ABSTRACT
Supply Chain has components such as vendors, manufacturers, factories, warehouses retailers, customers, etc. Every relationship between components must have good information in order to create informed business decisions. Sales forecast are part of a decline in supply chain function and are a way to predict future product sales. The large gap between demand forecasting and actual demand proves that the forecasting method used in forecasting is not quite right so it can cause high error rates. In this study, the calculation of demand forecasting using the Artificial Neural Network (ANN) method was chosen as a good method because ANN learning method that works through an iterative process using training data comparing the predicted value of the network each sample of data and the weight of the network relation in each process is modified to minimize the value of Mean Squared Error (MSE). With the right parameters and good training in the data, the error number at the ANN calculation output using MATLAB will produce demand forecasting numbers that are getting closer to the actual demand numbers. The application of the ANN method to demand forecasting can make improvements to the error value performance using the MSE, MAD equation. and MAPE. The decline in MSE in 2018 from 1,894,299,389 to 26,612,567, in 2019 from 1,035,177,794 to 16,889,433, and in 2020 from 426,876,921 to 2,647,350. The decline in MAD in 2018 from 42,089 to 3,324, in 2019 from 26,924 to 2,888, and in 2020 from 20,661 to 1,627. MAPE reduction in 2018 from 23% to 2%, 2019 from 15% to 2%, and in 2020 from 11% to 1%.
- Maulaya, A., Ridwan, A.Y., Santoso, B., (2019), Spare Part Inventory Policy Planning based on FRMIC (Fuzzy-Rule-based approach for Multi-Criteria Inventory Classification) using Base-Stock Policy Method (S-1, S), Proceedings of 1st International Conference on Engineering and Management in Industrial SystemGoogle ScholarCross Ref
- Nurhasanah, H., Ridwan, A.Y., Santosa, B., (2019), A Condition-based maintenance and spare parts provisioning based on markov chains, IOP Conference Series: Materials Science and EngineeringGoogle ScholarCross Ref
- Vhatkar, S., & Dias, J. (2016). Oral-Care Goods Sales Forecasting Using Artificial Neural Network Model. Procedia Computer Science, 79, 238--243.Google ScholarCross Ref
- Tonbul, T. S. (2019). Sales Forecast in FMCG Sector with Artificial Neural Networks. November.Google Scholar
- Amelia, P., Ridwan, A.Y., Santosa, B., (2019), Designing of Raw Material Scheduling Supply Multi on Supplier Strategies with Price, Lead time, and Stochastic Demand Variations. Case Study: Electricity Manufacturer, IOP Conference Series: Materials Science and EngineeringGoogle ScholarCross Ref
- Fadly, M., Ridwan, A.Y., Deni Akbar, M., (2019), Hotel room price determination based on dynamic pricing model using nonlinear programming method to maximize revenue, Proceedings of 2nd International Conference on Applied Information Technology and Innovation: Exploring the Future Technology of Applied Information Technology and InnovationGoogle ScholarCross Ref
- Sani, M., & Eunike, A. (2018). Analisis Peramalan Permintaan Produk Fast Moving Consumer Goods Menggunakan Metode Artificial Neural Network Forecasting Demand Analysis For Fast Moving Consumer Goods. 7(5), 61--70. Malang: Universitas BrawijayaGoogle Scholar
- Singh, A., & Sahay, K. B. (2018). Short-Term Demand Forecasting by Using ANN Algorithms. IEECON 2018-6th International Electrical Engineering Congress, 1--4.Google ScholarCross Ref
- Kim, P. (2017). MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence. In Library of Congress Control Number.Google Scholar
- Haykin, S. (2009). Neural Network: A Comprehensive Foundation. Delhi: Pearson.Google Scholar
- Julpan, Nababan, E. B., & Zarlis, M. (2015). Analisis Fungsi Aktivasi Sigmoid Biner Dan SIgmoid Bipolar Dalam Algoritma Backpropagation Pada Prediksi Kemampuan Siswa. Jurnal Teknovasi, 02(1), 103--116.Google Scholar
Index Terms
- Demand Forecasting for Drinking Water Products to Reduce Gap Between Estimation and Realization of Demand Using Artificial Neural Network (ANN) Methods in PT. XYZ
Recommendations
Based on Improved BP Neural Network to Forecast Demand for Spare Parts
NCM '09: Proceedings of the 2009 Fifth International Joint Conference on INC, IMS and IDCAccording to the historical data of the x x factory the BP artificial neural network model is uesed, and a mapping relationship between the input and the output value of spare parts demand is set up. Model training results show that the model can better ...
Study on the Model of Demand Forecasting Based on Artificial Neural Network
DCABES '10: Proceedings of the 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and ScienceThe issue of the modeling for the distribution management based on demand forecasting. ANN(Artificial Neural Network,for short ANN) model is applied to the field of demand forecasting. The modeling of market demand forecasting is built using BP ...
A fully adaptive forecasting model for short-term drinking water demand
For the optimal control of a water supply system, a short-term water demand forecast is necessary. We developed a model that forecasts the water demand for the next 48 h with 15-min time steps. The model uses measured water demands and static calendar ...
Comments