Analisis Peramalan Permintaan Produksi pada Pembuatan Smart Trolley dengan Single Exponential Smoothing

Indra Setiawan; Bennedictus Abimanyu; Muhammad Daffa Arlianto; Ignasius Septian Pandiangan; Muhammad Rafi; Muhammad Aziz Fitroryan

doi:10.22441/pasti.2026.v20i1.012

Authors

Indra Setiawan (ID Scopus: 57245604300; Scopus H-Index=3) ASTRA Polytechnic, Indonesia https://orcid.org/0000-0003-3306-7052
Bennedictus Abimanyu Politeknik Astra, Indonesia
Muhammad Daffa Arlianto Politeknik Astra, Indonesia
Ignasius Septian Pandiangan Politeknik Astra, Indonesia
Muhammad Rafi Politeknik Astra, Indonesia
Muhammad Aziz Fitroryan Politeknik Astra, Indonesia

DOI:

https://doi.org/10.22441/pasti.2026.v20i1.012

Keywords:

Forecasting, Production Planning, Single Exponential Smoothing, Smart Trolley

Abstract

Production Planning and Inventory Control (PPIC), particularly forecasting, plays an important role in supporting accurate production planning for Smart Trolley manufacturing. Dynamic demand changes in the production process create challenges in determining material requirements, production quantities, and schedules. Based on historical data from January to December, Smart Trolley demand increased from 5 units to 25 units, representing a 400% increase. These conditions create challenges in production planning, making the use of a reliable forecasting approach essential to support planning decisions. This study examines Smart Trolley demand patterns and determines the best parameter of the Single Exponential Smoothing method with the lowest forecasting error. A quantitative approach was applied using 12 months of historical demand data with smoothing constants (α) of 0.3 and 0.5. Forecasting accuracy was evaluated using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results show that Single Exponential Smoothing with α = 0.5 achieved the best accuracy, producing a forecast of 23.05 units for the next period and providing a basis for more effective Smart Trolley production planning.

Downloads

Download data is not yet available.

Author Biography

Indra Setiawan, (ID Scopus: 57245604300; Scopus H-Index=3) ASTRA Polytechnic

Industrial Engineering Department

References

Amrina, U., & Firmansyah, H. (2019). Analysis of Defect and Quality Improvement for O Ring Product Through Applying DMAIC Methodology. Jurnal PASTI, 13(2), 136. https://doi.org/10.22441/pasti.2019.v13i2.003

Aprianto, T., Nuryono, A., Setiawan, I., Kurnia, H., & Purba, H. H. (2022). Waste Analysis in the Speaker Box Assy Process to Reduce Lead Time in the Electronic Musical Instrument Industry. Quality Innovation Prosperity, 26(3), 53–65. https://doi.org/10.12776/qip.v26i3.1744

Jufriyanto, M. (2020). Peramalan Permintaan Keripik Singkong Pada UMKM Difaa dengan Simulasi Monte Carlo. Jurnal Teknik Industri: Jurnal Hasil Penelitian Dan Karya Ilmiah Dalam Bidang Teknik Industri, 6(2), 107. https://doi.org/10.24014/jti.v6i2.10452

Kirana, D. H. (2020). The Analysis of Aggregate Planning Implementation to Satisfy the Changes of Consumer Demand in PT. PIC. Jurnal Ekonomi Dan Bisnis Terapan Universitas Padjajaran, 16(2), 21–29.

Kusumawati, A. N., Ghofur, M., Putri, M. A., & Alfatah, Z. A. (2021). Peramalan permintaan menggunakan time series forecasting model untuk merancang resources yang dibutuhkan IKM percetakan. JENIUS: Jurnal Terapan Teknik Industri, 2(2), 105-115. https://doi.org/10.37373/jenius.v2i2.159

Maharani, M. P., & Sari, T. (2025). Pengendalian Persediaan Bahan Baku Menggunakan Metode Analisis Abc, Peramalan, Dan Economic Order Quantity (Eoq) Pada Pt. Xyz. J@ti Undip: Jurnal Teknik Industri, 20(2), 95–103. https://doi.org/10.14710/jati.20.2.95-103

Oktarini, D., Pratiwi, I., & Utami, O. (2017). Perencanaan pengendalian produksi dan persediaan pada industri karet pt melania indonesia. Jurnal Integrasi, 2(2), 16–24.

Permana, T. W., Yudoko, G., & Prasetio, E. A. (2025). Manufacturing cycle prediction using structural equation model toward industrial early warning system simulation: The Indonesian case. Heliyon, 11(1), e41522. https: //doi.org/10.1016/j.heliyon.2024.e41522

Putra, G., & Maulud, A. R. (2020). Peramalan Kebutuhan Batubara Menggunakan Metode Single Exponential Smoothing di PT . Solusi Bangun Andalas. Jurnal Optimalisasi, 6, 131–141. www.jurnal.utu.ac.id/joptimalisasi

Riki, S. et al. (2020). Pengendalian Persediaan Dengan Metode Forcasting : Moving Average dan Exponential Smoothing. Algor, 2(1), 22.

Roswandi, I., Junaedi, D., Riadi, S., & Rokhim, M. (2021). Implementasi Prinsip Kerja 5S Untuk Meningkatkan Efisiensi Waktu Produksi Studi Kasus Di Section Injection Di PT HBR. Jurnal PASTI, 15(3), 320. https://doi.org/10.22441/pasti.2021.v15i3.008

Sartika, D. ., & Nasution, H. . (2018). Penggunaan Metode Smoothing Eksponensial Dalam Meramal Pergerakan Inflasi Di Kota Medan. KARISMATIKA: Kumpulan Artikel Ilmiah, Informatika, Statistik, Matematika Dan Aplikasi, 3(1), 24–35. https://doi.org/10.24114/jmk.v3i1.8823

Setiawan, I., & Setiawan. (2020). Defect reduction of roof panel part in the export delivery process using the DMAIC method: a case study. Jurnal Sistem Dan Manajemen Industri, 4(2), 108–116. https://doi.org/10.30656/jsmi.v4i2.2775

Setiawan, S., Wiyatno, T. N., & Setiawan, I. (2025). Increasing Productivity in The Automotive Industry Through Optimizing Production Systems with Lean Manufacturing Methods. Jurnal Media Teknik & Sistem Industri, 9(2), 86–93. https://doi.org/10.35194/jmtsi.v9i2.4943

Suparno, S., & Rufaidah, A. (2021). Analisis Perbandingan Metode Moving Average dan Exponential Smoothing untuk Meramalkan Permintaan Produk Turning Pada CV. Gavra Perkasa. Jurnal Optimalisasi, 7(2), 201. https://doi.org/10.35308/jopt.v7i2.4311

Susanti, N., & Sahli, M. (2013). Penerapan Metode Exponential Smoothing Dalam Sistem Informasi Pengendalian Persediaan Bahan Baku (Studi Kasus Toko Tirta Harum). Simetris : Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 3(1), 59. https://doi.org/10.24176/simet.v3i1.89