Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry

Chang, Pei‐Chann; Wang, Yen-Wen

doi:10.1016/j.eswa.2005.07.031

Cited by 150 publications

(68 citation statements)

References 18 publications

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“…It is a cost effective and time efficient method as only a small number of samples are required and the outcomes are reasonably objective (Kuo and Chen 2010). Following the sources of Chang and Wang (2006) and Lee et al (2010), the steps involved in the fuzzy Delphi method are described in Appendix 1.…”

Section: Risk Mitigation Strategy Identification With Fuzzy Delphimentioning

confidence: 99%

Communicating supply chain risks and mitigation strategies: a comprehensive framework

Wang

Tiwari

Chen

2017

Production Planning & Control

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Concerning the increasing emphasis on risk management in this uncertain global environment, there is an urgent demand for practical decision support tools that support supply chain risk communication and management. This research proposes an integrated framework that takes explicit account of multiple types of risk in aiding decision-making, and compares and ranks alternative risk mitigation strategies individually and collectively in indicator basis using fuzzy set theory and multiple criteria decision analysis (MCDA) methods. Through an illustrative case, the research demonstrates that the proposed framework provides a holistic view of supply chain risks and enables firms to foresee, spot and respond to the exposed risks in an effective and efficient manner.

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Section: Risk Mitigation Strategy Identification With Fuzzy Delphimentioning

confidence: 99%

Communicating supply chain risks and mitigation strategies: a comprehensive framework

Wang

Tiwari

Chen

2017

Production Planning & Control

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“…Their findings show that the adaptive formulation of the combined neural network model had the least MAPE, showing that models that allow correction of itself as new information becomes available are able to forecast sales more accurately. Chang and Wang (2006) integrated fuzzy logic and artificial neural network into the fuzzy back-propagation network (FBPN) for sales forecasting in Printed Circuit Board (PCB) industry in Taiwan. The results from FBPN are compared to those of Grey Forecasting (GF), Multiple Regression Analysis (MRA) and Back-propagation network (BPN).…”

Section: Literaturementioning

confidence: 99%

“…Empirical findings in general show that combining improves forecasting accuracy and reduces the variance of post-sample forecasting errors (Makridakis and Winkler, 1983) and this holds true in statistical forecasting, judgmental estimates 4 and when averaging statistical and subjective predictions (Clemen, 1989). In this regard, most studies on retail sales forecasting has attempted to improve forecasts from single models by combining forecasts from two or atmost three ANN models, primarily (see Chang and Wang 2006;Doganis et al, 2006;Aburto and Weber, 2007;Chen et al 2009;Ou, 2011a, 2011b;Ni and Fan, 2011).…”

Section: Introductionmentioning

confidence: 99%

Forecasting aggregate retail sales: The case of South Africa

Aye

Balcılar

Gupta

et al. 2015

International Journal of Production Economics

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Forecasting aggregate retail sales may improve portfolio investors" ability to predict movements in the stock prices of the retailing chains. Therefore, this paper uses 26 (23 single and 3 combination) forecasting models to forecast South Africa"s aggregate seasonal retail sales. We use data from 1970: 01 -2012:05, with 1987:01-2012:05 as the out-of-sample period. Unlike, the previous literature on retail sales forecasting, we not only look at a wider array of linear and nonlinear models, but also generate multi-steps-ahead forecasts using a real-time recursive estimation scheme over the out-of-sample period, to mimic better the practical scenario faced by agents making retailing decisions. In addition, we deviate from the uniform symmetric quadratic loss function typically used in forecast evaluation exercises, by considering loss functions that overweight forecast error in booms and recessions. Focusing on the single models alone, results show that their performances differ greatly across forecast horizons and for different weighting schemes, with no unique model performing the best across various scenarios. However, the combination forecasts models, especially the discounted mean-square forecast error method which weighs current information more than past, produced not only better forecasts, but were also largely unaffected by business cycles and time horizons. This result, along with the fact that individual nonlinear models performed better than linear models, led us to conclude that theoretical research on retail sales should look at developing dynamic stochastic general equilibrium models which not only incorporates learning behaviour, but also allows the behavioural parameters of the model to be state-dependent, to account for regime-switching behaviour across alternative states of the economy.

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“…Sajfert [10] exemplified the possibility of applying fuzzy logic into the process of decision making regarding the selection of executive managers. Reliable prediction of sales can improve the quality of business strategy by Chang [11]. Fuzzy approach for risk evaluating and forecasting in accidents caused by working with vehicles such as lift truck was used by Naieni [12].…”

Section: Introductionmentioning

confidence: 99%

A Comparison between Fuzzy Inference Systems for Prediction (with Application to Prices of Fund in Egypt)

Fahmy¹,

Zaher²,

Kandil³

2015

IJCA

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This paper outlines the basic differences between the Fuzzy logic techniques, including Mamdani , Sugeno fuzzy inference system models and Adaptive Neuro-Fuzzy Inference System (ANFIS). The main motivation behind this research is to assess which approach provides the best performance for predicting prices of Fund. Due to the importance of performance in Economy, the Mamdani , Sugeno models and ANFIS are compared with the actual values. Fuzzy inference systems (Mamdani , Sugeno and ANFIS fuzzy models ) can be used to predict the weekly prices of Fund for the Egyptian Market. The application results indicate that (ANFIS) model is better than that of Mamdani and Sugeno . The results of the three fuzzy inference systems (FIS) are compared.

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Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry

Cited by 150 publications

References 18 publications

Communicating supply chain risks and mitigation strategies: a comprehensive framework

Communicating supply chain risks and mitigation strategies: a comprehensive framework

Forecasting aggregate retail sales: The case of South Africa

A Comparison between Fuzzy Inference Systems for Prediction (with Application to Prices of Fund in Egypt)

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