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Springer Series in Reliability Engineering


Series Editor
Professor Hoang Pham
Department of Industrial and Systems Engineering
Rutgers, The State University of New Jersey
96 Frelinghuysen Road
Piscataway, NJ 08854-8018
USA

Other titles in this series
The Universal Generating Function
in Reliability Analysis and Optimization
Gregory Levitin

Human Reliability and Error in Transportation Systems
B.S. Dhillon

Warranty Management and Product
Manufacture
D.N.P. Murthy and Wallace R. Blischke

Complex System Maintenance Handbook
D.N.P. Murthy and Khairy A.H. Kobbacy

Maintenance Theory of Reliability
Toshio Nakagawa

Recent Advances in Reliability and Quality
in Design

Hoang Pham

System Software Reliability
Hoang Pham
Reliability and Optimal Maintenance
Hongzhou Wang and Hoang Pham
Applied Reliability and Quality
B.S. Dhillon
Shock and Damage Models in Reliability
Theory
Toshio Nakagawa
Risk Management
Terje Aven and Jan Erik Vinnem
Satisfying Safety Goals by Probabilistic
Risk Assessment
Hiromitsu Kumamoto

Product Reliability
D.N.P. Murthy, Marvin Rausand and Trond
Østerås
Mining Equipment Reliability,
Maintainability, and Safety
B.S. Dhillon
Advanced Reliability Models and
Maintenance Policies
Toshio Nakagawa
Justifying the Dependability of Computerbased Systems
Pierre-Jacques Courtois

Offshore Risk Assessment (2nd Edition)

Jan Erik Vinnem

Reliability and Risk Issues in Large Scale
Safety-critical Digital Control Systems
Poong Hyun Seong

The Maintenance Management Framework
Adolfo Crespo Márquez

Risks in Technological Systems
Torbjörn Thedéen and Göran Grimvall


Riccardo Manzini · Alberto Regattieri
Hoang Pham · Emilio Ferrari

Maintenance for
Industrial Systems
With 504 figures and 174 tables

123


Prof. Riccardo Manzini
Università di Bologna
Dipartimento Ingegneria
delle Costruzioni Meccaniche,
Nucleari, Aeronautiche
e di Metallurgia (DIEM)
Viale Risorgimento, 2

40136 Bologna
Italy


Prof. Hoang Pham
Rutgers University
Department of Industrial
and Systems Engineering
96 Frelinghuysen Road
Piscataway NJ 08854-8018
USA


Prof. Alberto Regattieri
Università di Bologna
Dipartimento Ingegneria
delle Costruzioni Meccaniche,
Nucleari, Aeronautiche
e di Metallurgia (DIEM)
Viale Risorgimento, 2
40136 Bologna
Italy


Prof. Emilio Ferrari
Università di Bologna
Dipartimento Ingegneria
delle Costruzioni Meccaniche,
Nucleari, Aeronautiche
e di Metallurgia (DIEM)

Viale Risorgimento, 2
40136 Bologna
Italy


ISSN 1614-7839
ISBN 978-1-84882-574-1
e-ISBN 978-1-84882-575-8
DOI 10.1007/978-1-84882-575-8
Springer Dordrecht Heidelberg London New York
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to Sara and Marta


Preface

Billions of dollars are currently spent producing high-technology products and services in a variety of production systems operating in different manufacturing and
service sectors (e. g., aviation, automotive industry, software development, banks
and financial companies, health care). Most of these products are very complex and
sophisticated owing to the number of functions and components. As a result, the
production process that realizes these products can be very complicated.
A significant example is the largest passenger airliner in the world, the Airbus
A380, also known as the “Superjumbo,” with an operating range of approximately
15,200 km, sufficient to fly directly from New York City to Hong Kong. The failure
and repair behaviors of the generic part of this system can be directly or indirectly
associated with thousands of different safety implications and/or quality expectations and performance measurements, which simultaneously deal with passengers,
buildings, the environment, safety, and communities of people.
What is the role of maintenance in the design and management of such a product, process, or system? Proper maintenance definitely helps to minimize problems,
reduce risk, increase productivity, improve quality, and minimize production costs.

This is true both for industrial and for infrastructure assets, from private to government industries producing and supplying products as well as services.
We do not need to think about complex production systems, e. g., nuclear power
plants, aerospace applications, aircraft, and hospital monitoring control systems, to
understand the strategic role of maintenance for the continuous functioning of production systems and equipment.
Concepts such as safety, risk, and reliability are universally widespread and
maybe abused, because daily we make our choices on the basis of them, willingly
or not. That is why we prefer a safer or a more reliable car, or why we travel with
a safer airline instead of saving money with an ill-famed company. The acquisition
of a safer, or high-quality, article is a great comfort to us even if we pay more.
The strategic role of maintenance grows in importance as society grows in complexity, global competition increases, and technological research finds new applications. Consequently the necessity for maintenance actions will continue to increase
in the future as will the necessity to further reduce production costs, i. e., increase
efficiency, and improve the safety and quality of products and processes. In particular, during the last few decades the so-called reliability and maintenance engineering

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viii

discipline has grown considerably in both universities and industry as well as in government.
The activities of planning, design, management, control, and optimization of
maintenance issues are very critical topics of reliability and maintenance engineering. These are the focus of this book, whose aim is to introduce practitioners and
researchers to the main problems and issues in reliability engineering and maintenance planning and optimization.
Several supporting decision models and methods are introduced and applied: the
book is full of numerical examples, case studies, figures, and tables in order to
quickly introduce the reader to very complicated engineering problems. Basic theory
and fundamentals are continuously combined with practical experience and exercises
useful to practitioners but also to students of undergraduate and graduate schools of
engineering, science, and management.
The most important keywords used in this book are as follows: product, process,
production system, productivity, reliability, availability, maintainability, risk, safety,

