Napovedovanje količine naročil v podjetju s področja industrije umetnih mas z uporabo naprednih napovednih metod

Kuk, Gal

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Title:	Napovedovanje količine naročil v podjetju s področja industrije umetnih mas z uporabo naprednih napovednih metod
Authors:	ID Kuk, Gal (Author) ID Ojsteršek, Robert (Mentor) More about this mentor... ID Frešer, Blaž (Mentor) More about this mentor... ID Črnčič, Luka (Comentor)
Files:	MAG_Kuk_Gal_2026.pdf (1,84 MB) MD5: 93125C18472D9E98695EABB3023753AA
Language:	Slovenian
Work type:	Master's thesis/paper
Typology:	2.09 - Master's Thesis
Organization:	FS - Faculty of Mechanical Engineering
Abstract:	V magistrskem delu smo raziskovali uspešnost napovedovanja mesečnih naročil v podjetju iz plastično-predelovalne industrije, kjer visok delež materiala v lastni ceni in nihanja povpraševanja otežujejo načrtovanje proizvodnje in nabave. Namen naloge je bil ugotoviti, ali napredni modeli za analizo časovnih vrst prinašajo dodano vrednost v primerjavi z enostavnimi pristopi na specifičnih podatkih izbranega podjetja. Teoretični del temelji na analizi časovnih vrst in njihovih komponent (trend, sezonskost, cikel in slučajna komponenta), na konceptu stacionarnosti ter na statističnih testih, ki jo preverjajo (razširjeni Dickey-Fullerjev in KPSS test). Predstavljeni so postopki priprave podatkov (STL dekompozicija, winsorizacija), merila natančnosti napovedi (MAE, RMSE, MAPE) ter diagnostika ostankov s testoma Ljung-Box in Kolmogorov-Smirnov. Obravnavane napovedne metode so naivna in sezonska naivna metoda, modeli eksponentnega glajenja (ETS), modeli ARIMA ter model NNAR. V empiričnem delu smo v programskem okolju R za tri artikle podatke vnaprej procesirali, analizirali in vizualizirali, nato pa izdelali trimesečne napovedi. Rezultati kažejo, da enotnega najboljšega modela ni: pri prvem artiklu se je najbolje odrezal model NNAR (MAPE 16,8 %), pri drugem ETS (25,8 %) in ARIMA (25,9 %), pri tretjem pa naivna metoda (19,9 %). Diagnostika ostankov je izločila več modelov z navidezno nizko napako, kar potrjuje, da vrednost MAPE sama po sebi ni zadosten kriterij za izbiro modela. Rezultati podjetju omogočajo neposredno uporabo napovedi pri načrtovanju kapacitet, nabavi surovine in določanju varnostnih zalog.
Keywords:	napovedovanje, časovne vrste, napredne metode, ARIMA, ETS, NNAR
Place of publishing:	Maribor
Year of publishing:	2026
PID:	20.500.12556/DKUM-99336
Publication date in DKUM:	03.09.2026
Views:	234
Downloads:	8
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Categories:	KTFMB - FS
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Licences

License:	CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Link:	http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:	The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:	13.08.2026

Secondary language

Language:	English
Title:	Forecasting order quantities in the plastics industry using advanced forecasting methods
Abstract:	This master's thesis examines the accuracy of monthly order forecasting in a plastics processing company, where a high share of material costs and volatile demand complicate production and procurement planning. The purpose was to determine whether advanced time series models offer added value over simple approaches on the specific data of the selected company. The theoretical part is based on time series analysis and its components (trend, seasonality, cycle, and the irregular component), on the concept of stationarity and the statistical tests used to assess it (Augmented Dickey-Fuller and KPSS). It further covers data preparation procedures (STL decomposition, winsorization), forecast accuracy measures (MAE, RMSE, MAPE), and residual diagnostics using the Ljung-Box and Kolmogorov-Smirnov tests. The forecasting methods examined are the naive and seasonal naive methods, exponential smoothing (ETS) models, ARIMA models based on the Box-Jenkins methodology, and a neural network autoregression model (NNAR). In the empirical part, data for three products were preprocessed, analyzed, and visualized in the R environment, followed by three-month forecasts. The results show that no single model is universally best: NNAR performed best for the first product (MAPE 16.8%), ETS (25.8%), and ARIMA (25.9%) for the second, and the naive method (19.9%) for the third. Residual diagnostics eliminated several models with seemingly low error, confirming that MAPE alone is not a sufficient criterion for model selection. The results allow the company to apply the forecasts directly in capacity planning, raw material procurement, and safety stock determination.
Keywords:	forecasting, time series, advanced methods, ARIMA, ETS, NNAR

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