A maturity model to determine the degree of utilization of machine learning in production planning and control processes

Aug 3, 2026

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This master’s thesis develops a maturity model for assessing how extensively machine learning is utilized within production planning and control processes. It establishes theoretical foundations spanning machine learning concepts and PPC process structures, then synthesizes findings from existing maturity models and related research through analytical methods such as SWOT. The work designs the model, defines maturity levels, specifies design fields and dimensions, and formulates a maturity model-based design approach to structure evaluation and improvement of ML adoption.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages112

Reading Time4 h 42 min

AccessFree to download

Introduction

Initial Situation

Objective and Research Question

Structure of the Work

Theoretical Foundations

Machine Learning

PPC Processes

Total System Challenge

Maturity Model

State of the Art

SWOT Analysis

Related Works & Research Fields

Analysis and Results

Analysis of present Maturity Models

Basic Design of the Maturity Model

Maturity Model with Design Fields & Dimensions

Technische Hochschule Ingolstadt Faculty of Engineering and Management Master’s course Engineering and Business Master’s thesis Subject: A maturity model to determine the degree of utilization of machine learning in production planning and control processes Name: Jakob Joseph Lorenz Hartl Issued on: April 17, 2023 Submitted on: October 17, 2023 First examiner: Prof. Dr. Jürgen Bock Second examiner: Prof. Dr. Alexander Schiendorfer Declaration I hereby declare that this thesis is my own work, that I have not presented it elsewhere for examination purposes and that I have not used any sources or aids other than those stated. I have marked verbatim and indirect quotations as such. Ingolstadt, October 10, 2023 Table of Contents List of Figures ........................................................................................................................................ V List of Tables......................................................................................................................................... VI List of Abbreviations............................................................................................................................ VII Abstract .............................................................................................................................................. VIII 1 Introduction ................................................................................................................................... 1 1.1 Initial Situation ....................................................................................................................... 1 1.2 Objective and Research Question...........................................................................................3 1.3 Structure of the Work.............................................................................................................4 2 Theoretical Foundations ................................................................................................................5 2.1 Machine Learning ...................................................................................................................5 2.1.1 Basics of AI supported Data Processing ..........................................................................5 2.1.2 Types of Artificial Intelligence.........................................................................................7 2.1.3 Machine Learning without Neural Networks ..................................................................8 2.1.4 Machine Learning with Neural Networks .....................................................................10 2.1.5 Back Propagation Algorithm ......................................................................................... 11 2.1.6 Data Clustering ............................................................................................................. 13 2.1.7 Preparation and Selection of Data for the Training ......................................................14 2.2 PPC Processes ....................................................................................................................... 15 2.2.1 Production Fundamentals............................................................................................. 15 2.2.2 Production System ........................................................................................................ 16 2.2.3 Production Planning and Control System .....................................................................18 2.3 Total System Challenge ........................................................................................................21 2.3.1 Digital Business Transformation ...................................................................................22 2.3.2 Industry 4.0........................................................................................

FAQs

What is the purpose of the maturity model in this thesis?

It provides a structured way to determine the degree of utilization of machine learning in production planning and control processes.

Which topics are covered in the theoretical foundations?

The thesis covers machine learning basics, PPC process fundamentals, and the broader challenges of digital transformation and Industry 4.0, culminating in maturity model theory.

How is the maturity model developed and validated in the analysis?

The work analyzes existing maturity models, then designs the proposed model by defining maturity levels, design fields, and dimensions using a maturity model-based design approach.

Aug 3, 2026

Upload by Aria