failure modes and criticality analyses (failure modes and effects analysis and failure
mode, effects, and criticality analysis), prediction and evaluation, assessment, preventive maintenance, inspection maintenance, optimization, cost minimization, spare
parts fulfillment and management, computerized maintenance management system,
total productive maintenance, overall equipment effectiveness, fault tree analysis,
Markov chains, Monte Carlo simulation, numerical example, and case study.
The book consists of 12 chapters organized as introduced briefly below.
Chapter 1 identifies and illustrates the most critical issues concerning the planning activity, the design, the management, and the control of modern production
systems, both producing goods (manufacturing systems in industrial sectors) and/or
supplying services (e. g., hospital, university, bank). This chapter identifies the role
of maintenance in a production system and the capability of guaranteeing a high level
of safety, quality, and productivity in a proper way.
Chapter 2 introduces quality assessment, presents statistical quality control models and methods, and finally Six Sigma theory and applications. A brief illustration
and discussion of European standards and specifications for quality assessment is
also presented.
Chapter 3 introduces the reader to the actual methodology for the implementation
of a risk evaluation capable of reducing risk exposure and guaranteeing the desired
level of safety.
Chapter 4 examines the fundamental definitions concerning maintenance, and
discusses the maintenance question in product manufacturing companies and service suppliers. The most important maintenance engineering frameworks, e. g.,
reliability-centered maintenance and total productive maintenance, are presented.
Chapter 5 introduces the reader to the definition, measurement, management, and
control of the main reliability parameters that form the basis for modeling and evaluating activities in complex production systems. In particular, the basic maintenance
terminology and nomenclature related to a generic item as a part, component, device,
subsystem, functional unit, piece of equipment, or system that can be considered individually are introduced.
Chapter 6 deals with reliability evaluation and prediction. It also discusses the
elementary reliability configurations of a system in order to introduce the reader to
the basic tools used to evaluate complex production systems.

Preface

Preface

ix

Chapter 7 discusses about the strategic role of the maintenance information system and computerized maintenance management systems in reliability engineering.
Failure rate prediction models are also illustrated and applied.
Chapter 8 introduces models and methods supporting the production system designer and the safety and/or maintenance manager to identify how subsystems and
components could fail and what the corresponding effects on the whole system are,
and to quantify the reliability parameters for complex systems. In particular models,
methods, and tools (failure modes and effects analysis and failure mode, effects, and
criticality analysis, fault tree analysis, Markov chains, Monte Carlo dynamic simulation) for the evaluation of reliability in complex production systems are illustrated
and applied to numerical examples and case studies.
Chapter 9 presents basic and effective models and methods to plan and conduct
maintenance actions in accordance with corrective, preventive, and inspection strategies and rules. Several numerical examples and applications are illustrated.
Chapter 10 discusses advanced models and methods, including the block replacements, age replacements, and inspection policies for maintenance management.
Chapter 11 presents and applies models and tools for supporting the activities of
fulfillment and management of spare parts.
Chapter 12 presents two significant case studies on reliability and maintenance
engineering. In particular, several models and methods introduced and exemplified
in previous chapters are applied and compared.
We would like to thank our colleagues and students, particularly those who deal
with reliability engineering and maintenance every day, and all professionals from
industry and service companies who supported our research and activities, Springer
for its professional help and cooperation, and finally our families, who encouraged
us to write this book.
Bologna (Italy) and Piscataway (NJ, USA)
Autumn 2008

Riccardo Manzini

Alberto Regattieri
Hoang Pham
Emilio Ferrari


Contents

1

A New Framework for Productivity in Production Systems . . . . . . . .

1

1.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

1.2

A Multiobjective Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

1.2.1

Product Variety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

1.2.2

Product Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

1.3

Production System Design Framework . . . . . . . . . . . . . . . . . . . . . .

4

1.4

Models, Methods, and Technologies for Industrial Management

5

1.4.1

The Product and Its Main Features . . . . . . . . . . . . . . . . . . . . . . . . .

5

1.4.2

Reduction of Unremunerated Complexity:
The Case of Southwest Airlines . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

1.4.3

The Production Process and Its Main Features . . . . . . . . . . . . . . .

7

1.4.4

The Choice of Production Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

1.5

Design, Management, and Control of Production Systems . . . . .

10

1.5.1

Demand Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

1.5.2

Product Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

1.5.3

Process and System Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

1.5.4

Role of Maintenance in the Design of a Production System . . . .

11

1.5.5

Material Handling Device Design . . . . . . . . . . . . . . . . . . . . . . . . . .

11

1.5.6

System Validation and Profit Evaluation . . . . . . . . . . . . . . . . . . . .

11

1.5.7

Project Planning and Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . .

11

1.5.8

New Versus Existing Production Systems . . . . . . . . . . . . . . . . . . .

11

1.6

Production System Management Processes for Productivity . . . .

13

1.6.1

Inventory and Purchasing Management . . . . . . . . . . . . . . . . . . . . .

14

1.6.2

Production Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14

1.6.3

Distribution Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14

1.7

Research into Productivity and Maintenance Systems . . . . . . . . .

14
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xii

2

3

Contents

Quality Management Systems and Statistical Quality Control . . . . . .
2.1
Introduction to Quality Management Systems . . . . . . . . . . . . . . . .
2.2
International Standards and Specifications . . . . . . . . . . . . . . . . . . .
2.3
ISO Standards for Quality Management and Assessment . . . . . . .
2.3.1 Quality Audit, Conformity, and Certification . . . . . . . . . . . . . . . . .
2.3.2 Environmental Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4
Introduction to Statistical Methods for Quality Control . . . . . . . .
2.4.1 The Central Limit Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.4.2 Terms and Definition in Statistical Quality Control . . . . . . . . . . .
2.5
Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.6
Control Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.7
Control Charts for Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.7.1 The R-Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.7.2 Numerical Example, R-Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.7.3 The x-Chart
N
............................................
2.7.4 Numerical Example, x-Chart
N
..............................
2.7.5 The s-Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.7.6 Numerical Example, s-Chart and x-Chart
N
...................
2.8
Control Charts for Attribute Data . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8.1 The p-Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8.2 Numerical Example, p-Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8.3 The np-Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8.4 Numerical Example, np-Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8.5 The c-Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8.6 Numerical Example, c-Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8.7 The u-Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8.8 Numerical Example, u-Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.9

Capability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.9.1 Numerical Example, Capability Analysis
and Normal Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.9.2 Numerical Examples, Capability Analysis
and Nonnormal Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.10 Six Sigma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.10.1 Numerical Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.10.2 Six Sigma in the Service Sector. Thermal Water Treatments
for Health and Fitness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

17
17
19
19
19
21
23
23
24
25
25
26
26
29
29
30
30
33
33
35

36
37
37
37
39
40
40
40

Safety and Risk Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1
Introduction to Safety Management . . . . . . . . . . . . . . . . . . . . . . . .
3.2
Terms and Definitions. Hazard Versus Risk . . . . . . . . . . . . . . . . . .
3.3
Risk Assessment and Risk Reduction . . . . . . . . . . . . . . . . . . . . . . .
3.4
Classification of Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5
Protective and Preventive Actions . . . . . . . . . . . . . . . . . . . . . . . . . .
3.6
Risk Assessment, Risk Reduction, and Maintenance . . . . . . . . . .
3.7
Standards and Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53
53
54
57
58

60
63
63

42
46
48
51
51


Contents

xiii

4

Introduction to Maintenance in Production Systems . . . . . . . . . . . . . .
4.1
Maintenance and Maintenance Management . . . . . . . . . . . . . . . . .
4.2
The Production Process and the Maintenance Process . . . . . . . . .
4.3
Maintenance and Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4
Maintenance Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5
Maintenance Engineering Frameworks . . . . . . . . . . . . . . . . . . . . . .
4.6
Reliability-Centered Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . .

