Modelling for supply uncertainty of production using adaptive neuro-fuzzy system

Year 2026, Volume: 10 Issue: 2 , 608 - 619 , 01.05.2026

Iffan Maflahah , Dian Farida Asfan , Raden Arief Firmansyah

https://doi.org/10.31127/tuje.1838015

https://izlik.org/JA93PR23NB

Abstract

The amount of fruit supply is unpredictable because it is seasonal, while the demand for juice constantly increases. Efforts to address uncertainty in both supply and demand necessitate the implementation of accurate production quantity forecasting. Therefore, this study aimed to use the Adaptive Neuro Fuzzy System (ANFIS) approach to model the amount of fruit production based on the uncertainty of the amount of supply. The dataset comprised 48 observation periods, and to develop a robust and reliable model, the data were partitioned into two subsets, including 75% for training and 25% for testing the ANFIS model. The model structure built with ANFIS uses three inputs, including demand quantity, fruit supply quantity, and puree availability. The supply of fruit is seasonal in nature, resulting in substantial availability during the harvest period. During this time, the surplus fruit is processed into puree and juice to ensure continuity of supply beyond the harvest season. The output produced is the amount of juice produced. Based on the most accurate error value based on RMSE (0.063), MAPE (1.55%), MAD (0.027), and R2 (94.4%). The forecasting model for fruit juice production with the ANFIS approach is the Gaussian membership function (Hybrid), with the number of memberships 3 – 4 – 5.

Keywords

ANFIS , Forecasting , Juice Production , Supply , Uncertainty

Ethical Statement

The authors declare that there is no conflict of interest and this research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Supporting Institution

University of Trunojoyo Madura

Project Number

Not applicable

Thanks

The authors declare no conflict of interest and acknowledge no outside assistance or funding sources related to this document

