Open AccessAdvising an Appropriate Forecasting Method for a Snacks Item (Biscuit) Manufacture Company in Bangladesh
Author(s) -
Nandita Barman,
M Babul Hasan,
Nayan Dhali
Publication year - 2018
Publication title -
the dhaka university journal of science
Language(s) - English
Resource type - Journals
eISSN - 2408-8528
pISSN - 1022-2502
DOI - 10.3329/dujs.v66i1.54545
Subject(s) - exponential smoothing , autoregressive integrated moving average , factory (object oriented programming) , moving average , demand forecasting , econometrics , time series , operations research , computer science , production (economics) , mathematics , statistics , economics , macroeconomics , programming language
In this paper, we study the most appropriate short-term forecasting methods for the newly launched biscuit factory produces different types of biscuits. One of them is nut-orange twisted biscuits. As it is a newly launched biscuit factory, it does not use any scientific method to find future demand of their products to produce for the purpose of sales. Having an error free production as well as a good inventory management we try to find an appropriate forecasting method for the sets of data we analyzed for that specific production. Several forecasting methods of time series forecasting such as the Moving Averages, Linear Regression with Time, Exponential Smoothing, Holt‘s Method, Holt-Winter‘s Method etc. can be applied to estimate the demand and supply for these companies. This paper focuses on selecting an appropriate forecasting technique for the newly launched biscuit company. For this, we analyze Exponential Smoothing method as used to time series. We observe from the empirical results of the analysis that if the data has no trend as well as seasonality, Exponential Smoothing Forecasting Method processes as the most appropriate forecasting method for the factory. If the data experiences linear trend in it then Holt’s Forecasting Method processes as the most appropriate forecasting method for the sets of data we analyzed.
Dhaka Univ. J. Sci. 66(1): 55-58, 2018 (January)