4.7
Total Productive Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.7.1 Introduction to TPM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.7.2 The Concept of TPM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.7.3 TPM Operating Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.7.4 From Tradition to TPM: A Difficult Transition . . . . . . . . . . . . . . .
4.8
Maintenance Status Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.9
Maintenance Outsourcing and Contracts . . . . . . . . . . . . . . . . . . . .

65
65
66
69
70
70
72
73
73
74
75
76
80
83

5

Basic Statistics and Introduction to Reliability . . . . . . . . . . . . . . . . . . . .
5.1

Introduction to Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2
Components and Systems in Reliability . . . . . . . . . . . . . . . . . . . . .
5.3
Basic Statistics in Reliability Engineering . . . . . . . . . . . . . . . . . . .
5.4
Time to Failure and Time to Repair . . . . . . . . . . . . . . . . . . . . . . . .
5.5
Probability Distribution Function . . . . . . . . . . . . . . . . . . . . . . . . . .
5.6
Repairable and Nonrepairable Systems . . . . . . . . . . . . . . . . . . . . .
5.7
The Reliability Function – R(t) . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.8
Hazard Rate Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.8.1 Hazard Rate Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.8.2 Mean Time to Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.9
Stochastic Repair Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.10 Parametric Probability Density Functions . . . . . . . . . . . . . . . . . . .
5.10.1 Constant Failure Rate Model: The Exponential Distribution . . . .
5.10.2 Exponential Distribution. Numerical example . . . . . . . . . . . . . . . .
5.10.3 The Normal and Lognormal Distributions . . . . . . . . . . . . . . . . . . .
5.10.4 Normal and Lognormal Distributions. Numerical example . . . . .
5.10.5 The Weibull Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.10.6 Weibull Distribution. Numerical Example . . . . . . . . . . . . . . . . . . .
5.11 Repairable Components/Systems:
The Renewal Process and Availability A(t) . . . . . . . . . . . . . . . . . .
5.12 Applications and Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.12.1 Application 1 – Nonrepairable Components . . . . . . . . . . . . . . . . .

5.12.2 Application 2 – Repairable System . . . . . . . . . . . . . . . . . . . . . . . . .

87
88
88
89
90
90
91
91
92
94
95
95
97
97
99
103
106
110
112

Reliability Evaluation and Reliability Prediction Models . . . . . . . . . . .
6.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2
Data Collection and Evaluation of Reliability Parameters . . . . . .
6.2.1 Empirical Functions Direct to Data . . . . . . . . . . . . . . . . . . . . . . . . .
6.2.2 Theoretical Distribution Research . . . . . . . . . . . . . . . . . . . . . . . . . .
6.3

Introduction to Reliability Block Diagrams . . . . . . . . . . . . . . . . . .
6.4
Serial Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.4.1 Numerical Example – Serial Configuration . . . . . . . . . . . . . . . . . .
6.5
Parallel Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

133
133
134
135
145
152
153
154
161

6

113
117
117
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xiv

Contents

6.5.1

6.6
6.7
6.8
6.8.1
6.9
6.9.1

Numerical Example – Parallel Configuration . . . . . . . . . . . . . . . . .
Combined Series–Parallel Systems . . . . . . . . . . . . . . . . . . . . . . . . .
Combined Parallel–Series Systems . . . . . . . . . . . . . . . . . . . . . . . . .
k-out-of-n Redundancy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Numerical Examples, k-out-of-n Redundancy . . . . . . . . . . . . . . . .
Simple Standby System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Numerical Example – Time-Dependent Analysis:
Standby System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.10 Production System Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.10.1 Water Supplier System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.10.2 Continuous Dryer System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

163
168
170
170
171
174

7

Maintenance Information System and Failure Rate Prediction . . . . .
7.1

The Role of a Maintenance Information System . . . . . . . . . . . . . .
7.2
Maintenance Information System Framework . . . . . . . . . . . . . . . .
7.2.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2.2 Maintenance Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2.3 Interventions and Workload Analysis . . . . . . . . . . . . . . . . . . . . . . .
7.2.4 Spare Parts and Equipment Management . . . . . . . . . . . . . . . . . . . .
7.3
Computer Maintenance Management Software . . . . . . . . . . . . . . .
7.4
CMMS Implementation: Procedure and Experimental Evidence
7.4.1 System Configuration and Integration . . . . . . . . . . . . . . . . . . . . . .
7.4.2 Training and Data Entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.4.3 Go Live . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.4.4 Postimplementation Phase and Closing . . . . . . . . . . . . . . . . . . . . .
7.4.5 Experimental Evidence Concerning CMMS Implementation . . .
7.5
Failure Rate Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.5.1 Accelerated Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.5.2 Failure Data Prediction Using a Database . . . . . . . . . . . . . . . . . . .
7.6
Remote Maintenance/Telemaintenance . . . . . . . . . . . . . . . . . . . . .
7.6.1 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

189
189
190
190
192
194

195
196
199
199
200
200
200
200
204
204
206
214
216

8

Effects Analysis and Reliability Modeling
of Complex Production Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.1
Introduction to Failure Modes Analysis
and Reliability Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2
Failure Modes and Effects Analysis . . . . . . . . . . . . . . . . . . . . . . . .
8.2.1 Product Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2.2 Failure Mode, Effects, and Causes Analysis . . . . . . . . . . . . . . . . .
8.2.3 Risk Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2.4 Corrective Action Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2.5 FMEA Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.3
Failure Mode, Effects, and Criticality Analysis . . . . . . . . . . . . . . .

8.3.1 Qualitative FMECA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.3.2 Quantitative FMECA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.3.3 Numerical Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.4
Introduction to Fault Tree Analysis . . . . . . . . . . . . . . . . . . . . . . . . .
8.5
Qualitative FTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.5.1 Fault Tree Construction Guidelines . . . . . . . . . . . . . . . . . . . . . . . . .

