Forecasting the Items Consumption in the Hotel Storage with the Autoregressive Integrated Moving Average Method

Authors

  • Christopher Chandra Universitas Putera Batam
  • Alfannisa Annurrullah Fajrin Universitas Putera Batam
  • Cosmas Eko Suharyanto Universitas Putera Batam

DOI:

https://doi.org/10.21512/emacsjournal.v3i1.6979

Keywords:

Resupply, ARIMA, forecast, consumption, Python

Abstract

In this era, hotel has storage as a storing space for every kind of items. Items stored in the storage are items being used for the needs of the staffs, also for the needs of hotel’s operational. The item consumption is running smoothly with resupply. However, there are often mistakes in resupplying the items. For preventing those several mistakes, a reference is needed to be used for controlling the amount of items arrival (monthly) with minding the amount of items in the storage should be. The reference to be used is the forecast of the item consumption every month. Forecasting was being done with Autoregressive Integrated Moving Average (ARIMA) method. There are five steps needed to build the ARIMA model, such as plot identification, model identification, model estimation, choosing the best model, and prediction (forecast). The input variable to be used in this research is the rime series from the data of storage’s item consumption starts from January 2018 until October 2020, and the output variable is the result of the prediction of item consumption in the next period, such as in November to December 2020. The results is subtracted with the number of items left in storage to obtain the minimum amount of item to be entered for the month.

Author Biographies

Alfannisa Annurrullah Fajrin, Universitas Putera Batam

Department of Informatics Engineering, Faculty of Engineering and Computers

Cosmas Eko Suharyanto, Universitas Putera Batam

Department of Informatics Engineering

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Published

2021-02-01

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Articles