INTEGRATING QUALITY TOOL ON IMPROVING OVERALL EQUIPMENT EFFICIENCY IN AUTOCLAVE PROCESS: CASE STUDY.

INTEGRATING QUALITY TOOL ON IMPROVING

OVERALL EQUIPMENT EFFICIENCY IN AUTOCLAVE

PROCESS: CASE STUDY

CHAN YUN KIM

B050810046

UNIVERSITI TEKNIKAL MALAYSIA MELAKA 2012

UNIVERSITI TEKNIKAL MALAYSIA MELAKA

INTEGRATING QUALITY TOOL ON IMPROVING OVERALL

EQUIPMENT EFFICIENCY IN AUTOCLAVE PROCESS:

CASE STUDY

This report submitted in accordance with requirement of the Universiti Teknikal Malaysia Melaka (UTeM) for the Bachelor Degree of Manufacturing Engineering

(Manufacturing Management) (Hons.)

by

CHAN YUN KIM B050810046 880107-05-5086

FACULTY OF MANUFACTURING ENGINEERING 2012

UNIVERSITI TEKNIKAL MALAYSIA MELAKA

BORANG PENGESAHAN STATUS LAPORAN PROJEK SARJANA MUDA

TAJUK: INTEGRATING QUALITY TOOL ON IMPROVING OVERALL EQUIPMENT EFFICIENCY IN AUTOCLAVE PROCESS: A CASE STUDY

SESI PENGAJIAN: 2011/12 Semester 2

Saya CHAN YUN KIM

mengaku membenarkan Laporan PSM ini disimpan di Perpustakaan Universiti Teknikal Malaysia Melaka (UTeM) dengan syarat-syarat kegunaan seperti berikut: 1. Laporan PSM adalah hak milik Universiti Teknikal Malaysia Melaka dan penulis. 2. Perpustakaan Universiti Teknikal Malaysia Melaka dibenarkan membuat salinan

untuk tujuan pengajian sahaja dengan izin penulis.

3. Perpustakaan dibenarkan membuat salinan laporan PSM ini sebagai bahan pertukaran antara institusi pengajian tinggi.

4. **Sila tandakan (√ )

SULIT

TERHAD

TIDAK TERHAD

(Mengandungi maklumat yang berdarjah keselamatan atau kepentingan Malaysiasebagaimana yang termaktub dalam AKTA RAHSIA RASMI 1972)

(Mengandungi maklumat TERHAD yang telah ditentukan oleh organisasi/badan di mana penyelidikan dijalankan)

Alamat Tetap:

251, Jalan Sri Pulai 15, Taman Sri Pulai 1, 70400 Seremban, N.S.

Tarikh:

Disahkan oleh:

Cop Rasmi:

Tarikh: _______________________

** Jika Laporan PSM ini SULIT atau TERHAD, sila lampirkan surat daripada pihak berkuasa/organisasi berkenaan dengan menyatakan sekali sebab dan tempoh laporan PSM ini perlu dikelaskan sebagai SULIT atau TERHAD.

DECLARATION

I hereby, declared this report entitled “Integrating Quality Tools on Improving OEE in Autoclave Process: A Case Study” is the results of my own research except as cited in references.

Signature :

Author’s Name : CHAN YUN KIM

APPROVAL

This report is submitted to the Faculty of Manufacturing Engineering of UTeM as a partial fulfillment of the requirements for the degree of Bachelor of Manufacturing Engineering (Manufacturing Management) (Hons.). The member of the supervisory committee is as follow:

………

i Projek Sarjana Muda merupakan subjek yang wajib diambil oleh mahasiswa Tahun 4 Ijazah Sarjana Muda sebagai kerja kursus akademik dan pengijazahan. Tujuan projek sarjana muda tersebut adalah untuk mendedahkan mahasiswa terhadap etika kejuruteraan, tingkah laku profesionalisme kejuruteraan, kecekapan komunikasi, membina keyakinan dan menanam sikap positif dalam menjalani dan mengaplikasikan theori yang telah dipelajari sepanjang pembelajaran. Tajuk projek yang telah dipilih ialah “Integrating Quality Tools on Improving OEE in Autoclave Process” yang bermatlamat untuk meningkatkan pretasi kecekapan peralatan di industri pembuatan komposit dengan menggunakan pelbagai kualiti teknik. Intipati kajian ini adalah untuk menyampaikan satu kertas kerja yang mengesahkan aplikasi tertentu untuk meningkatkan kecekapan peralatan melalui pelaksanaan teknik quality di peringkat yang berbeza. Penggunaan melalui pengiraan kecekapan peralatan telah digunakan sebagai teknik quality yang utama dan teras untuk meningkatkan pretasi kecekapan peralatan. Cause and Effect Diagram, FMEA dan SMED akan digunakan sebagai alat sokongan. Penyelidikan terhadap teknik quality telah dijalankan melalui penemuan mengenai pelaksanaan taknik quality tersebut dalam kajian dahulu. Metodologi yang dibentangkan mengenai konsep integrasi teknik quality akan membantu dalam kawasan perindustrian yang menggunakan OEE sebagai ukuran kecekapan peralatan.