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183
185
187

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220
220
221
222
222
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229
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xv

8.5.2
8.5.3
8.5.4
8.6
8.6.1
8.6.2
8.6.3

Numerical Example 1. Fault Tree Construction . . . . . . . . . . . . . . .
Boolean Algebra and Application to FTA . . . . . . . . . . . . . . . . . . .
Qualitative FTA: A Numerical Example . . . . . . . . . . . . . . . . . . . . .
Quantitative FTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Quantitative FTA, Numerical Example 1 . . . . . . . . . . . . . . . . . . . .
Quantitative FTA, Numerical Example 2 . . . . . . . . . . . . . . . . . . . .
Numerical Example. Quantitative Analysis in the Presence
of a Mix of Statistical Distributions . . . . . . . . . . . . . . . . . . . . . . . .
8.7
Application 1 – FTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.7.1 Fault Tree Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.7.2 Qualitative FTA and Standards-Based Reliability Prediction . . . .
8.7.3 Quantitative FTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.8
Application 2 – FTA in a Waste to Energy System . . . . . . . . . . . .
8.8.1 Introduction to Waste Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.8.2 Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8.8.3 Emissions and Externalities: Literature Review . . . . . . . . . . . . . .
8.8.4 SNCR Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.8.5 SNCR Plant. Reliability Prediction and Evaluation Model . . . . .
8.8.6 Qualitative FTA Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.8.7 NOx Emissions: Quantitative FTA Evaluation . . . . . . . . . . . . . . . .
8.8.8 Criticality Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.8.9 Spare Parts Availability, What-If Analysis . . . . . . . . . . . . . . . . . . .
8.8.10 System Modifications for ENF Reduction and Effects Analysis .
8.9
Markov Analysis and Time-Dependent Components/Systems . . .
8.9.1 Redundant Parallel Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.9.2 Parallel System with Repairable Components . . . . . . . . . . . . . . . .
8.9.3 Standby Parallel Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.10 Common Mode Failures and Common Causes . . . . . . . . . . . . . . .
8.10.1 Unavailability of a System Subject to Common Causes . . . . . . . .
8.10.2 Numerical Example, Dependent Event . . . . . . . . . . . . . . . . . . . . . .
9

Basic Models and Methods for Maintenance of Production Systems .
9.1
Introduction to Analytical Models
for Maintenance of Production Systems . . . . . . . . . . . . . . . . . . . . .
9.1.1 Inspection Versus Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2
Maintenance Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.3
Introduction to Preventive Maintenance Models . . . . . . . . . . . . . .
9.4
Component Replacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.4.1 Time-Related Terms and Life Cycle Management . . . . . . . . . . . .

9.4.2 Numerical Example. Preventive Replacement
and Cost Minimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.5
Time-Based Preventive Replacement –
Type I Replacement Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.5.1 Numerical Example. Type I Replacement Model . . . . . . . . . . . . .
9.5.2 Numerical Example. Type I Model
and Exponential Distribution of ttf . . . . . . . . . . . . . . . . . . . . . . . . .
9.5.3 Type I Replacement Model for Weibull distribution of ttf . . . . . .
9.5.4 The Golden Section Search Method . . . . . . . . . . . . . . . . . . . . . . . .

240
241
242
244
248
252
254
263
264
266
269
277
277
278
279
280
281
283
287

292
295
300
301
302
304
306
309
310
311
313
314
315
315
318
319
319
320
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Contents

9.5.5

9.6
9.6.1
9.6.2
9.6.3
9.6.4
9.7
9.7.1
9.7.2
9.7.3
9.7.4
9.7.5
9.7.6
9.7.7
9.8
9.8.1
9.9
9.9.1
9.9.2
9.10
9.11
9.11.1
9.11.2
9.12
9.12.1
9.12.2
9.13
9.14
9.15
9.15.1
9.15.2

9.16
9.17
9.18
9.18.1
9.18.2
9.18.3
9.19

Numerical Example. Type I Model and the Golden Section
Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Time-Based Preventive Replacement Including Duration
of Replacements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Numerical Example 1: Type I Replacement Model Including
Durations Tp and Tf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Type I Model with Duration of Replacement for Weibull
Distribution of ttf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Numerical Example 2: Type I Model with Durations Tp and Tf . .
Practical Shortcut to tp Determination . . . . . . . . . . . . . . . . . . . . . .
Block Replacement Strategy – Type II . . . . . . . . . . . . . . . . . . . . . .
Renewal Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Laplace Transformation: W(t) and w(t) . . . . . . . . . . . . . . . . . . . . . .
Renewal Process and W(t) Determination, Numerical Example .
Numerical Example, Type II Model . . . . . . . . . . . . . . . . . . . . . . . .
Discrete Approach to W(t) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Numerical Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Practical Shortcut to W(t) and tp Determination . . . . . . . . . . . . . .
Maintenance Performance Measurement in Preventive
Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Numerical Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Minimum Total Downtime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Type I – Minimum Downtime . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Type II – Downtime Minimization . . . . . . . . . . . . . . . . . . . . . . . . .
Group Replacement: The Lamp Replacement Problem . . . . . . . .
Preventive Maintenance Policies for Repairable Systems . . . . . . .
Type I Policy for Repairable Systems . . . . . . . . . . . . . . . . . . . . . . .
Type II Policy for Repairable Systems . . . . . . . . . . . . . . . . . . . . . .
Replacement of Capital Equipment . . . . . . . . . . . . . . . . . . . . . . . . .
Minimization of Total Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Numerical Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Literature Discussion on Preventive Maintenance Strategies . . . .
Inspection Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Single Machine Inspection Model Based
on a Constant Value of Conditional Probability Failure . . . . . . . .
Numerical Example 1, Elementary Inspection Model . . . . . . . . . .
Numerical Example 2, Elementary Inspection Model . . . . . . . . . .
Inspection Frequency Determination and Profit
per Unit Time Maximization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Inspection Frequency Determination and Downtime
Minimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Inspection Cycle Determination and Profit
per Unit Time Maximization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Exponential Distribution of ttf . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Weibull Distribution of ttf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Numerical Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Single Machine Inspection Model Based on Total Cost
per Unit Time Minimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

328
333
333

335
335
335
339
340
341
341
343
348
349
352
353
354
355
355
357
358
359
360
370
372
372
372
372
373
375
376
377
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380

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xvii

9.20

Single Machine Inspection Model Based on Minimal Repair
and Cost Minimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.21 Inspection Model Based on Expected Availability
per Unit Time Maximization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.22 Group of Machines Inspection Model . . . . . . . . . . . . . . . . . . . . . . .
9.23 A Note on Inspection Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.24 Imperfect Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.24.1 Imperfect Preventive Maintenance p – q . . . . . . . . . . . . . . . . . . . . .
9.25 Maintenance-Free Operating Period . . . . . . . . . . . . . . . . . . . . . . . .
9.25.1 Numerical Example (Kumar et al. 1999) . . . . . . . . . . . . . . . . . . . .
9.25.2 MFOPS and Weibull Distribution of ttf . . . . . . . . . . . . . . . . . . . . .
9.26 Opportunistic Maintenance Strategy . . . . . . . . . . . . . . . . . . . . . . . .

