A New Methodology for Evaluation of Worker Performance in the Manufacturing Process LE Song Thanh Quynh Japan Advanced Institute of Science and Technology Doctoral Dissertation A New Methodology for Evaluation of Worker Performance in the Manufacturing Process 1720027 LE Song Thanh Quynh Supervisor: Professor HUYNH, Nam-Van Graduate School of Advanced Science and Technology Japan Advanced Institute of Science and Technology [Knowledge Science] March, 2020 Abstract The production environment has a lot of revolutions in recent decades, with most companies taking part in mass customization production. The style of products, quality requirements from customers, materials, and even the machines involved in manufactur- ing are evolving quickly and orders are decreasing in size. In this situation, the employee is the important factor that determines the productivity and quality of product in a pro- duction process. This is why the selection of the right workers for operating tasks in an assembly line is always an important question, especially today because many tasks are becoming increasingly complex, as they must deal with the development of technologies, materials, and machines in the manufacturing process.
If a task is more complex, the worker needs more skill and time to finish it. For all of the main purposes of the manufac- turing enterprise, such as planning and scheduling, operators training or line balancing, the main requirement is almost always on predicting operator performance. In a manufacturing process, the performance of the worker can be identified as their abil- ity to accomplish a task based on the expectations of a standard. To determine how well a worker performs their job, various performance evaluation techniques can be used, such as the Synthetic Rating, Pace Rating method or the Westinghouse system.
These methods have been applied recently to calculate operator performance ratings. The three traditional performance methods just apply effectively in the manufacturing process that the workstation is designed well. In these contexts, the manufacturing scheduling is completely based on machine capacity and the task characteristics remain consistent be- tween customer’s requirements. This makes it simple to set up standards to compare orders.
Additionally, the impact of employee performance on production capacity is ac- counted for by very large orders. That is, workers have adequate time to meet the target performance, so production managers are not concerned with calculating operator skill level and task complexity to predict whether a worker’s performance capacity is best suited to a specific task. Further, in this conventional context, operator skills are learned and improved through comprehensive, industry standard training, and skill are enhanced gradually through precise, continuous repetitions of work processes. However, in the new manufacturing environment, the worker’s performance results from the interaction be- tween the skill levels of workers and the fluctuation of the characteristics of tasks.
The 1 new and changing environment of the manufacturing industry, however, means that the usual ways of allocating workers tasks are less effective at forecasting workers’ performance requirements. Moreover, such outdated approaches also lack success in driving workers to gain and master the new skills required to enhance quality and productivity. In addition, managers base their decisions only on their previous experience without the support of a systematic knowledge base. They merely observe the operation of workers and evaluate their performance based on subjective judgments.
The accuracy of these judgments will mainly be dependent on the amount of experience the manager possesses. My research proposal aims to propose a new methodology for the prediction of worker per- formance in manufacturing that is capable of effectively handling multiple factors of both a quantitative and qualitative nature that involve uncertainty and imprecision. Firstly, a methodology for evaluating worker skill levels is devised with the combination of the Delphi method, the principal component analysis and the ordinal logistic regression. Sec- ondly, this research presents a method that combines the Analytic hierarchy process and Proportional 2-tuple linguistic representation model to evaluate the level of complexity of tasks in the manufacturing process.
With regard to how the worker skill level and the complexity level of a task is evaluated, this research will pay closer attention to analysis of the relationship between task complexity and worker skill level, to clearly understand the interaction between them in order to predict the performance of workers. The newly developed methodology will be illustrated with a case study in the clothing industry to demonstrate its practical applicability in industrial contexts. Keywords: worker’s performance, skill level of worker, task complexity, decision support technique, rule-based support system. 2 Acknowledgments In order to carry out and complete my Ph.
program, I have received much support as well as consideration and encouragement from many organizations and individuals. research is completed based on references and learning from previous research, books and specialized papers from many authors in the research community. In partic- ular, I am grateful for the education and support of the professors at Japan Advanced Institute of Science and Technology and the spiritual support from my family, friends, and colleagues. First of all, I would like to express my deep thanks to Professor HUYNH, Nam Van—my main supervisor who has spent a lot of time and effort to guide me throughout the process of researching and completing my Ph.
Without his guidance and support, this research would not have been completed. I would like to thank all of the teachers in the Knowledge Science school who have conveyed knowledge and supported me in the learning and research process with such dedication. Especially, I would like to acknowledge my second supervisor—Professor Takashi Hashimoto and my minor research supervisor—Professor Youji Kohda who taught me a lot of knowledge in the research process. I gratefully acknowledge my parents, my family, and my daughter who always encour- age and support me spiritually.
In particular, I am very grateful to my husband; if I did not receive his encouragement, I could not accomplish my dream. I am grateful to have him as my husband. I would also like to thank the Ministry of Education and Training, Vietnam, which gave me the full Project scholarship that allowed me fulfill my dream of being a Ph. student at the Japan Advanced Institute of Science and Technology.
I gratefully acknowledge all of the HUYNH-lab members, for all their friendship, en- thusiasm, and encouragement for supporting me in research. HUYNH-lab is my big family at Japan Advanced Institute of Science and Technology. 3 Lastly, I would also like to thank all of my colleagues at Ho Chi Minh City University of Technology, Vietnam for sharing the teaching duty and giving me an opportunity to pursue my Ph. degree at JAIST.