References

• Sharma, H. K., & Kumar, N. (2022). Agro Processing: Scope and Importance BT - Agro-Processing and Food Engineering: Operational and Application Aspects (H. K. Sharma & N. Kumar (eds.); pp. 1–22). Springer Singapore. https://doi.org/10.1007/978-981-16-7289-7_1
• Yadav, V. S., Singh, A. R., Gunasekaran, A., Raut, R. D., & Narkhede, B. E. (2022). A systematic literature review of the agro-food supply chain: Challenges, network design, and performance measurement perspectives. Sustainable Production and Consumption, 29, 685–704. https://doi.org/https://doi.org/10.1016/j.spc.2021.11.019
• Kück, M., & Freitag, M. (2021). Forecasting of cusomer demands for production planning by local k-nearest neighbor models. International Journal of Production Economics, 231, 107837. https://doi.org/https://doi.org/10.1016/j.ijpe.2020.107837
• Daneshvar, A., Radfar, R., Ghasemi, P., Bayanati, M., & Pourghader Chobar, A. (2023). Design of an Optimal Robust Possibilistic Model in the Distribution Chain Network of Agricultural Products with High Perishability under Uncertainty. Sustainability (Switzerland), 15(15). https://doi.org/10.3390/su151511669
• Mahto, A. K., Alam, M. A., Biswas, R., Ahmed, J., & Alam, S. I. (2020). Short-Term Forecasting of Agriculture Commodities in Context of Indian Market for Sustainable Agriculture by Using the Artificial Neural Network. Journal of Food Quality, 2021(1). https://doi.org/https://doi.org/10.1155/2021/9939906
• Azadi, Z., Eksioglu, S. D., Eksioglu, B., & Palak, G. (2019). Stochastic optimization models for joint pricing and inventory replenishment of perishable products. Computers & Industrial Engineering, 127, 625–642. https://doi.org/https://doi.org/10.1016/j.cie.2018.11.004
• Diabat, A., Jabbarzadeh, A., & Khosrojerdi, A. (2019). A perishable product supply chain network design problem with reliability and disruption considerations. International Journal of Production Economics, 212, 125–138. https://doi.org/https://doi.org/10.1016/j.ijpe.2018.09.018
• Faqih, A., Elizabeth, R., & Azahari, D. H. (2020). The increasing of competitiveness of agro-industry products through institutional empowerment to support the achievement of sustainable agricultural development. International Journal of Energy Economics and Policy, 10(5), 663–671. https://doi.org/10.32479/ijeep.10376
• Parra-Hernández, N. A., García-Santamaría, L. E., Fernández-Echeverría, E., Fernández-Lambert, G., & Martínez-Mendoza, E. (2024). Risks and Resilient Strategies in Agro-Industrial Supply Chains – a Scoping Review 2013–2023. Logforum, 20(1), 39–54. https://doi.org/10.17270/J.LOG.001012
• Arismawatia, P., & Prastyabudi, W. A. (2021). An Inventory Policy on Agroindustry Supply Chain: A Case Study of Fruit Seasonal in East Java. Food & Agribusiness Management (FABM), 2(2), 35–39. https://doi.org/10.26480/fabm.02.2021.
• Chernonog, T. (2020). Inventory and marketing policy in a supply chain of a perishable product. International Journal of Production Economics, 219, 259–274. https://doi.org/https://doi.org/10.1016/j.ijpe.2019.06.019
• AL-Qaysi, A. M. M., Bozkurt, A., & Ates, Y. (2023). Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq. Energies, 16(6). https://doi.org/10.3390/en16062919
• Januschowski, T., Gasthaus, J., Wang, Y., Salinas, D., Flunkert, V., Bohlke-Schneider, M., & Callot, L. (2020). Criteria for classifying forecasting methods. International Journal of Forecasting, 36(1), 167–177. https://doi.org/https://doi.org/10.1016/j.ijforecast.2019.05.008
• Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Ben Taieb, S., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., Browell, J., Carnevale, C., Castle, J. L., Cirillo, P., Clements, M. P., Cordeiro, C., Cyrino Oliveira, F. L., De Baets, S., Dokumentov, A., … Ziel, F. (2022). Forecasting: theory and practice. International Journal of Forecasting, 38(3), 705–871. https://doi.org/https://doi.org/10.1016/j.ijforecast.2021.11.001
• Demir, V., & Doğu, R. (2024). Prediction of elevation points using three different heuristic regression techniques. Turkish Journal of Engineering, 8(1), 56–64. https://doi.org/10.31127/tuje.1257847
• Öztürk, A., Allahverdi, N., & Saday, F. (2022). Application of artificial intelligence methods for bovine gender prediction. Turkish Journal of Engineering, 6(1). https://doi.org/10.31127/tuje.807019