384
385
386
387

388
388
390
391
392
393

10 Advanced Maintenance Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.2 Maintenance Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.2.1 Age Replacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.2.2 Block Replacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.3 Modeling of Nonrepairable Degraded Systems . . . . . . . . . . . . . . .
10.4 Modeling of Inspection-Maintenance Repairable Degraded
Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.4.1 Calculate EŒNI  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.4.2 Calculate Pp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.4.3 Expected Cycle Length Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.4.4 Optimization of Maintenance Cost Rate Policy . . . . . . . . . . . . . . .
10.4.5 Numerical Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.5 Warranty Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

397
397
398
398
399
399
402

403
404
405
405
406
406
408

11 Spare Parts Forecasting and Management . . . . . . . . . . . . . . . . . . . . . . .
11.1 Spare Parts Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.2 Spare Parts Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.3 Forecasting Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.4 Croston Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.5 Poisson Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.6 Binomial Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.6.1 Numerical Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.7 Spare Parts Forecasting Accuracy . . . . . . . . . . . . . . . . . . . . . . . . .
11.8 Spare Parts Forecasting Methods: Application and Case Studies
11.8.1 Case Study 1: Spare Parts Forecasting for an Aircraft . . . . . . . . .
11.8.2 Case Study 2: Spare Parts Forecasting in a Steel Company . . . . .
11.9 Methods of Spare Parts Management . . . . . . . . . . . . . . . . . . . . . . .
11.9.1 Spare Parts Management: Qualitative Methods . . . . . . . . . . . . . . .
11.9.2 Spare Parts Management: Quantitative Methods . . . . . . . . . . . . . .

409
409
410
411
412
413

414
415
416
417
417
418
422
423
426

12 Applications and Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12.1 Preventive Maintenance Strategy Applied
to a Waste to Energy Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12.1.1 Motor System Reliability Evaluation . . . . . . . . . . . . . . . . . . . . . . .

433
433
434


xviii

Contents

12.1.2
12.1.3
12.1.4
12.1.5
12.1.6
12.1.7

12.1.8
12.1.9
12.2

Bucket Reliability Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Motor System. Determination of Maintenance Costs . . . . . . . . . .
Time-Based Preventive Replacement for the Motor System . . . .
Time-Based Preventive Replacement for the Bucket Component
Time-Based Preventive Replacement with Durations Tp and Tf .
Downtime Minimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Monte Carlo Dynamic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .
Monte Carlo Analysis of the System . . . . . . . . . . . . . . . . . . . . . . . .
Reliability, Availability, and Maintainability Analysis
in a Plastic Closures Production System for Beverages . . . . . . . .
RBD construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rotating Hydraulic Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Data Collection and Reliability Evaluation of Components . . . . .
Reliability Evaluation, Nonrepairable Components/Systems . . . .
Data on Repairs and Maintenance Strategies . . . . . . . . . . . . . . . . .
Monte Carlo Analysis of the Repairable System . . . . . . . . . . . . . .
Alternative Scenarios and System Optimization . . . . . . . . . . . . . .
Conclusions and Call for New Contributions . . . . . . . . . . . . . . . . .

436
437
439
439
441
442
442

446

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A.1
Standardized Normal Distribution . . . . . . . . . . . . . . . . . . . . . . . . . .
A.2
Control Chart Constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A.3
Critical Values of Student’s Distribution with Degree
of Freedom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

463
463
464

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

467

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

475

12.2.1
12.2.2
12.2.3
12.2.4
12.2.5
12.2.6
12.2.7

12.3
A

446
448
449
449
454
456
456
460
462

465


1

A New Framework for Productivity
in Production Systems

Contents
1.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

1.2

A Multiobjective Scenario . . . . . . . . . . . . . . . . . . . . .
1.2.1 Product Variety . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.2 Product Quality . . . . . . . . . . . . . . . . . . . . . . . . .

2
3
3

1.3

Production System Design Framework . . . . . . . . .

4

1.4

Models, Methods, and Technologies
for Industrial Management . . . . . . . . . . . . . . . . . . . .
1.4.1 The Product and Its Main Features . . . . . . . . .
1.4.2 Reduction of Unremunerated Complexity:
The Case of Southwest Airlines . . . . . . . . . . .
1.4.3 The Production Process and Its Main Features
1.4.4 The Choice of Production Plant . . . . . . . . . . .

1.5

1.6

1.7

Design, Management, and Control
of Production Systems . . . . . . . . . . . . . . . . . . . . . . . .
1.5.1 Demand Analysis . . . . . . . . . . . . . . . . . . . . . . .
1.5.2 Product Design . . . . . . . . . . . . . . . . . . . . . . . . .
1.5.3 Process and System Design . . . . . . . . . . . . . . .
1.5.4 Role of Maintenance in the Design
of a Production System . . . . . . . . . . . . . . . . . .
1.5.5 Material Handling Device Design . . . . . . . . .
1.5.6 System Validation and Profit Evaluation . . . .
1.5.7 Project Planning and Scheduling . . . . . . . . . .
1.5.8 New Versus Existing Production Systems . . .
Production System Management Processes
for Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.6.1 Inventory and Purchasing Management . . . . .
1.6.2 Production Planning . . . . . . . . . . . . . . . . . . . . .
1.6.3 Distribution Management . . . . . . . . . . . . . . . .

5
5
6
7
7
10
10
10
10
11
11
11
11

11
13
14
14
14

Research into Productivity
and Maintenance Systems . . . . . . . . . . . . . . . . . . . . . 14

The pressure of the global market ... we all face increased competition for share. The fundamental key is
the productivity of the system. All players in the indus-

try are in the same race to become low cost producers,
including manufacturers, our suppliers, and their suppliers, too. And each of us must do it while improving quality, because consumers require it (Alain Batty,
CEO, Ford Motor Company of Canada, 2004).
High levels of product personalization and quality standardization are essential requirements in current market conditions, in which prices are falling, and
in which a new production paradigm for a production
system has come into existence.
The planning, management, and control of a production system are crucial activities requiring an integrated approach examining the internal features of
available production resources and guiding their rational exploitation.
Maintenance techniques play a major role in supporting research into productivity, and these very effective tools must be adopted by modern companies.