Thank you, LE, Song Thanh Quynh JAIST, February 2020 4 Table of Contents Abstract 1 Acknowledgments 3 Table of Contents 5 List of Figures 7 List of Tables 8 1 Introduction 11 1.1 Worker’s performance measurement methods .1 Westinghouse System Method .2 Synthetic Rating Method .2 Disadvantage of previous methods. 21 3 Grading Operator Skill Using Principal Component Analysis and Ordi- nal Logistic Regression 23 3.2 Principal Component Analysis .3 Ordinal Logistic Regression .3 A Methodology for grading operator skill level .1 Step 1- Identifying the Factors Affecting Worker Skill Levels Using the Delphi Method .2 Step 2 - Reducing These Qualitative Variables by Using Principal Component Analysis .3 Step 3 - Ranking and Predicting the Sewing Worker Skill Level by Applying Ordinal Logistic Regression .4 Results and Verification .1 Results of the Proportional Odds Model .2 Results of the Partial Proportional Odds Model. 39 4 An evaluation methodology for the complexity level of tasks 42 4.1 Analytic hierarchy process .2 Proportional 2-tuple linguistic representation model .3 The proposed approach .1 Identify the criteria that affect the complexity level of sewing tasks 51 4.2 Develop the hierarchical structure of task complexity .3 The linguistic setting for these attributes .4 Applying the Proportional 2-tuple linguistic for estimating the com- plexity level of sewing task. 59 5 Predicting Worker Performance Using A Decision Tree 63 5.1 Data mining in manufacturing process .3 Predicting worker performance.
87 Biblography 89 Publications 96 7 List of Figures 1.1 The research process.1 Five basic motions of the finger.1 The example of the questionnaire .1 The procedure for evaluating the complexity levels of task.2 Hierarchical structure of task complexity .3 Five trapezoidal linguistic term set of the weight of the fabric .4 Five trapezoidal linguistic term set of the elasticity of the fabric .5 Five trapezoidal linguistic term set of length of seam .1 The basic structure of decision tree .3 Fuzzy linguistic for three quantitative sub-criteria of task complexity.4 Menswear Formal Shirt Garment Specification Sheet.5 The final decision tree for classifying the performance. 80 8 List of Tables 2.1 The Westinghouse system.1 Three kinds of logistic regression model.2 Six elements for grading sewing skill levels of workers.3 Experts’evaluation scores .4 Eigenalysis of the covariance matrix .5 Results of the proportional odds model .6 Results of the partial proportional odds model .7 Model verification data .8 The results of the Mann-Whitney Test .3 Pairwise and weight of characteristic of material sub-criteria .4 Pairwise and weight of type of method used sub-criteria .5 Pairwise and weight of criteria .6 Linguistic values of trapezoidal fuzzy numbers for the weight of the fabric.7 Linguistic values of trapezoidal fuzzy numbers for the elasticity of the fabric.8 Linguistic values of trapezoidal fuzzy numbers for the length of seam.9 Linguistic values of trapezoidal fuzzy numbers for three qualitative attributes.10 The CCV and Trapezoidal fuzzy number of weight of decision marker .11 The evaluation matrix provided by expert E1 .12 The evaluation matrix provided by expert E2 .13 The evaluation matrix provided by expert E3 .14 The overall proportional 2-tuple linguistic comprehensive evaluation matrix L 61 9 5.1 The levels of performance rating of worker.3 The performance of the model. 81 10 Chapter 1 Introduction Section 1.1 introduces the background to my research.2 outlines my research motivations and goals. The ways this research contributes to the wider field are detailed in Section 1.4 provides the structure of this thesis.1 Background The manufacturing environment has changed drastically in recent decades, with most companies taking part in mass customization production [1].
Product designs, customer quality requirements, materials, and even equipment now change at a rapid pace. Cus- tomer’s orders are constantly decreasing in size. Further, today’s customers have more demands than previously in terms of product quality, cost, and delivery time: these must be higher, lower, and non-negotiable with a significant penalty given for any delay, re- spectively. Due to these changes, workers in an assembly line are required to learn a lot of new tasks far more frequently.
As product cycle times and production runs compress, workers require constantly updated skills, technologies, and processes to align with the altered pace. The most important factor in the manufacturing process for predicting the effectiveness of an assembly line is the worker’s performance. When setting up an assembly line, worker’s performance is often chose with care to complete tasks using a range of measures, including standard productivity, quality requirements, task natures, and skill level requirements [2]. Of these, skill level of worker and task characteristics are the factors that receive the most 11 consideration when assigning or re-assigning workers to a task.
A mixture of employee skill level and nature of task determines operator performance. To figure out how well workers might complete a task, performance evaluation methods are often adopted. In recent times, several such techniques have been used to systematically set out worker performance ratings. These include the Speed rating method, the Syn- thetic Rating, and the Westinghouse system.
The only factor considered by the Speed rating method is the employee’s speed operation. To determine this, the manager detects the speed with which the worker operates and measures this against the level expected. In doing so, they are able to consider the link between the two to determine the rating speed factor, which can be used for various factors.