• Khemavuk, P., & Leenatham, A. (2020). A conceptual model for uncertainty demand forecasting by artificial neural network and adaptive neuro-fuzzy inference system based on quantitative and qualitative data. International Journal of Operations and Quantitative Management, 26(4), 285–302. https://doi.org/10.46970/2020.26.4.3
• Kasperski, A., & Zieliński, P. (2021). Soft robust solutions to possibilistic optimization problems. Fuzzy Sets and Systems, 422, 130–148. https://doi.org/https://doi.org/10.1016/j.fss.2020.12.016
• Mubarak, S. M. J., Crampton, A., Carter, J., & Parkinson, S. (2022). Robust data expansion for optimised modelling using adaptive neuro-fuzzy inference systems. Expert Systems with Applications, 189, 116138. https://doi.org/https://doi.org/10.1016/j.eswa.2021.116138
• Chandar, S. K. (2019). Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach. Cluster Computing, 22(6), 13159–13166. https://doi.org/10.1007/s10586-017-1321-6
• Tabbussum, R., & Dar, A. Q. (2021). Performance evaluation of artificial intelligence paradigms—artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction. Environmental Science and Pollution Research, 28(20), 25265–25282. https://doi.org/10.1007/s11356-021-12410-1
• Prado, F., Minutolo, M. C., & Kristjanpoller, W. (2020). Forecasting based on an ensemble Autoregressive Moving Average - Adaptive neuro - Fuzzy inference system – Neural network - Genetic Algorithm Framework. Energy, 197, 117159. https://doi.org/https://doi.org/10.1016/j.energy.2020.117159
• Acakpovi, A., Ternor, A. T., Asabere, N. Y., Adjei, P., & Iddrisu, A.-S. (2020). Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems. Mathematical Problems in Engineering, 2020. https://doi.org/https://doi.org/10.1155/2020/4181045
• Bousqaoui, H., Slimani, I., & Achchab, S. (2021). Comparative analysis of short-term demand predicting models using ARIMA and deep learning. International Journal of Electrical and Computer Engineering, 11(4), 3319–3328. https://doi.org/10.11591/ijece.v11i4.pp3319-3328
• Kouadio, L., Byrareddy, V. M., Sawadogo, A., & Newlands, N. K. (2021). Probabilistic yield forecasting of robusta coffee at the farm scale using agroclimatic and remote sensing derived indices. Agricultural and Forest Meteorology, 306, 108449. https://doi.org/https://doi.org/10.1016/j.agrformet.2021.108449
• Herrera-Granda, I. D., Lorente-Leyva, L. L., Peluffo-Ordóñez, D. H., & Alemany, M. del M. E. (2020). A Forecasting Model to Predict the Demand of Roses in an Ecuadorian Small Business Under Uncertain Scenarios. Springer Nature Switzerland, August, 1–14.
• Nabavi-Pelesaraei, A., Rafiee, S., Hosseini-Fashami, F., & Chau, K. (2021). Chapter 11 - Artificial neural networks and adaptive neuro-fuzzy inference system in energy modeling of agricultural products (R. Deo, P. Samui, & S. S. B. T.-P. M. for E. M. and P. S. E. Roy (eds.); pp. 299–334). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-12-817772-3.00011-2
• Tay, A., Lafont, F., & Balmat, J. F. (2021). Forecasting pest risk level in roses greenhouse: Adaptive neuro-fuzzy inference system vs artificial neural networks. Information Processing in Agriculture, 8(3), 386–397. https://doi.org/10.1016/j.inpa.2020.10.005
• Sremac, S., Zavadskas, E. K., Matić, B., Kopić, M., & Stević, Ž. (2019). Neuro-fuzzy inference systems approach to decision support system for economic order quantity. Economic Research-Ekonomska Istrazivanja , 32(1), 1114–1137. https://doi.org/10.1080/1331677X.2019.1613249
• Diarsih, I. H., Tarno, & Rusgiyono, A. (2019). Modeling of red onion production in Central Java using hybrid ARIMA-ANFIS. Journal of Physics: Conference Series, 1217(1). https://doi.org/10.1088/1742-6596/1217/1/012080
• Fatemi, S. E., & Parvini, H. (2022). The impact assessments of the ACF shape on time series forecasting by the ANFIS model. Neural Computing and Applications, 34(15), 12723–12736. https://doi.org/10.1007/s00521-022-07140-5
• Karaboga, D., & Kaya, E. (2019). Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artificial Intelligence Review, 52(4), 2263–2293. https://doi.org/10.1007/s10462-017-9610-2
• Widodo, A. W., Yuliastuti, G. E., Rizki, A. M., & Mahmudy, W. F. (2021). An efficient adaptive neuro fuzzy inference system for product demand forecasting. Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology, 90–94. https://doi.org/10.1145/3427423.3427443