1.1 Introduction
In this book explicitly devoted to maintenance, the
first chapter aims to identify and to illustrate the
most critical issues concerning the planning activity, the design, the management, and the control of
modern production systems, both producing goods
(manufacturing systems in industrial sectors) and/or
supplying services (e. g., hospital, university, bank).
By this discussion it is possible to identify the role of

maintenance in a production system and the capability
of guaranteeing a high level of safety, quality, and productivity in a proper way. In particular, the expression

R. Manzini, A. Regattieri, H. Pham, E. Ferrari, Maintenance for Industrial Systems
© Springer 2010

1


2

“research for productivity” frequently animates the
sections of this chapter.
The following section introduces the uncertain operating scenario that modern companies have to face
to compete in a globalized market.
Section 1.3 illustrates a meta-framework for the design of a production system with an enterprise perspective. The aim is to underline the most important
tasks and decisional steps affecting the performance
of the system with particular attention being given to
the business and corporate strategies of the enterprise
and its related companies.
Section 1.4 briefly discusses the models, methods,
and technologies currently available to support the decision-making process dealing with production systems.
Section 1.5 presents a conceptual framework, proposed by the authors, for the integration of the design,
management, and control of a production system.

1.2 A Multiobjective Scenario
Vaughn et al. (2002) identified the most critical factors
affecting the performance of a production system as
part of an enterprise system. The enterprise does not
have complete control over these factors:

• Market uncertainty. This is defined as the demand
fluctuations for the product, including both shortterm random variability and long-term step/cyclical
variability. The uncertainty of demand can create
overcapacity or undercapacity, generating customer
dissatisfaction.
• Production volume, i. e., the number of products to
be manufactured over a time period. Market uncertainty and production volume are tightly coupled.
Production volume determines the production system capacity and most of the factory physical design, e. g., floor space needed, machine selection,
layout, and number of workers.
• Product mix. This is the number of different products to be manufactured. The production system
has to be capable of producing various versions of
a product, or different products simultaneously in
the same plant in order to fulfill the market need
with the best exploitation of the resources. Product mix and product volume are closely related
(Manzini et al. 2004).

1 A New Framework for Productivity in Production Systems

• Frequency of changes. This is the number of engineering changes per time period. The changes can
be either structural or upgrades to existing systems.
It is not possible to foresee all the changes that
might be introduced into a product in the future. For
example, the frequency of changes is a very critical
issue for the electronic control systems of packaging machines. A packaging system can be used by
a generic customer for a few decades: the electronic
technologies change very quickly and the customer
could need to replace failed parts with new, different spare parts.
• Complexity. There are several ways to measure
product, process, or system complexity. A few examples are the number of parts, the number of process steps, and the number of subsystems. Complexity deals with the level of difficulty to design,
manufacture, assemble, move, etc. a part, and it

is affected by the available process capability (see
Chap. 2).
• Process capability, as the ability to make something repeatedly with minimal interventions. This
factor deals with the quality of the process, product, and production systems, as properly illustrated
in Chap. 2.
• Type of organization and in particular the innovation of the workforce participating in product, process, and system improvements.
• Worker skill level, i. e., the availability of high-level
employee skills. This factor is strongly linked to the
necessary and/or available level of automation.
• Investment, as the amount of financial resources required. This is one of the most critical constraints
in the production system design, management, and
control.
• Time to first part. This is another very critical constraint and represents the time from the initial system design to the full rate of production.
• Available/existing resources (financial, technological, human, etc.).
Current markets have changed a great deal from those
of a few years ago. Mass production (large quantities
of a limited range of products) has declined in several
production systems and been replaced by customeroriented production. Sales and quantities have essentially remained constant, but the related product mix
is growing ever larger. Companies are attempting to
spread risk over a wider range of base products and


1.2 A Multiobjective Scenario

3

meet (or anticipate) customer needs and desires. This
trend is intensified by global competition: different
players throughout the world are supplying “similar”
products to the same markets.

This situation has produced significant changes in
production systems (which either produce products or
supply services): production batches are very small,
production lead times are kept very short, product life
cycle is also brief, and consequently product time to
market is very compressed.
In conclusion, production systems must possess
two important features: flexibility and elasticity. Flexibility deals with the ability of the production system to evolve continuously and manufacture wide
ranges of products. On the other hand, elasticity allows great variation in production volumes without
a significant change in the production system configuration (i. e., without needing time-consuming and expensive work). The literature also names these concepts “capability flexibility” and “capacity flexibility.”

1.2.1 Product Variety
The great increase in product variety is easily verified
in several case studies. It is sufficient to investigate
a single product in order to see how many different
versions are now offered in comparison with 10 years
ago.
Some significant results from the research conducted by Thonemann and Bradley (2002) on product
variety analysis are reported below.
Table 1.1 shows the increase of product mix in different industrial sectors in the decade 1990–2000. The
smallest increase of a little over 50% occurred in commodities.
Table 1.1 Product variety increase in various industrial sectors
Sector

Percentage variety increase
(1990–2000)

Commodities
Telecommunications
Information technology

Automotive
Defense

52
57
77
80
81

Table 1.2 Increase in variety of different products
Product

1970

2001

Car models
Newspapers
TV sizes
Breakfast cereals
Types of milk
Running shoes
Brands of sparkling waters
Pantyhose

140
339
5
160
4

5
16
5

260
790
15
340
19
285
50
90

The change in several product mix ratios is relevant
and, as Table 1.2 illustrates, these have more than doubled in some cases.

1.2.2 Product Quality
In addition to the range of the product mix, another
feature has also greatly increased in significance and
is a growing trend: product quality. Consumers have
developed great sensitivity and their perception of the
quality of products and services is increasing.
Consequently, companies must not only produce
but also supply products and services to very high
quality standards, meaning stand-alone quality is no
longer a marginal success factor.
In addition to these observations of “new market
trends,” industrial and service companies also need
their industrial investments to be remunerated. This
field is also significantly affected by global competition: with prices falling, companies are forced to reduce production costs. Therefore, modern companies

must expand their product mix, increase the quality of
the product and the process, and reduce costs: a very
stimulating challenge!
Moreover, companies are striving to improve the
productivity and quality of their production systems,
with the most relevant targets in this multiscenario
decision-making process including:
• a great degree of flexibility and elasticity in the production system;
• short lead times;
• high-quality products and production processes;
• short time to market;
• control of production costs.