ii Final year project is the compulsory subject taken by the final year Bachelor students as to complete their own measure course and graduation. Purpose of the final year project is to expose students to engineering practice, professionalism engineering behavior, communication skills, build self confidence and instill the right work attitude to apply their technical knowledge in future. The project chosen entitled “Integrating Quality Tools on Improving OEE in Autoclave Process” aims to improve the overall equipment efficiency performance measure in the research company of composite manufacturing based using different types of quality tools. Essence of this study is to develop a manual instruction on OEE improvement methodology through the implementation of quality tools and techniques in different stage. OEE is the main or core quality tools that decide to use for the improvement where Cause and Effect Diagram, FMEA and SMED will use as the supportive tools. The research on the tools is carried out through findings on historical implementation of journal study. Methodology presented will help in the industrial area which performs OEE measurement by proposing them with the integrating quality tools concept.

iii Give a precious dedicated to my beloved supervisor, parents, and my friends who

fully support me along this project study.

DEDICATION

iv First and foremost, I would like to express my deepest gratitude to my supervisor IR.DR. Puvanasvaran A/L A.Perumal, of Manufacturing Engineering, University Technical Malaysia Malacca who guiding me with his valuable knowledge and spend his time to assist me on overcome any difficulties during the project study. Besides that, I would like to thank to my friends for giving me supports and helps especially in period of uncertainties and difficulties.

Finally, I would like to express my gratitude with highly appreciation and dedication to my family for their concerns and encouragements.

