What is Really "On-Time"? A Comparison of Due Date Performance Indicators in Production

Table of Contents

1 Introduction

1.1 Problem of the thesis

1.2 Aims of the thesis

1.3 Methodological Framework

2 Literature Review

2.1 Background and Definitions

2.2 Overview of Due Date Performance Measures

2.2.1 Output Lateness, Absolute Lateness, Squared Lateness and Tardiness

2.2.2 Relative Lateness

2.2.3 Binary Lateness and Schedule Unreliability

2.3 Classification Approach

3 Methodology

3.1 Data Overview & Data Cleaning

3.2 Lateness Computations

3.3 Interrelationship Analysis

4 Findings

4.1 Lateness Computations

4.2 Interrelationship Analysis

5 Discussion

6 Final Consideration

6.1 Results and critical reflections

6.2 Implications for further research

6.3 Implications for practice

Research Objectives and Themes

The primary goal of this thesis is to examine and compare various Due Date Performance Measures (DDPMs) used in production systems to understand their interrelationships and evaluate how the choice of indicator impacts performance assessment and bottleneck identification.

• Review and classification of established due date performance measurement methods.
• Empirical analysis of lateness values derived from production feedback data across four manufacturers.
• Statistical assessment of the interrelationship between different performance indicators.
• Critical discussion on the implications of selecting specific performance indicators for industrial practice.

Excerpt from the Book

Summary of Chapters

1 Introduction: This chapter introduces the challenges of modern production environments, defines key concepts like delivery reliability and due date performance, and outlines the research motivation.

2 Literature Review: An overview of fundamental terms and existing due date performance measures is provided, culminating in a classification approach that categorizes them based on their mathematical properties.

3 Methodology: The section details the empirical research design, including the criteria for data cleaning, the two-level computation approach (machine and weekly order level), and the statistical methods used for interrelationship analysis.

4 Findings: The results of the statistical tests (Friedman-Test, Wilcoxon signed-rank test, and Spearman correlation) are presented to demonstrate the impact of different DDPM choices on computed lateness results.

5 Discussion: The findings are evaluated regarding their practical significance, specifically focusing on the critical role of performance indicator selection in identifying production bottlenecks.

6 Final Consideration: The thesis concludes with a critical reflection on the results, addresses the study's limitations, and provides specific implications and recommendations for both future research and industrial practice.

Keywords

Due Date Performance, Production Scheduling, Delivery Reliability, Lateness Measures, Schedule Reliability, Statistical Analysis, Friedman-Test, Wilcoxon Test, Bottleneck Identification, Production Logistics, Performance Indicator, Throughput Time, Industry 4.0, Supply Chain, Operations Management.

Frequently Asked Questions

What is the core focus of this thesis?

The thesis focuses on evaluating different Due Date Performance Measures (DDPMs) to understand how they function and how their selection influences the perception of production performance.

What are the primary themes discussed in the work?

Key themes include the mathematical definition of lateness measures, the impact of scheduling techniques, statistical comparability of performance indicators, and the implications for identifying bottlenecks in a manufacturing context.

What is the central research question?

The central question is how different ways of measuring due date performance correlate with one another and whether the choice of a specific measure significantly alters the results and the subsequent management decisions.

Which scientific methods are applied to the data?

The study utilizes empirical statistical methods, including normality testing (Shapiro-Wilks), the non-parametric Friedman-Test for comparing groups, Spearman correlation for association analysis, and the Wilcoxon signed-rank test for paired comparisons.

What is covered in the main section of the document?

The main section covers an extensive literature review on DDPMs, a detailed classification of these measures, the data cleaning process for real-world manufacturer datasets, and the execution of various statistical analyses to compare the chosen indicators.

Which keywords best characterize the work?

The work is characterized by terms such as Due Date Performance, Lateness Measures, Schedule Reliability, Production Scheduling, and Performance Indicator Analysis.

Why are "Binary Lateness" and "Schedule Unreliability" considered system-level measures?

They are considered system-level measures because they do not track the extent of lateness for individual operations but rather categorize operations into binary states (on-time vs. late), making them most meaningful when aggregated as means across an entire system.

How does the author define the difference between "Lateness" and "Tardiness"?

Lateness accounts for both early and late completion (with earliness potentially balancing out lateness), whereas Tardiness is a one-sided measure that ignores early completion, only assigning positive values to delayed operations.

What is the practical implication regarding bottleneck identification?

The research concludes that because different measures can lead to different interpretations of system performance, choosing the wrong DDPM may result in focusing improvement efforts on the wrong machines, potentially leading to sub-optimal system outcomes.