4

1 A New Framework for Productivity in Production Systems

1.3 Production System Design
Framework

INFRASTRUCTURE

This section presents a conceptual framework for supporting the design of a production system with an
enterprise perspective. It takes inspiration from the
study by Fernandes (2001) in the aerospace industry
and lean production. The illustration of this framework is very useful for identification of the operating
context of modern production systems and for justification of the introduction of an integrated quality-,
safety-, and reliability-based approach to support the
design, management, and control of a complex system.

In particular, maintenance models and methods reveal
themselves as very effective tools to conduct this process.
Figure 1.1 presents the meta-framework which also
contain other tools, methods, and processes applicable
to the design process of production systems operating
in different industrial and service sectors, such as auto-

motive, food, health care, pharmaceutical, education,
and public administration.
The proposed framework is made of three main and
distinct elements:
1. Infrastructure, as a result of the enterprise strategy formulation which defines important and critical attributes of the system as operating policy, organizational structure, location, and environment
(see the top portion of Fig. 1.1). This strategy is
the result of long-term objectives and programs,
and is focused on creating operating capabilities.
The corporate-level strategy balances the conflicting needs of the numerous stakeholders (e. g., customers, employees, and owners) facing the overall
enterprise the production system belongs to, by the
formulation of a corporate strategy which is transferred to the business units throughout the corporation.
2. Structure (see the bottom portion Fig. 1.1). It is
the physical manifestation of the detailed produc-

Corporate
level

Strategy
formulation

Stakeholders

Corporate level

Corporate
business strategy

Business unit

Suppliers
Make/buy

Product
Production
Marketing
design
system
DFA, DFM, conCustomer needs
current engineering technical feasibility

Product
strategy

Manufacturing

Physical
implications

STRUCTURE

Requirements/considerations/constraints

Manufacturing system design/selection

Analytical &
simulation tools
Implement
Fine tune

Trial & error
Evaluate/
validate

Finalized product design

RATE PRODUCTION

Fig. 1.1 Production system design framework. DFA design for assembly, DFM manufacturing. (Fernandes 2001)

System
design

Marketing

PRODUCT
STRATEGY

Suppliers

Product
design


1.4 Models, Methods, and Technologies for Industrial Management

tion system design and is the result of the factory
layout, number and configuration of machines, and
production methods and processes.
3. Product strategy. congruence between the
corporate-level business strategy and the functional strategies. It involves functional elements
such as marketing, product design, supplier, and
manufacturing (see the concurrent engineering
overlapping of functions in Fig. 1.1)
This meta-framework gives the concurrent engineering approach a great and strategic importance and provides enlightenment on the validation analysis, and the
continuous improvement (see the so-called modification loop in Fig. 1.1).

1.4 Models, Methods, and Technologies
for Industrial Management
Which resources are capable of supporting companies
in meeting the challenge introduced in the previous
section?
First of all, it is important to state that only resources relating to products (or services) and to production processes (i. e., manufacturing and assembly
activities in industrial companies) are considered in
this chapter. It is not the authors’ purpose to take into
account some other factors associated with advertising, marketing, or administrative areas.
In brief, research supports productivity via three
fundamental and interrelated drivers: the product, the
process, and the production system.

1.4.1 The Product and Its Main Features
Products are usually designed with reference to their
performance (i. e., the ability to satisfy customer
needs) and to the aesthetic appearance required by
the market. Requirements derived from the production system are sometimes neglected, thus having

a negative effect on final production costs. As a consequence, during the last few decades several strategies
or techniques for product design, such as design for
manufacturing (DFM) and design for assembly (DFA),
which, respectively, consider manufacturing and assembly requirements during the design process, have

5

been proposed in the literature and applied in modern
production systems. They provide a valid support to
the effective management of total production costs.
In recent years, the matter of reuse and/or recycling
of products has become extremely pressing worldwide, and many countries are facing problems relating
to waste evaluation and disposal. The significance of
these topics is demonstrated by the wide diffusion of
product life cycle management, as the process of managing the entire life cycle of a product from its conception, through design and manufacture, to service
and disposal. Figure 1.2 presents a conceptual model
of the product life cycle, including the design, production, support, and ultimate disposal activities. Maintenance of production facilities and recovery of products explicitly play a strategic role in product life cycle
management.
As a consequence, a product design process that
also considers product disassembly problems at the
end of the product life cycle has become a success factor in modern production systems. This approach to
the design process is known as “design for disassembly” (DFD). In several supply chains (e. g., tires and
batteries) the manufacturer is burdened with the reuse
or final disposal of the product, and DFD is a particularly effective tool for the reduction of production
costs. Section 1.2 discusses the advantages and disadvantages associated with the production of a wide
variety of products: wide ranges of product mix are
an effective strategy in meeting customer expectations,
but companies must reach this goal with the minimum
number of components and parts.
In particular, any part or function not directly perceived by the customer implies both an unnecessary

and a harmful addition of complexity because it is not
remunerated. Research and trials examining this special kind of complexity lead to the formulation of the
following production strategy: what is visible over the
skin of the product is based on a very high degree of
modularity under the skin.
The so-called product platforms are a good solution to support product variability, and so have been
adopted in modern production systems. Several families of similar products are developed on the same
platform using identical basic production guidelines
for all “derivative” products. A well-known example
is the “spheroid platform” developed by Piaggio (the
Italian manufacturer of the famous Vespa scooter): the
products named Zip, Storm, Typhoon, Energy, Skip-


6

1 A New Framework for Productivity in Production Systems

Fig. 1.2 Product life cycle
model

per, Quartz, and Free are all derived from the same
underlying fundamental design of the scooter called
“Sphere” (hence the spheroid platform). Another significant example is the standardization of car speed indicators in the automotive sector: the manufacturers
tend to use the same component in every product mix
regardless of the speed attainable by each individual
car model. As a result of this strategy, the range of the
product mix is reduced and the management of parts
is simplified without affecting product performance.
Every remark or comment about the techniques and

strategies cited is also effective both in production systems and in supply services such as hospitals, banks,
and consultants.

1.4.2 Reduction of Unremunerated
Complexity: The Case
of Southwest Airlines
Southwest Airlines has developed several interesting
ideas for reducing complexity in the service sector.

