On-time delivery is essential in today’s dynamic conditions: if a company cannot produce and deliver on time, it has to make up for it by using high cost express delivery or faces customer dissatisfaction. One factor influencing the delivery reliability is the due date performance (DDP) within production. Although the significance of DDP has been established, the question of how to measure it remains. A review of existing literature shows the vast amount of different DDP measures (lateness, relative lateness, tardiness, schedule reliability, etc.). The purpose of this paper is to compare different DDP measures used in manufacturing in order to assess their interrelationship, so that companies are better able to understand the impact of their choice of measure. A review of DDP measures described in literature is performed, followed by statistical analysis of the relations between those measures computed on production feedback data from four real-world manufacturers. The results indicate that there exist differences across DDP measure groups. Further research is needed to assess the benefits of each measure in a given situation.
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
2.2.1 Output Lateness, Absolute Lateness, Squared Lateness and Tardiness
The most basic measure is that of output lateness, also sometimes denoted as job lateness. This describes the concrete time difference between the planned and actual end date of an operation (i). It is a measure often used in connection to scheduling techniques and their impact on lateness and system performance. Referring back to Figure 1, one can formulate output lateness Li, out as follows: Li,out = ti,end - ti,endplan (1)
In cases of lateness, the computed output will be negative. In the opposite case, positive values will be the result of early production completion. However, one of the main disadvantages of this DDPM is the acceptance of early production finish dates as a positive case, whilst studies have suggested the negative impact of earliness.
A second approach addresses the issue of the negative consequences of early completion of jobs, such as high inventory levels. Absolute lateness is calculated in a similar manner as output lateness, but as the expression suggests, it considers absolute rather than positive and negative values. The mathematical notation of absolute lateness ALi of operation (i) can be expressed as follows: ALi = |ti,end - ti,endplan| (2)
As before, the point of scheduled and of actual completion are being considered. For both, early and late completion, positive values are retrieved. Absolute lateness is regarded an accuracy indicator between predicted and real values and sums up tardiness as well as earliness.
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.
- Citar trabajo
- Ricarda Schäfer (Autor), 2016, What is Really "On-Time"? A Comparison of Due Date Performance Indicators in Production, Múnich, GRIN Verlag, https://www.grin.com/document/370576