v

TABLE OF CONTENT

Abstrak i

Abstract ii

Dedication iii

Acknowledgement iv

Table of Content v

List of Tables viii

List of Figures ix

List Abbreviations, Symbols and Nomenclature x

CHAPTER 1: INTRODUCTION 1

1.0 Introduction 1

1.1Background 1

1.2Problem Statement 2

1.3 Objectives 3

1.4 Scope of Study 3

CHAPTER 2: LITERATURE REVIEW 4

2.0 Introduction 4

2.1 Overall Equipment Efficiency (OEE) 4

2.1.1 General Causes of Low OEE Value 7

2.1.2 Application of OEE 8

2.1.3 Implementation Steps of OEE in Previous Study 9

2.1.4 Advantages 11

2.1.5 Limitations 12

2.1.6 Difficulty of Implementation 13

2.2 Value Stream Mapping (VSM) 14

2.2.1 Purpose of Value Stream Mapping 14

2.3 Cause and Effect Diagram 15

2.3.1 Purpose of Cause and Effect Diagram 16 2.3.2 Implementation Steps of Cause and Effect Diagram 17

vi

2.4.1 Purpose of FMEA 18

2.4.2 Implementation Steps of FMEA 19

2.4.3 Prerequisites of FMEA 20

2.5 Single Minute Exchange Dies (SMED) 21

2.5.1 Considerations of SMED Implementation 22

2.5.2 Implementation steps of SMED 23

2.6 Problem Solving Capability 25

2.7 Summary of Literature Review 27

CHAPTER 3: METHODOLOGY 30

3.0 Introduction 30

3.1 Flow Chart 30

3.1.1 PSM 1 Project Flow 32

3.1.1.1 Conceptualization 33

3.1.1.2 Collection on related information 33

3.1.1.3 Methodology design 34

3.1.2 PSM 2 Project Flow 34

3.1.2.1 Observation 35

3.1.2.2 Data Collection 35

3.1.2.3 Data Analysis 35

3.1.2.4 Summary of implementation stage 36

3.2.3 Result Evaluation 36

CHAPTER 4: RESULT AND DISCUSSION 37

4.0 Introduction 37

4.1 Observation 38

4.2 Data Collection 41

4.2.1 Overall Equipment Efficiency (OEE) 41

4.2.2 OEE Calculation Parameter 42

4.2.2.1 Availability Ratio 42

4.2.2.2 Performance Ratio 43

4.2.2.3 Quality Ratio 44

vii

4.3.1 Value Stream Mapping (VSM) 45

4.3.2 Cause and Effect Diagram 51

4.3.3 Failure Mode Effect Analysis (FMEA) 53

4.4 Improvement Action 58

4.4.1 SMED Implementation Steps 59

4.4.2 Expected Outcome 63

4.5 Framework Development 65

CHAPTER 5: CONCLUSION 67

5.0 Conclusion 67

REFERENCES 69

APPENDICES

Appendix A Gantt Chart for PSM 1 semester 1

Appendix B Gantt Chart for Planning PSM 2 semester 2 Appendix C Sample Calculation of OEE

Appendix D Comparison of Company OEE and Validated OEE Appendix E Time Losses in Machine Utilization

Appendix F Cause and Effect Analysis on Excessive Long Setup Appendix G Cause and Effect Analysis on Machine Breakdown Appendix H Failure Mode Effect Analysis (FMEA)

Appendix I Pareto Chart of RPN

Appendix J Actual versus Plan Cure Time

Appendix K Time Spent in Loading and Unloading Process Appendix M Future State Value Stream Mapping

viii

LIST OF TABLES

2.0 Defining Six Big Losses 6

2.1 Phases of SMED Implementation 24

2.2 Summary of Literature Review 27

4.0 Severity Rating Table 54

4.1 Occurrence Rating Table 55

4.2 Detection Rating Table 56

4.3 Cumulative Percentages of RPN 57

4.4 Current Loading Process Time 59

4.5 Internal and External Activities 61

4.6 VA and NVA in Autoclave Curing 63

ix

LIST OF FIGURES

2.0 OEE Computation with Ten Big Losses 10

2.1 The Losses Ignored by OEE 12

2.2 Cause and Effect Related to Six Big Losses 16

2.3 FMEA Implementation Steps 19

2.4 OEE before Set-Up Reduction 21

2.5 OEE after Set-Up Reduction 22

2.6 SMED Conceptual Stages 24

3.0 The Overall Flowchart of the Project 31

3.1 Project Flow for PSM 1 32

3.2 Project Flow for PSM 2 34

4.0 Flowchart of Curing Process 38

4.1 Sub-Processes of Loading and Unloading Process 40

4.2 Current OEE Data 41

4.3 Availabilty Ratio for each month 43

4.4 Performance Ratio for each month 44

4.5 Value Stream Mapping of Curing Process 47 4.6 Value Stream Mapping of Loading Sub-processes 49 4.7 Value Stream Mapping of Unloading Sub-processes 50 4.8 Causes and Effect Analysis on Lower OEE Value 51

4.9 Pareto chart of RPN 58

4.10 Framework of Developing Integrating Quality Tool 66 on Improving OEE

x

LIST OF ABBREVIATION

FMEA - Failure Mode Effect Analysis

FRACAS - Failure Analysis and Corrective Action System NST - Non-Scheduled Time

NVA - Non-Value Added

OEE - Overall Equipment Efficiency PM - Performance Measurement R&D - Research and Development RPN - Risk Priority Number

SEMI - Semiconductor Equipment and Material International SMED - Single Minute Exchange Die

SOW - Statement of Work

SRD - System Requirement Document TPM - Total Productive Maintenance WIP - Work In Process

VA - Value Added

1

1.0 INTRODUCTION

This chapter is the overall starting concept of the study conducted in the company which consists of the company background, problem statement that highlighted and emphasized the purpose to carry out the study which necessitates alternatives remedies, objectives that the research to be conducted and scope of study. The expected finding of this project will also be discussed in the end of this chapter.

1.1BACKGROUND

ABC Company which situated in Malacca is selected to be studied in this project. A subsidiary of ABC Company is responsible in manufacturing the composites aero structures for its strategic partner and customers from around the world.

In fulfilling the requirements and quality standard put forward by its customers, it is committed to adopt the Lean Enterprise Systems as its business strategy and work culture. The main objectives of adopting the Lean Enterprise Systems are to achieve the profitable and sustainable growth, improving the work force empowerment, to develop and utilise all company resources, implementing structured and goal orientated change management programme and to align the company’s strategies for global challenges through KPI and Industrial benchmarking.