Figure 1.3 shows the cost per passenger for each mile
traveled with the main US airlines.
Two fundamental facts can be observed in Fig. 1.3:
since 2004 the cost per passenger for each mile
traveled (extrapolated from available seat miles) for
Southwest Airlines has been lower than for its competitors, clearly competing in the same market and
over the same routes. Moreover, the available seat
mile costs of Southwest Airlines have continued to
decrease since 11 September 2001, in contrast to
those of its competitors. Moreover, these costs have
significantly increased owing to the increase in the
cost of petroleum and owing to the introduction of
new safety and security standards.
How can this be explained? The answer lies in the
efforts of Southwest Airlines, since 1996, to reduce the
variety and complexity of services offered to its customers but not directly perceived by them.
A significant analysis of the fleet configurations of
major American airlines is reported in Table 1.3.
The average number of different models of airplane
used by the major USA airlines is 14, but Southwest
Airlines employs only Boeing 737 airplanes. In fact,

1.4 Models, Methods, and Technologies for Industrial Management

7

Table 1.3 Number of different models of airplane used by USA airlines (June 2008)

No. of different models of airplane in fleet

United
Airlines

Delta
Airlines

American
Airlines

Average for
USA airlines

Southwest
Airlines

13

9

6

7

1

Boeing 737

very effectively. Among a great many original approaches proposed during the last two decades for the
reduction of complexity in a production system, the
well-known Variety Reduction Program (VRP) developed by Koudate and Suzue (1990) is worthy of mention.

1.4.3 The Production Process
and Its Main Features

Fig. 1.3 Cost per passenger for each mile traveled. ASM available seat miles. (United States Securities and Exchange Commission 2000)

in June 2008, Southwest Airlines owned 535 airplanes
of this particular type but using various internal configurations, ranging from 122 to 137 seats.
Variety based on the type of airplane is completely
irrelevant to customers. Furthermore, when a passenger buys a ticket, the airline companies do not communicate the model of airplane for that flight. However,
reducing the number of different models of airplane in
the fleet directly results in a significant saving for the
airline company: only one simulator for pilot training
is required, only one training course for technicians
and maintenance staff, spare parts management and
control activities are optimized, “on ground” equipment such as systems for towing and refueling are
standard, etc.
In spite of critical safety problems and high fuel
costs, Southwest Airlines has been able to compete

Production processes in several industrial sectors have
recently been forced to undergo significant transformations in order to ensure both cost reductions and
high quality. A good example from the wood sector is
the nonstop pressing process used to simplify the assembly process by using small flaps, gluing, and other
techniques instead of screw junctions.
Every process innovation capable of consuming too
many production resources such as energy, manpower,
and raw materials is a very useful motivating factor
driving research into productivity.
Consequently, when a new production investment
is being made in a manufacturing or service sector,
a benchmark investigation is required in order to check
the state of the art of the production processes. In addition to this, from an economic or technical point of
view, scouting for alternative processes that could be
more effective is also recommended.

1.4.4 The Choice of Production Plant
An effective production process is a basic condition
in making an entire production system effective. Thorough analysis of the specific characteristics of production factors, e. g., resources and equipment required by
the available processes, is one of the most important
aspects of research into productivity.


8

It is possible to have two different production plants
carrying out the same process with their own specifications and production lead times to get the same results,
but at different costs.
A great deal of effort in innovation of the plant
equipment has taken place in recent years, but innovation in the production process is a very difficult problem to solve, often involving contributions

from various industrial disciplines (e. g., electronics,
robotics, industrial instrumentation, mechanical technology). One of the most significant trends in equipment innovation developments is represented by flexible automation, which provides the impetus for a production system to achieve high levels of productivity.
Presently, industrial equipment and resources are
highly automated. However, flexible automation is
required so that a wide mix of different products
and services is achieved without long and expensive
setups. One of the best expressions of this concept, i. e., the simultaneous need for automation and
flexibility, is the so-called flexible manufacturing
system (FMS). A flexible manufacturing system is

1 A New Framework for Productivity in Production Systems

a melting pot where several automatic and flexible
machines (e. g., computer numerical control (CNC)
lathes or milling machines) are grouped and linked
together using an automatic and flexible material
handling system. The system can operate all job sequences, distinguish between different raw materials
by their codes, download the correct part program
from the logic controller, and send each part to
the corresponding machine. This basic example of
the integration of different parts shows how successful productivity in a modern production system
can be.
The potential offered by flexible automation can
only be exploited effectively if every element of the
integrated system is capable of sharing information
quickly and easily.
The information technology in flexible systems
provides the connectivity between machines, tool storage systems, material feeding systems, and each part
of the integrated system in general.
Figure 1.4 presents a brief classification, proposed

by Black and Hunter (2003), of the main manufac-

Fig. 1.4 Different kinds of manufacturing systems (Black and Hunter 2003)


1.4 Models, Methods, and Technologies for Industrial Management

turing systems in an industrial production context by
comparing different methodologies based on production rates and flexibility, i. e., the number of different
parts the generic system can handle.
In conclusion, the required system integration
means developing data exchange and sharing of information, and the development of production systems in
the future will be based on this critical challenge.
The current advanced information technology solutions (such as local area networks, the Internet, wire-

9

less connectivity, and radio-frequency identification
(RFID)) represent a valid support in the effective integration of production activities.
Figure 1.5 is extracted from a previous study by
the authors and briefly summarizes the productivity
paradigm discussed in this chapter. This figure was
proposed for the first time by Rampersad (1995).
Research into productivity also requires technical,
human, and economic resources. Consequently, before
a generic production initiative is embarked upon, it is

EXTERNAL DEVELOPMENTS
RESOURCES
MARKET DEVELOPMENTS

PRODUCT DEVELOPMENTS

SELLING MARKET
•
•
•
•
•
•
•

•

New design strategies
(DFM, DFA, DFD,..)
• New materials

International competition
Shorter product life cycle
Increasing product diversity
Decreasing product quantity
Shorter delivery times
Higher delivery reliability
Higher quality requirements

PROCESS DEVELOPMENTS
•
•
•

LABOR MARKET
•
•

Increasing labour costs
Lack of well-motivated and
qualified personnel

Innovative processes
New process strategies
New joining methods

SYSTEM DEVELOPMENTS
•
•
•

Flexible automation
Integration
Information technology

COMPANY
COMPANY

POLICY

COMPANY OBJECTIVES

ACTIVITIES

•

High flexibility

•

Effective system design

•

Constant and high product quality

•

Effective system management

•

Short throughput times

•

Low production costs

Fig. 1.5 The new productivity paradigm for a production system. DFM design for manufacturing, DFA design for assembly, DFD
design for disassembly. (Rampersad 1995)