forecasting can therefore be considered as the act of predicting the future using the past (Raicharoen, Lursinsap, & Sanguanbhokai, 2003). Time series models are built using only historical data to construct forecasts (Athiyarath, Paul, & Krishnaswamy, 2020). One of the basic applications of this type of model is to construct the forecast for the next period as the same as the one for the current period. [...]
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