Forecasting Models of Community Biodegradable Waste Management - Karnasuta, Sujinna; Laoanantana, Panya (Arts of Management Journal, 2022)
This research objection on the data collection of the organic waste amount was produced from 2016 until 2019, through 4 years, the unit is in kilograms. The organic waste is divided into 2 categories, which are fruit peel and food waste, at the center of organic management, generating bio-fermented water to supply the gardens of KU campus and green schools with Vertical Recycled Box (VRB) in Bangkok city. The organic waste data were analyzed using correlation and forecasting statistics, as well as statistical numerical modeling. Pearson’s correlation in this research is a technique to measure the correlation between interesting data, but not causation. The forecasting techniques of this research are the moving average, the weight moving average, the simple exponential smoothing, the Holt’s exponential smoothing. The amount of organic waste that was produced from 2016 until 2019. There are four forecasting techniques, which are 1) Moving Average k=3, 2) Weight Moving Average k=3, 3) Simple Exponential Smoothing, and 4) Holt’s Exponential Smoothing. All the techniques seem to be similar from March until December of 2020 due to the amount of food waste in the year 2018 until early 2019 being stable. But in January and February of 2020, the Moving Average was calculated from the past 3 years of data, and in January of 2016, the amount of food waste was extremely high at almost 1,200 kilograms, which had an effect on the forecasting value in January of 2020. The research result benefits from Pearson’s correlation and the forecasting techniques of this research with the moving average, the weight moving average, the simple exponential smoothing, and Holt’s exponential smoothing can be used to prepare for what will happen in the future, gain valuable insight, and the result from prediction methods could decrease the cost of environmental management of organic waste.