• Sinaga, D. S., Windarto, A. P., Nasution, R. A., & Damanik, I. S. (2022). Prediction of Product Sales Results Using Adaptive Neuro Fuzzy Inference System (Anfis). Journal of Artificial Intelligence and Engineering Applications (JAIEA), 1(2), 92–101. https://doi.org/10.59934/jaiea.v1i2.73
• Sade, J. (2026). Predicting fetal health using ANFIS : A comparative study with machine learning models. Turkish Journal of Engineering, 10(1), 187–196. https://doi.org/https://doi.org/10.31127/tuje.1711661
• Köken, E., & Koca, T. K. (2023). A comparative study to estimate the mode I fracture toughness of rocks using several soft computing techniques. Turkish Journal of Engineering, 7(4), 296–305. https://doi.org/10.31127/tuje.1120669
• Deshpande, G., Lu, B., Huynh, N., & Rangaprakash, D. (2025). Transfer Learning for Improving Neuroimaging-Based Diagnostic Classification. IEEE Transactions on Computational Biology and Bioinformatics. https://doi.org/10.1109/TCBBIO.2025.3647485
• Schultz, B. G., Joukhadar, Z., Nattala, U., Quiroga, M. D. M., Bolk, F., & Vogel, A. P. (2021). Best practices for supervised machine learning when examining biomarkers in clinical populations. In Big Data in Psychiatry and Neurology (pp. 1–34). https://doi.org/10.1016/B978-0-12-822884-5.00013-1
• Roesyadi, B. P., & Sahroni, T. R. (2025). Neuro-Fuzzy Inference System for Accurate Prediction of Z-Axis Values in Screw Installation. International Journal of Technology, 16(6), 1888–1893. https://doi.org/10.14716/ijtech.v16i6.7557
• Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524. https://doi.org/https://doi.org/10.1016/j.asoc.2019.105524
• Huang, L., Qin, J., Zhou, Y., Zhu, F., Liu, L., & Shao, L. (2023). Normalization Techniques in Training DNNs: Methodology, Analysis and Application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8), 10173–10196. https://doi.org/10.1109/TPAMI.2023.3250241
• Aghelpour, P., Mohammadi, B., Biazar, S. M., Kisi, O., & Sourmirinezhad, Z. (2020). A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods. In ISPRS International Journal of Geo-Information (Vol. 9, Issue 12). https://doi.org/10.3390/ijgi9120701
• Sarkar, S., Pramanik, A., & Maiti, J. (2023). An integrated approach using rough set theory, ANFIS, and Z-number in occupational risk prediction. Engineering Applications of Artificial Intelligence, 117, 105515. https://doi.org/https://doi.org/10.1016/j.engappai.2022.105515
• Hussain, W., & Sohaib, O. (2019). Analysing Cloud QoS Prediction Approaches and Its Control Parameters: Considering Overall Accuracy and Freshness of a Dataset. IEEE Access, 7, 82649–82671. https://doi.org/10.1109/ACCESS.2019.2923706
• Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. Geoscientific Model Development, 15(14), 5481–5487. https://doi.org/10.5194/gmd-15-5481-2022
• Chai, T. (2020). Root Mean Square. In Encyclopedia of Earth Sciences Series. Springer Nature Link. https://doi.org/10.1007/978-3-030-26050-7_280-1
• Acı, M., Acı, Ç. İ., & Avcı, M. (2018). Performance Comparıson of Anfıs, ANN, SVR, CART and MLR Technıques for Geometry Optımızatıon of Carbon Nanotubes usıng Castep. Turkish Journal of Engineering, 2(3), 119 - 124. https://doi.org/10.31127/tuje.408976
• Sharma, N., Thakur, M. S., Kumar, R., Malik, M. A., Alahmadi, A. A., Alwetaishi, M., & Alzaed, A. N. (2022). Assessing Waste Marble Powder Impact on Concrete Flexural Strength Using Gaussian Process, SVM, and ANFIS. Processes, 10(12). https://doi.org/10.3390/pr10122745
• Talpur, N., Salleh, M. N. M., & Hussain, K. (2017). An investigation of membership functions on performance of ANFIS for solving classification problems. IOP Conference Series: Materials Science and Engineering, 226(1). https://doi.org/10.1088/1757-899X/226/1/012103
• Amruthavarshini, V., & Hanumanthappa, S. (2023). Comparative study of ANN and ANFIS models for detection of damages due to cracks in single bay framed structure. Materials Today: Proceedings, 88, 93–99. https://doi.org/10.1016/j.matpr.2023.05.021
• Raharja, M. A., Darmawan, I. D. M. B. A., Nilakusumawati, D. P. E., & Supriana, I. W. (2021). Analysis of membership function in implementation of adaptive neuro fuzzy inference system (ANFIS) method for inflation prediction. Journal of Physics: Conference Series, 1722(1). https://doi.org/10.1088/1742-6596/1722/1/012005