INTRODUCTION

2 This project is conducted by implementing the quality tools to improve the Overall Equipment Efficiency (OEE) in the autoclave study. Overall Equipment Effectiveness (OEE) is a key measurement of efficiency in manufacturing processes (at machine, manufacturing cell or assembly line levels). This research is focusing on the improvement of autoclave utilization through implementation of several quality tools and OEE as the core measurement of the improvement. Few quality tools to be implemented as the supportive tools here include Failure Mode Effect Analysis (FMEA), Cause and Effect Diagram and Single Minutes Exchange of Die (SMED).

1.2PROBLEM STATEMENT

Overall equipment effectiveness (OEE) is seen to be the fundamental way of measuring performance efficiency. It is the essential measure of total productive maintenance (TPM) and lean maintenance. The concept of OEE is being used increasingly in industry because it monitors the actual performance of a machine relative to its performance capabilities under optimal manufacturing conditions. OEE at its simplest form is the cumulative impact of three factors availability, performance and quality. In the measurement of the three factors, the key reasons that most operations do not achieved high OEE percentages are generally caused by the six big losses which are breakdowns, setup and adjustments, small stops, reduced speed, start-up rejects and the production rejects. These losses created during the operation would somehow reduce the efficiency of the machine and material usage, quality of products and the time utilization for the overall process. The performance of OEE would be affected due to the changing of parameter during the autoclave process. In this project, the Autoclave is chosen as the subject of study among the processes of the production because it is the bottleneck that contributes to delay of the production and shipment. The problems exist in the Autoclave include the excessive long setup time, unexpected breakdown or delay and sometimes the cancelation of curing process just before the scheduled time for the particular curing. Some of these problems happen in relatively short time and are usually neglected. This is somehow contributing to big loss when they are accumulated and there is a necessity to quantify the total loss in effectiveness of utilization for the autoclave.

3

1.3OBJECTIVES

The objectives of this study are:

(a) To identify the area of improvement in autoclave curing process.

(b) To quantify the loss accumulated and improvement of the autoclave section, before and after quality tools implementation.

(c) To develop a macro framework as a Standard Operating Procedure in any field that involved in OEE calculation.

1.4SCOPE OF STUDY

This project is mainly implementing the quality tools as well as lean tools along with OEE approach for the improvement of curing process in an aerospace company. Throughout the study, time utilization can be reduced in term of the six big losses. OEE will be calculated via real time data which is acquired from the computerized recording system. Performance measurement via OEE is one of the scopes in this project before and after the implementation of quality tools. Implementation of quality tools as stated above, on the other hand, is responsible in reducing and eliminating six losses identified in the OEE measurement. In short, problem solving technique using quality and lean tools is introduced to optimize the root causes as well as the six big losses that will affect the performance of OEE. However, the improvement of the process from the term of finance is not covered in this project.

4

2.0 INTRODUCTION

This chapter consists of the general information and the historical data which obtained through several resources from the research of journals, articles, books or any internet server. General knowledge of the quality tools that developed in this study is included to serve as a guideline in designing and planning the project. Furthermore, the information that highlighted in this chapter is used to support the result and discussion which is developed in the following chapter. The content of this chapter includes the information of OEE as the core quality tool in this project, and other related supportive tools to facilitate the implementation of OEE measurement.

2.1 OVERALL EQUIPMENT EFFICIENCY (OEE)

Overall equipment effectiveness (OEE) is the key measure of both total productive maintenance (TPM) and lean maintenance. The concept of OEE, introduced by Nakajima (1988), is being used increasingly in industry. It looks at the wider manufacturing aspects, not only the equipment availability and performance, but also the efficiency losses that result from rework and yield losses.

According to Tajiri and Gotoh (1992) the relationship between OEE and losses depends on equipment availability, their performance rates and the quality of the product. OEE monitors the actual performance of a machine relative to its performance capabilities under optimal manufacturing conditions. While OEE

LITERATURE REVIEW

5 reveals equipment’s hidden losses after loading time, a new method in the steel industry based on market factors can monitor all losses within duration of satisfying the needs of internal and external market.

