An efficient adaptive neuro fuzzy inference system for product demand forecasting

Agus Wahyu Widodo; Gusti Eka Yuliastuti; Agung Mustika Rizki; Wayan Firdaus Mahmudy

doi:10.1145/3427423.3427443

An efficient adaptive neuro fuzzy inference system for product demand forecasting

Agus Wahyu Widodo
, Gusti Eka Yuliastuti
, Agung Mustika Rizki
, Wayan Firdaus Mahmudy

Faculty of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

A very important early stage and can affect other stage in manufacturing supply chain management is product demand forecasting. The forecasting result will be used in the next stage that is called aggregate production planning which will determine the production size of each product. In this study, the authors use Adaptive Neuro Fuzzy Inference System (ANFIS) to forecast monthly product demand by consumer for the next year. ANFIS that was developed by incorporating neural networks and fuzzy logic is used because it is considered capable of acquiring knowledge from data that have uncertain pattern such as consumer demand. Determination of part of historical data as system input, fuzzy membership function, and set of fuzzy rules are carefully designed for ANFIS to produce accurate results. Computational experiments show that the ANFIS produce forecasting result that close to the actual data pattern.

Original language	English
Title of host publication	Proceedings of 2020 International Conference on Sustainable Information Engineering and Technology, SIET 2020
Publisher	Association for Computing Machinery
Pages	90-94
Number of pages	5
ISBN (Electronic)	9781450376051
DOIs	https://doi.org/10.1145/3427423.3427443
Publication status	Published - 16 Nov 2020
Event	5th International Conference on Sustainable Information Engineering and Technology, SIET 2020 - Virtual, Online, Indonesia Duration: 16 Nov 2020 → 17 Nov 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	5th International Conference on Sustainable Information Engineering and Technology, SIET 2020
Country/Territory	Indonesia
City	Virtual, Online
Period	16/11/20 → 17/11/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

adaptive neuro fuzzy inference system (ANFIS)
artificial neural network
product demand forecasting
takagi-sugeno-kang FIS

Access to Document

10.1145/3427423.3427443

Cite this

Widodo, A. W., Yuliastuti, G. E., Rizki, A. M., & Mahmudy, W. F. (2020). An efficient adaptive neuro fuzzy inference system for product demand forecasting. In Proceedings of 2020 International Conference on Sustainable Information Engineering and Technology, SIET 2020 (pp. 90-94). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3427423.3427443

Widodo, Agus Wahyu ; Yuliastuti, Gusti Eka ; Rizki, Agung Mustika et al. / An efficient adaptive neuro fuzzy inference system for product demand forecasting. Proceedings of 2020 International Conference on Sustainable Information Engineering and Technology, SIET 2020. Association for Computing Machinery, 2020. pp. 90-94 (ACM International Conference Proceeding Series).

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title = "An efficient adaptive neuro fuzzy inference system for product demand forecasting",

abstract = "A very important early stage and can affect other stage in manufacturing supply chain management is product demand forecasting. The forecasting result will be used in the next stage that is called aggregate production planning which will determine the production size of each product. In this study, the authors use Adaptive Neuro Fuzzy Inference System (ANFIS) to forecast monthly product demand by consumer for the next year. ANFIS that was developed by incorporating neural networks and fuzzy logic is used because it is considered capable of acquiring knowledge from data that have uncertain pattern such as consumer demand. Determination of part of historical data as system input, fuzzy membership function, and set of fuzzy rules are carefully designed for ANFIS to produce accurate results. Computational experiments show that the ANFIS produce forecasting result that close to the actual data pattern.",

keywords = "adaptive neuro fuzzy inference system (ANFIS), artificial neural network, product demand forecasting, takagi-sugeno-kang FIS",

author = "Widodo, \{Agus Wahyu\} and Yuliastuti, \{Gusti Eka\} and Rizki, \{Agung Mustika\} and Mahmudy, \{Wayan Firdaus\}",

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month = nov,

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doi = "10.1145/3427423.3427443",

language = "English",

series = "ACM International Conference Proceeding Series",

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Widodo, AW, Yuliastuti, GE, Rizki, AM & Mahmudy, WF 2020, An efficient adaptive neuro fuzzy inference system for product demand forecasting. in Proceedings of 2020 International Conference on Sustainable Information Engineering and Technology, SIET 2020. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 90-94, 5th International Conference on Sustainable Information Engineering and Technology, SIET 2020, Virtual, Online, Indonesia, 16/11/20. https://doi.org/10.1145/3427423.3427443

An efficient adaptive neuro fuzzy inference system for product demand forecasting. / Widodo, Agus Wahyu; Yuliastuti, Gusti Eka; Rizki, Agung Mustika et al.
Proceedings of 2020 International Conference on Sustainable Information Engineering and Technology, SIET 2020. Association for Computing Machinery, 2020. p. 90-94 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Yuliastuti, Gusti Eka

AU - Rizki, Agung Mustika

AU - Mahmudy, Wayan Firdaus

PY - 2020/11/16

Y1 - 2020/11/16

N2 - A very important early stage and can affect other stage in manufacturing supply chain management is product demand forecasting. The forecasting result will be used in the next stage that is called aggregate production planning which will determine the production size of each product. In this study, the authors use Adaptive Neuro Fuzzy Inference System (ANFIS) to forecast monthly product demand by consumer for the next year. ANFIS that was developed by incorporating neural networks and fuzzy logic is used because it is considered capable of acquiring knowledge from data that have uncertain pattern such as consumer demand. Determination of part of historical data as system input, fuzzy membership function, and set of fuzzy rules are carefully designed for ANFIS to produce accurate results. Computational experiments show that the ANFIS produce forecasting result that close to the actual data pattern.

AB - A very important early stage and can affect other stage in manufacturing supply chain management is product demand forecasting. The forecasting result will be used in the next stage that is called aggregate production planning which will determine the production size of each product. In this study, the authors use Adaptive Neuro Fuzzy Inference System (ANFIS) to forecast monthly product demand by consumer for the next year. ANFIS that was developed by incorporating neural networks and fuzzy logic is used because it is considered capable of acquiring knowledge from data that have uncertain pattern such as consumer demand. Determination of part of historical data as system input, fuzzy membership function, and set of fuzzy rules are carefully designed for ANFIS to produce accurate results. Computational experiments show that the ANFIS produce forecasting result that close to the actual data pattern.

KW - adaptive neuro fuzzy inference system (ANFIS)

KW - artificial neural network

KW - product demand forecasting

KW - takagi-sugeno-kang FIS

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M3 - Conference contribution

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T3 - ACM International Conference Proceeding Series

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Widodo AW, Yuliastuti GE, Rizki AM, Mahmudy WF. An efficient adaptive neuro fuzzy inference system for product demand forecasting. In Proceedings of 2020 International Conference on Sustainable Information Engineering and Technology, SIET 2020. Association for Computing Machinery. 2020. p. 90-94. (ACM International Conference Proceeding Series). doi: 10.1145/3427423.3427443

An efficient adaptive neuro fuzzy inference system for product demand forecasting

Abstract

Publication series

Conference

UN SDGs

Keywords

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