A Maturity Model to Determine the Degree of Utilization of Machine Learning in Production Planning and Control Processes - Abstract

Aug 4, 2026

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The work presents a maturity model to evaluate Machine Learning implementations, primarily targeting Production Planning and Control processes while also covering relevant organizational and technical factors. Built after analyzing 14 existing maturity models, it addresses research gaps by identifying success factors and obstacles across maturity levels, organized through defined dimensions and design fields. The structured design supports practical adoption, especially for small and medium-sized enterprises, enabling critical self-assessment and reflection to improve implementation outcomes.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Foundations

Maturity Models

Machine Learning

Production Planning and Control Processes

Analysis of Existing Maturity Models

Proposed Maturity Model

ISSN 1846-6168 (Print), ISSN 1848-5588 (Online) Original scientific paper [https://doi.org/10.31803/tg-20240513233046](https://doi.org/10.31803/tg-20240513233046) Received: 2024-05-13, Accepted: 2024-05-15 A Maturity Model to Determine the Degree of Utilization of Machine Learning in Production Planning and Control Processes Jakob Hartl, Jürgen Bock* Abstract: The presented work introduces a maturity model for evaluating Machine Learning implementations, with a primary focus on Production Planning and Control processes, as well as broader organizational and technical aspects in companies. This model emerges as a response to the research gap identified in the analysis of 14 existing maturity models, which served as foundational bases for the development of this novel approach. By examining success factors and obstacles at different maturity levels, categorized according to defined dimensions and overarching design fields, this model can serve as a catalyst for bridging the research gap between models demanded in practice and the scholary exploration of topics related to Machine Learning in corporate processes. Notably, the structured design of this maturity model ensures accessibility for small and mediumsized enterprises (SMEs) . Keywords: machine learning; maturity model; production planning and control; project success; SME; success factors 1 INTRODUCTION With the rise of Artificial Intelligence (AI) technologies and applications, our society is witnessing a strong impact in day-to-day tasks through intelligent software solutions. Particularly prediction and generative models become more and more present in private and business environments. However, since most AI technologies require large amounts of training data, application-ready solutions are available mostly in domains where large amounts of (high quality) data is available. While this is clearly the case in internet-based information systems, where consumers voluntarily share and annotate texts, images, audios and videos, the situation is more difficult in industrial environments. Particularly in the realm of production planning and control (PPC) only few companies, are leveraging the potential brought about by AI and Machine Learning (ML) . Theoretical research continues to push the boundaries of ML methods, while the industry, particularly small and medium-sized enterprises (SMEs), is primarily focused on implementing fundamental functions with respect to digitalization. Despite this, companies are eager to invest in AI disciplines such as ML due to the perceived importance of future business process enhancements. However, the realization of these benefits through practical implementation remains elusive. A study of Gupta [1] shows that 78% of AI or ML projects do not reach a stage where they are ready for deployment in a productive environment. Fig. 1 shows the various reasons for early cancellation. In order to overcome such setbacks and improve the success rate of ML implementations, a systematic analysis of the ML-readiness and critical self-assessment and reflection is required by companies. This paper contributes to the state of the art by introducing a maturity model for companies to determine the degree of utilization of ML in PPC processes. To this end, Section 2 introduces the basic concepts required in the scope of this paper. Section 3 provides a systematic analysis of 14 existing maturity models, which results in the novel maturity model introduced in Section 4. Figure 1 Machine Learning projects failure rate [1] 2 FOUNDATIONS This section introduces the core concepts of maturity models, Machine Learning, and production planning and control processes, as a basis for the remainder of this paper. 2.1 Maturity Models Maturity models serve the purpose of assessing the present status of an application and pinpointing areas for potential enhancement [2]. They have emerged as a valuable tool for facilitating the execution of organiz

FAQs

What problem does the proposed maturity model address?

It addresses the research and practical gap in evaluating ML implementation readiness for productive environments, especially within production planning and control contexts.

How was the maturity model developed?

The model is derived from an analysis of 14 existing maturity models, using identified success factors and obstacles to define maturity levels, dimensions, and design fields.

Why is the model designed to support SMEs?

Its structured and accessible design helps smaller and medium-sized enterprises conduct critical self-assessment and reflect on ML utilization progress, improving adoption chances.

Aug 4, 2026

Upload by Liam