Tajiri and Gotoh (1992) classified major losses into six groups. Breakdown losses, setup and adjustment losses are downtime losses used to determine a true value for the availability of a machine. The third and fourth losses including minor stoppage and reduced speed losses are known as speed losses. They are used as a measure of performance rate of a given machine. Rework and yield losses are defined as quality losses that determine the quality rate for the equipment.

According to Ericsson (1997), OEE attempts to identify production losses and other indirect and “hidden” costs, which are those that contribute with a large proportion of the total cost of production. These losses are formulated as a function of a number of mutually exclusive components (Huang et al., 2003), namely: availability (A), performance (P) and quality (Q). In essence, OEE is the result achieved by multiplying these three factors altogether.

The availability factor measures the total time that the system is not operating because of breakdowns, set-up, adjustment, and other stoppages (Jonsson and Lesshammar, 1999). It is traditionally calculated using the Nakajima’s (1988) formula presented below. In this formula, loading time refers to the equipment’s total length of operation after any deduction of planned activities that may have disrupted production, for example: schedule and planned maintenance, official production breaks, process improvement initiatives or equipment tests, maintenance performed by the machine operator (e.g. equipment cleaning), operator training, etc.:

A = (Loading time – downtime)/ Loading time

The second OEE element, performance rate, measures the ratio of the actual operating speed of the equipment (e.g. the ideal speed minus speed losses, minor stoppages and idling) to its ideal speed (Jonsson and Lesshammar, 1999). It can be calculated in a number of different ways. However, Nakajima (1988) measures a fixed amount of output, and in his definition of “performance”, it indicates the actual

6 deviation in production in time from ideal cycle time. Performance (P) is calculated using the following Nakajima’s (1988) equation:

P = (Ideal cycle time – output)/ Operating time

The third element of OEE is quality (Q). It indicates the proportion of defective production to the total production volume. An important characteristic that should be noted is that the quality concept, as defined by Nakajima (1988), only involves defects that occur in that designated stage of production, usually on a specific machine or production line and not elsewhere. Quality (Q) is calculated using the Nakajima’s (1988) equation presented below:

Q = (Input - volume of quality defects)/ Input

Table 2.0: Defining Six Big Losses (Vorne, 2008). OEE Loss

Category

Six Big Loss

Category

Event Examples Comment

Down Time Loss

Breakdowns  Tooling Failures

 Unplanned Maintenance

 General Breakdowns

 Equipment Failure

There is flexibility on where to set the threshold between a Breakdown (Down Time Loss) and a Small Stop (Speed Loss). Setup and

adjustments

 Setup/Changeover

 Material Shortages

 Operator Shortages

 Major Adjustments

 Warm-Up Time

This loss is often addressed through setup time reduction programs.

Speed Loss Small Stops _ Obstructed Product Flow

 Component Jams

Typically only includes stops that are under five minutes and that

7

 Misfeeds

 Sensor Blocked

 Delivery Blocked

 Cleaning/Checking

do not require maintenance personnel.

Reduced Speed

 Rough Running

 Under Nameplate Capacity

 Under Design

Capacity

 Equipment Wear

 Operator Inefficiency

Anything that keeps the process from running at its theoretical maximum speed (Ideal Run Rate or Nameplate Capacity) Quality Loss Start-up Rejects  Scrap  Rework

 In-Process Damage

 In-Process Expiration

 Incorrect Assembly

Rejects during warm-up, startup or either early production. May be due to improper setup, warm-up period, etc.

Production Rejects

 Scrap

 Rework

 In-Process Damage

 In-Process Expiration

 Incorrect Assembly

Rejects during steady-state production

2.1.1 General Causes of Low OEE Value

Historical data of Overall Equipment Effectiveness (OEE) value was very low compared to the general manufacturing scenario will due to which the machines were not utilized effectively and hence production rate and volume was affected (Harsha, et al, 2009).

8 Low OEE might be contributed by:

(a) Operator Awareness: For any machine to run effectively the operator should be well experienced and capable enough to handle the machine. The operators were not well trained to operate the machine and any minor abnormalities found could not be rectified. Though the machine was capable of producing quality goods, due to manual error the quality was lost.

(b) Lack of standardized procedure: Operating procedure was not given. No shadow board near machines about the operating procedure of machines and lack of visual management made it difficult for any operator to produce the parts consistently. SOP (Standard Operating Procedure) was not in place. In addition, Process Flow chart was not provided.

