scholarly journals Analogous Forecasting of Products with a Short Life Cycle

2010 ◽  
Vol 4 (2) ◽  
pp. 71-85 ◽  
Author(s):  
Natalia Szozda
Keyword(s):  
Time Series ◽  
Supply Chain ◽  
Life Cycle ◽  
Historical Data ◽  
Life Cycles ◽  
Time Series Forecasting ◽  
High Tech ◽  
Short Life ◽  
Forecasting Methods ◽  
Fashion Apparel

Managing a supply chain for products with a short life cycle, like fashion apparel, high-tech, personal computers, toys, CD’s etc., is challenging for many companies (Fisher and Raman, 1999). Because the life cycles of these products are too short for standard time- series forecasting methods (not longer than one – two years), an important way of overcoming the challenges of managing supply chains for such products is to find appropriate forecasting methodologies. The standard forecasting methods require some historical data, which are often unavailable at the time when the forecasts are being performed for products with a short life cycle (Lin, 2005). The method described in this article allows forecasters to use life cycles of similar, analogous products to arrive at the initial forecasts for the product(s) at hand.

scholarly journals Aplikasi Metode Grey Forecasting Pada Peramalan Kebutuhan Bahan Bakar Alternatif Ramah Lingkungan di PT. Indocement Tunggal Prakarsa Tbk

2015 ◽  
Vol 14 (2) ◽  
Author(s):  
Nanda Lokita Nariswari ◽  
Cucuk Nur Rosyidi
Keyword(s):  
Time Series ◽  
Alternative Energy ◽  
Historical Data ◽  
Raw Materials ◽  
Time Series Forecasting ◽  
Short Term ◽  
Grey Forecasting ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
A Company

<span><em>Forecasting is one of the methods required by a company to plan the demand of raw materials in the </em><span><em>future, in order to avoid the emergence of various problems such as stock out. However, not all </em><span><em>forecasting methods can be used to forecast demand in the short term a specially a condition where the </em><span><em>company only has a few historical data. Grey method is a forecasting method which can be used to </em><span><em>predict the short-term demand. The purpose of this study is to determine how well the Grey method used </em><span><em>to predict the demand of alternative energy and compared with other forecasting methods. Mean Squared </em><span><em>Error (MSE) is used as a measure of the goodness of the method. The result of the study indicates that the </em><span><em>Grey Forecasting Methods MSE value that is smaller than other time series forecasting methods.</em></span></span></span></span></span></span></span><br /></span>

scholarly journals Proposed Improvement of Forecasting Using Time Series Forecasting of Fast Moving Consumer Goods

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Tasya Regina ◽  
Panca Jodiawan
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Moving Average ◽  
Time Series Forecasting ◽  
Class A ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Double Exponential ◽  
Data Points ◽  
Double Exponential Smoothing

<p>The company discussed in this paper is a national distributor firm that distributes FMCG products. The PPIC division in the company is responsible for forecasting the demand using the combination of the moving average method and intuition according to the interest of the company. However, the PPIC staff never measures the accuracy of their forecasting method. This research paper aims to evaluate the forecasting methods used to predict the demands of 12 classes of A SKU. Four-time series forecasting methods are particularly implemented, i.e., ARIMA, moving average (MA), double exponential smoothing (DES), and linear regression (RL). Forecasting using the ARIMA method is carried out by considering the stationarity of the average and variance of the historical data points. Forecasting using DES is carried out by using the optimal alpha and gamma values of the ARIMA method. The results show that the performance of each forecasting method varies, depending on which demands of class A SKU are predicted. Based on these results, the current forecasting method utilized by the company should be improved using the time series forecasting methods leading to the smallest error values for each class of A SKU.</p>

Forecasting Demand with Support Vector Regression Technique Combined with X13-ARIMA-SEATS Method in the Presence of Calendar Effect

2014 ◽  
Vol 5 (2) ◽  
pp. 74-86 ◽  
Author(s):  
Malek Sarhani ◽  
Abdellatif El Afia
Keyword(s):  
Time Series ◽  
Supply Chain ◽  
Support Vector Regression ◽  
Case Studies ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Reliable Prediction ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Two Stages

In order to better manage and optimize supply chain, a reliable prediction of future demand is needed. The difficulty of forecasting demand is due mainly to the fact that heterogeneous factors may affect it. Analyzing such kind of data by using classical time series forecasting methods, will fail to capture such dependency of factors. This paper is released to present a forecasting approach of two stages which combines the recent methods X13-ARIMA-SEATS and Support Vector Regression (SVR). The aim of the first one is to remove the calendar effect, while the purpose of the second one is to forecast the demand after the removal of this effect. This approach is applied to three different case studies and compared to the forecasting method based on SVR alone.

Forecasting Demand With Support Vector Regression Technique Incorporating Feature Selection in the Presence of Calendar Effect

2018 ◽  
pp. 302-316 ◽  
Author(s):  
Malek Sarhani ◽  
Abdellatif El Afia
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Supply Chains ◽  
Support Vector Regression ◽  
Case Studies ◽  
Time Series Forecasting ◽  
Regression Technique ◽  
Support Vector ◽  
Reliable Prediction ◽  
Forecasting Methods

Reliable prediction of future demand is needed to better manage and optimize supply chains. However, a difficulty of forecasting demand arises due to the fact that heterogeneous factors may affect it. Analyzing such data by using classical time series forecasting methods will fail to capture such dependency of factors. This chapter addresses these problems by examining the use of feature selection in forecasting using support vector regression while eliminating the calendar effect using X13-ARIMA-SEATS. The approach is investigated in three different case studies.

Enhanced Forecasting Accuracy of Fuzzy Time Series Model Based on Combined Fuzzy C-Mean Clustering with Particle Swam Optimization

2020 ◽  
Vol 19 (02) ◽  
pp. 2050017
Author(s):  
Nghiem Van Tinh
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Pso Algorithm ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Forecasting Accuracy ◽  
Forecasting Models ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
Universe Of Discourse

Over the past 25 years, numerous fuzzy time series forecasting models have been proposed to deal the complex and uncertain problems. The main factors that affect the forecasting results of these models are partition universe of discourse, creation of fuzzy relationship groups and defuzzification of forecasting output values. So, this study presents a hybrid fuzzy time series forecasting model combined particle swarm optimization (PSO) and fuzzy C-means clustering (FCM) for solving issues above. The FCM clustering is used to divide the historical data into initial intervals with unequal size. After generating interval, the historical data is fuzzified into fuzzy sets with the aim to serve for establishing fuzzy relationship groups according to chronological order. Then the information obtained from the fuzzy relationship groups can be used to calculate forecasted value based on a new defuzzification technique. In addition, in order to enhance forecasting accuracy, the PSO algorithm is used for finding optimum interval lengths in the universe of discourse. The proposed model is applied to forecast three well-known numerical datasets (enrolments data of the University of Alabama, the Taiwan futures exchange —TAIFEX data and yearly deaths in car road accidents in Belgium). These datasets are also examined by using some other forecasting models available in the literature. The forecasting results obtained from the proposed model are compared to those produced by the other models. It is observed that the proposed model achieves higher forecasting accuracy than its counterparts for both first—order and high—order fuzzy logical relationship.

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