Comparative Analysis of Artificial Neural Networks and Neuro-Fuzzy Models for Multicriteria Demand Forecasting

Comparative Analysis of Artificial Neural Networks and Neuro-Fuzzy Models for Multicriteria Demand Forecasting

Golam Kabir, M. Ahsan Akhtar Hasin
Copyright: © 2013 |Pages: 24
DOI: 10.4018/ijfsa.2013010101
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An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. The objective of the paper is to propose a new forecasting mechanism which is modeled by artificial intelligence approaches including the comparison of both artificial neural networks (ANN) and adaptive network-based fuzzy inference system (FIS) techniques to manage the fuzzy demand with incomplete information. Artificial neural networks has been applied as it is capable to model complex, nonlinear processes without having to assume the form of the relationship between input and output variables. Neuro-fuzzy systems also utilized to harness the power of the fuzzy logic and ANNs through utilizing the mathematical properties of ANNs in tuning rule-based fuzzy systems that approximate the way human’s process information. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated for a 20/25 MVA Distribution Transformer from Energypac Engineering Limited (EEL), a leading power engineering company of Bangladesh.
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Demand forecast is one of the most important inputs of production planning and supply chain (SC) planning models. In the manufacturing process, demands are predicted to perform the basic planning activities such as capacity planning, resource planning, and raw material purchasing. Demand forecasts are of great importance for marketing activities and personnel management and its accuracy affects directly the profitability of the company. There are many forecasting techniques that can be classified into four main groups: (1) Qualitative methods are primarily subjective; they rely on human judgment and opinion to make a forecast. (2) Time-series methods use historical data to make a forecast. (3) Causal methods involve assuming that the demand forecast is highly correlated with certain factors in the environment (e.g., the state of the economy, interest rate). (4) Simulation methods imitate the consumer choices that give rise to demand to arrive at a forecast (Chopra & Meindl, 2001).

Most prior studies have been applied to predict the customer demand primarily based on time-series models, such as moving-average, exponential smoothing, and the Box-Jenkins method, and casual models, such as regression and econometric models. More studies have been done on demand forecasting in the past 20 years. There are numerous models and many different ways to analyze and forecast demand.

Brännäs et al. (2002) obtained an integer-valued moving average model by cross-sectional and temporal aggregation. Smith et al. (2002) examined the theoretical foundation of nonparametric regression and answered the question of whether nonparametric regression based on heuristically improved forecast generation methods approach the single interval traffic flow prediction performance of seasonal ARIMA (Autoregressive Integrated moving average) models. Weatherford and Kimes (2003) tested a variety of forecasting methods and to determine the most accurate method to forecast the arrivals which is one of the key inputs for a successful hotel revenue management system. Winklhofer and Diamantopoulos (2003) presented and tested a path model of export sales forecasting behavior and performance incorporating organizational and export-specific characteristics. Franses and Dijk (2005) examined the forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production series of 18 OECD countries. Garcia-Ferrer et al. (2006) compared the empirical performance of various forecasting models in assessing the effects of policy variables, legal changes, and traffic security campaigns. Kahraman et al. (2006) provides a detailed revision of fuzzy sets applications in forecasting. Petrovic et al. (2006) proposes a demand forecasting model taking into account both statistical forecasting techniques based on historical data as well as expert judgments. Taylor (2007) constructed interval forecasts from quantile predictions generated using exponentially weighted quantile regression. Abdel-Aal (2008) used a univariate modeling of the monthly energy demand time series based only on data for 6 years to forecast the demand for the seventh year. Heij et al. (2008) proposed an improved method for the construction of principal components in macroeconomic forecasting with the underlying idea which was to maximize the amount of variance of the original predictor variables that is retained by the components in order to reduce the variance involved in estimating the forecast model.

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