(c) Maintenance Frequency: Periodic maintenance was not in place and only when the machine stopped, the maintenance was carried out. Preventive maintenance was not planned for any of the machines in the cell

(d) Checklist Points: The critical points of the machine, which required frequent attention was not identified. This led to frequent breakdown of the machine. Lack of checkpoint identification may be one of the reasons for low OEE.

2.1.2 Application of OEE

Dal et al. (2000) point out the OEE measure can provide topical information for daily decision making by utilizing largely existing performance data, such as preventive maintenance, material utilization, absenteeism, accidents, labor recovery, conformance to schedule, set-up and changeover data. Hansen (2001) describes OEE as a powerful production and maintenance tool for increasing profit. Bamber et al. (2003) discuss OEE as a total measure of performance and concluded that cross-functional team working is essential for its success.

Although OEE was originally designed to monitor and control performance, Dal (1999) suggests that the role of OEE goes far beyond the task of just monitoring and controlling. This is because OEE takes into account process improvement initiatives, prevents the sub-optimization of individual machines or production lines, provides a

3 1.3OBJECTIVES

The objectives of this study are:

(a) To identify the area of improvement in autoclave curing process.

(b) To quantify the loss accumulated and improvement of the autoclave section, before and after quality tools implementation.

(c) To develop a macro framework as a Standard Operating Procedure in any field that involved in OEE calculation.

1.4SCOPE OF STUDY

This project is mainly implementing the quality tools as well as lean tools along with OEE approach for the improvement of curing process in an aerospace company. Throughout the study, time utilization can be reduced in term of the six big losses. OEE will be calculated via real time data which is acquired from the computerized recording system. Performance measurement via OEE is one of the scopes in this project before and after the implementation of quality tools. Implementation of quality tools as stated above, on the other hand, is responsible in reducing and eliminating six losses identified in the OEE measurement. In short, problem solving technique using quality and lean tools is introduced to optimize the root causes as well as the six big losses that will affect the performance of OEE. However, the improvement of the process from the term of finance is not covered in this project.

4 2.0 INTRODUCTION

This chapter consists of the general information and the historical data which obtained through several resources from the research of journals, articles, books or any internet server. General knowledge of the quality tools that developed in this study is included to serve as a guideline in designing and planning the project. Furthermore, the information that highlighted in this chapter is used to support the result and discussion which is developed in the following chapter. The content of this chapter includes the information of OEE as the core quality tool in this project, and other related supportive tools to facilitate the implementation of OEE measurement.

2.1 OVERALL EQUIPMENT EFFICIENCY (OEE)

Overall equipment effectiveness (OEE) is the key measure of both total productive maintenance (TPM) and lean maintenance. The concept of OEE, introduced by Nakajima (1988), is being used increasingly in industry. It looks at the wider manufacturing aspects, not only the equipment availability and performance, but also the efficiency losses that result from rework and yield losses.

According to Tajiri and Gotoh (1992) the relationship between OEE and losses depends on equipment availability, their performance rates and the quality of the product. OEE monitors the actual performance of a machine relative to its performance capabilities under optimal manufacturing conditions. While OEE

LITERATURE REVIEW

5 reveals equipment’s hidden losses after loading time, a new method in the steel industry based on market factors can monitor all losses within duration of satisfying the needs of internal and external market.

Tajiri and Gotoh (1992) classified major losses into six groups. Breakdown losses, setup and adjustment losses are downtime losses used to determine a true value for the availability of a machine. The third and fourth losses including minor stoppage and reduced speed losses are known as speed losses. They are used as a measure of performance rate of a given machine. Rework and yield losses are defined as quality losses that determine the quality rate for the equipment.

According to Ericsson (1997), OEE attempts to identify production losses and other indirect and “hidden” costs, which are those that contribute with a large proportion of the total cost of production. These losses are formulated as a function of a number of mutually exclusive components (Huang et al., 2003), namely: availability (A), performance (P) and quality (Q). In essence, OEE is the result achieved by multiplying these three factors altogether.

