Developing Machine Learning-Based Models for Optimal Maintenance Scheduling Within the Food Industry - Conference presentation

Aug 5, 2026

Upload by Aurelia

Unplanned machine downtimes in the food industry case study primarily stem from ineffective maintenance scheduling, which erodes organisational profit. The study applies predictive maintenance to reduce machine inefficiencies and strengthen decision-making. It first introduces predictive maintenance and machine learning theory, then develops random forest and decision tree models. After preprocessing company data, models are trained and evaluated using cross-validation and hyperparameter tuning. One-year ahead forecasts for three retail line machines support proactive maintenance planning to cut unplanned downtime and improve production processes.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Introduction

Proactive maintenance and continuous improvement

Types of machine learning techniques

Background: maintenance across industrial revolutions

Reactive, preventive, productive, predictive maintenance

DEVELOPING MACHINE LEARNING-BASED MODELS FOR OPTIMAL MAINTENANCE SCHEDULING WITHIN THE FOOD INDUSTRY R. Oosthuizen1* , C. Bisset2 and C. Du Plessis3 1,2,3School of Industrial Engineering North-West University, South Africa [1](1Ruhanoo13@gmail.com)[Ruhanoo13@gmail.com](1Ruhanoo13@gmail.com), [2](2Chanelbisset26026856@gmail.com)[Chanelbisset26026856@gmail.com](2Chanelbisset26026856@gmail.com), [3](3Chantelle.Coetzee@nwu.ac.za)[Chantelle.Coetzee@nwu.ac.za](3Chantelle.Coetzee@nwu.ac.za) ABSTRACT In the context of the food industry, precisely the company used as a case study, unplanned machine downtimes are mainly caused by ineffective maintenance scheduling that impacts organisational profit. To address this challenge, the study explores predictive maintenance within the food industry and its application to reduce machine inefficiencies and improve overall decision-making. First, a theoretical background on predictive maintenance and machine learning is provided, followed by the development of the random forest and decision tree models. Company data is pre-processed, and the models are trained and tested using scientific methods from academic literature, including cross-validation and hyperparameter tuning. One-year future predictions are made for three retail line machines, aiding in proactive maintenance decision-making to reduce unplanned machine downtime. Subsequently, this study contributes towards academia and industry by providing actionable insights for optimising maintenance scheduling and production processes in the food industry. Keywords: Machine learning, Maintenance scheduling, Food industry, Predictive maintenance * Corresponding Author 1 INTRODUCTION All machines, equipment, and devices responsible for producing products are bound to wear and tear. Records of maintenance activities date back to ancient Egyptian times. An old Egyptian document dated 600 b.c. mentions a stoppage of cedar wood required to maintain the sacred boat of Amun Ra [1] . The Industrial Revolution incentivised the need for maintenance strategies to be redesigned to adhere to the advancements in technology in the industrial sector. Table 1 discusses the impact of the Industrial Revolution on maintenance practices. Table 1 : The correlation between the industrial revolutions and maintenance types [1] | Industrial
revolution | Type of
Maintenance | Overview of the correlation between the Industrial Revolution and maintenance type | | --- | --- | --- | | Industry 1.0 | Reactive | The first industrial revolution began in England, transforming energy sources, transportation, information transfer, and manufacturing. James Watt's invention of the steam engine in 1765 marked the beginning. The increased complexity of machines and increased productivity led to the evolution of maintenance methods. Reactive maintenance was costly and caused unanticipated downtime and productivity losses. | | Industry 2.0 | Preventive | The Second Industrial Revolution began in 1870, revolutionising society with mass manufacturing and inventions. As machines became more complex, preventative maintenance emerged to reduce downtime and improve equipment performance. Manufacturers now focus on routine inspections, maintenance, and repairs to prevent breakdowns and maintain equipment's overall performance. | | Industry 3.0 | Productive | Advancements in manufacturing, computer technology, and marketing and management procedures marked the Third Industrial Revolution. Productive Maintenance (PM) emerged after World War II, combining Corrective and Preventive Maintenance with an analytical, data-driven approach. PM improves equipment lifespans, reduces downtime, and reduces costs. Reliability-centered Maintenance (RCM) and Total Productive Maintenance (TPM) were developed during this time. RCM focuses on determining maintenance needs for physical assets, addressing issues like neglected maintenance or wear, and improving machine uptime, cost-effe

FAQs

Why does unplanned machine downtime occur in the food industry case study?

Unplanned downtime mainly results from ineffective maintenance scheduling, which negatively impacts organisational profit.

Which machine learning models are developed for predictive maintenance?

The study develops random forest and decision tree models for predictive maintenance.

How are the models validated and used for maintenance decisions?

Company data is pre-processed, then models are trained and tested using academic methods such as cross-validation and hyperparameter tuning. The models generate one-year future predictions for three retail line machines to enable proactive maintenance planning.

Aug 5, 2026

Upload by Aurelia