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Forecasting Solid Alum Sales for Knowledge Management

EasyChair Preprint no. 7177, version 2

Versions: 12history
9 pagesDate: December 23, 2021


Predicting or forcasting the number of sales is important for many manufactured companies. It will greatly affect the procurement of production of raw materials. Forecasting data is important in manufacturing companies to match demand and production capacity. This study tries to analyze the data sales of solid alum using the exponential smoothing method to obtain sales forecasts for the coming period. PT ABC is one of the companies that produce solid alum in Indonesia. Like any manufacturing company, this company also considers data to be important in making decisions. They confirm that data from previous sales are used to support the next decision relate to supplies. This research used 11 months of data sales. This study aims to produce an ARIMA prediction model and compare the prediction results with other methods, that is moving average and exponential smoothing. The comparison shows that the moving average model is more accurate than the other two models. The model produced in this study is ARIMA (2,3,1) model, which is an equation that can be used in sales forecasting. The results of these calculations can be used as knowledge for the production department to help estimate the stock of raw materials in the following periods.

Keyphrases: ARIMA, Data Mining, Forecasting, Knowledge Management, Solid Alum

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Fransiska Prihatini Sihotang and Ermatita},
  title = {Forecasting Solid Alum Sales for Knowledge Management},
  howpublished = {EasyChair Preprint no. 7177},

  year = {EasyChair, 2021}}
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