The availability factor measures the total time that the system is not operating because of breakdowns, set-up, adjustment, and other stoppages (Jonsson and Lesshammar, 1999). It is traditionally calculated using the Nakajima’s (1988) formula presented below. In this formula, loading time refers to the equipment’s total length of operation after any deduction of planned activities that may have disrupted production, for example: schedule and planned maintenance, official production breaks, process improvement initiatives or equipment tests, maintenance performed by the machine operator (e.g. equipment cleaning), operator training, etc.:

A = (Loading time – downtime)/ Loading time

The second OEE element, performance rate, measures the ratio of the actual operating speed of the equipment (e.g. the ideal speed minus speed losses, minor stoppages and idling) to its ideal speed (Jonsson and Lesshammar, 1999). It can be calculated in a number of different ways. However, Nakajima (1988) measures a fixed amount of output, and in his definition of “performance”, it indicates the actual

6 deviation in production in time from ideal cycle time. Performance (P) is calculated using the following Nakajima’s (1988) equation:

P = (Ideal cycle time – output)/ Operating time

The third element of OEE is quality (Q). It indicates the proportion of defective production to the total production volume. An important characteristic that should be noted is that the quality concept, as defined by Nakajima (1988), only involves defects that occur in that designated stage of production, usually on a specific machine or production line and not elsewhere. Quality (Q) is calculated using the Nakajima’s (1988) equation presented below:

Q = (Input - volume of quality defects)/ Input

Table 2.0: Defining Six Big Losses (Vorne, 2008).

OEE Loss Category

Six Big

Loss Category

Event Examples Comment

Down Time Loss

Breakdowns  Tooling Failures  Unplanned

Maintenance

 General Breakdowns  Equipment Failure

There is flexibility on where to set the threshold between a Breakdown (Down Time Loss) and a Small Stop (Speed Loss). Setup and

adjustments

 Setup/Changeover  Material Shortages  Operator Shortages  Major Adjustments  Warm-Up Time

This loss is often addressed through setup time reduction programs.

Speed Loss Small Stops _ Obstructed Product Flow

 Component Jams

Typically only includes stops that are under five minutes and that

7  Misfeeds

 Sensor Blocked  Delivery Blocked  Cleaning/Checking

do not require maintenance personnel.

Reduced Speed

 Rough Running  Under Nameplate

Capacity

 Under Design

Capacity

 Equipment Wear  Operator Inefficiency

Anything that keeps the process from running at its theoretical maximum speed (Ideal Run Rate or Nameplate Capacity) Quality Loss Start-up Rejects  Scrap  Rework

 In-Process Damage  In-Process Expiration  Incorrect Assembly

Rejects during warm-up, startup or either early production. May be due to improper setup, warm-up period, etc.

Production Rejects

 Scrap  Rework

 In-Process Damage  In-Process Expiration  Incorrect Assembly

Rejects during steady-state production

2.1.1 General Causes of Low OEE Value

Historical data of Overall Equipment Effectiveness (OEE) value was very low compared to the general manufacturing scenario will due to which the machines were not utilized effectively and hence production rate and volume was affected (Harsha, et al, 2009).

8 Low OEE might be contributed by:

(a) Operator Awareness: For any machine to run effectively the operator should be well experienced and capable enough to handle the machine. The operators were not well trained to operate the machine and any minor abnormalities found could not be rectified. Though the machine was capable of producing quality goods, due to manual error the quality was lost.

(b) Lack of standardized procedure: Operating procedure was not given. No shadow board near machines about the operating procedure of machines and lack of visual management made it difficult for any operator to produce the parts consistently. SOP (Standard Operating Procedure) was not in place. In addition, Process Flow chart was not provided.

(c) Maintenance Frequency: Periodic maintenance was not in place and only when the machine stopped, the maintenance was carried out. Preventive maintenance was not planned for any of the machines in the cell

(d) Checklist Points: The critical points of the machine, which required frequent attention was not identified. This led to frequent breakdown of the machine. Lack of checkpoint identification may be one of the reasons for low OEE.

2.1.2 Application of OEE

Dal et al. (2000) point out the OEE measure can provide topical information for daily decision making by utilizing largely existing performance data, such as preventive maintenance, material utilization, absenteeism, accidents, labor recovery, conformance to schedule, set-up and changeover data. Hansen (2001) describes OEE as a powerful production and maintenance tool for increasing profit. Bamber et al. (2003) discuss OEE as a total measure of performance and concluded that cross-functional team working is essential for its success.

Although OEE was originally designed to monitor and control performance, Dal (1999) suggests that the role of OEE goes far beyond the task of just monitoring and controlling. This is because OEE takes into account process improvement initiatives, prevents the sub-optimization of individual machines or production lines, provides a