Prediction of Air Handling Unit Module Assembly Times with Machine Learning

Aug 3, 2026

Finding the optimal scheduling for manufacturing systems is crucial for maximising profitability and maintaining promised delivery times, yet optimisation becomes difficult as systems grow more intricate, dynamic, and connected. When data to estimate process times is missing or incomplete, estimates rely on process study and traditional analytical modelling or simulation. With increased data availability, data-driven approaches are explored. This thesis evaluates supervised machine learning for air handling unit module assembly times versus analytical modelling.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages83

Reading Time3 h 29 min

AccessFree to download

Abstract

Problem and motivation

Research goals

Methods

Results

Oskari Oksanen PREDICTION OF AIR HANDLING UNIT MODULE ASSEMBLY TIMES WITH MACHINE LEARNING Master of Science Thesis Faculty of Engineering and Natural Sciences Examiners: University instructor Hasse Nylund Professor Minna Lanz October 2024 i ABSTRACT Oskari Oksanen: Prediction of air handling unit module assembly times with machine learning Master of Science Thesis Tampere University Master’s Programme in Mechanical Engineering October 2024 Finding the optimal scheduling for a production of a manufacturing system is crucial if the system’s profitability is to be maximised. Also, scheduling production and adhering to promised delivery times is an important part of customer satisfaction. But, optimisation of the production schedule is in many cases extremely difficult as manufacturing systems have become more intricate, dynamic and connected than ever before. In addition, data necessary for accurately estimating the processing times of individual processes in a manufacturing system may be missing or incomplete, in which case the estimations must be based on a process study. Traditionally, the processing times are estimated by applying analytical modelling methods or simulation. However, as the amount of collected data from manufacturing systems has increased, applying data-driven approaches for estimating the processing times has become more common. Therefore, applying the data-driven approaches might be a better option than the traditional method, if the data being collected from the manufacturing system is consistent and includes more relevant information than noise. The goal of this study is to explore whether supervised machine learning methods can predict the processing times of an air handling unit module assembly process more accurately than two process study-based analytical modelling methods. Another goal of this study is to develop a method, which utilizes the collected data to predict the times required to complete various working stages of the assembly process. The machine learning methods that are applied in the problem of this study are Multiple linear regression, K-Nearest Neighbor, Random forest and Multi-layer neural network. In this study, data analysis is used to examine the quality of the collected data from the assembly process and the suitability of the data to apply the machine learning methods for the problem of this study. Results of the study show that among the aforementioned machine learning methods, the Random forest model is the most suitable for predicting the processing times of the assembly process. The Random forest model predicts the processing times of air handling unit modules in the test data with a mean absolute error of 1 hour and 6 minutes, whereas a coarse analytical modelling method model achieves a mean absolute error of 2 hours and 49 minutes. Thus, it can be concluded that the Random forest model can outperform the coarse analytical modelling method model. The times required to complete working stages of the assembly process cannot be extracted from the available data using only supervised machine learning methods. Hence, a hybrid model was developed, combining a fine analytical modelling method with the Random forest method, to estimate the durations of the various working stages. Keywords: machine learning, regression, production scheduling, leadtime, product completion time, assembly The originality of this thesis has been checked using the Turnitin OriginalityCheck service. ii TIIVISTELMÄ Oskari Oksanen: Ilmanvaihtokoneen modulien kokoonpanoaikojen ennustaminen koneoppimisella Diplomityö Tampereen yliopisto Konetekniikan DI-ohjelma Lokakuu 2024 Tuotantojärjestelmän aikataulutuksen optimointi on tärkeää, mikäli tuotantojärjestelmän tuottavuus halutaan maksimoida. Tuotannon aikatauluttaminen ja luvatuista toimitusajoista kiinnipitäminen on myös tärkeä osa asiakastyytyväisyyttä . Tuotantojärjestelmän aikataulutuksen optimointi on kuitenkin useis

FAQs

What problem does the thesis address?

The thesis addresses predicting processing times for an air handling unit module assembly process to support production scheduling and reliable delivery times.

Which machine learning models are tested?

Multiple linear regression, K-Nearest Neighbor, Random forest, and a multi-layer neural network are evaluated using collected assembly data.

What is the main finding about model performance?

The Random forest model performs best for predicting assembly processing times, achieving a mean absolute error of 1 hour and 6 minutes on test data, better than a coarse analytical modelling approach.

Aug 3, 2026

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