The frameworks for the design and management of a production system, illustrated in Figs. 1.1, 1.5, and 1.6, underline how important the contributions of reliability, availability, and quality of resources (equipment, employees, and production plants) are to the production of products or services. In particular, there is a very strong positive link between mainte- nance and productivity. For example, the availability of a production plant is an absolute necessity for the scheduling of work orders, and spare parts forecasting
is a fundamental part of the planning and design processes (see Chap. 11).
A very important factor in purchasing is the qual- ity control of raw materials, and the new design tech- niques, such as DFM and DFA, must guarantee quality levels set as targets.
Modern companies must consider maintenance strategies, rules, procedures, and actions to be some of the most important issues and factors in their success.
In other words, the effective design and manage- ment of a production system requires the effective design and management of the correlated maintenance process and system.
A maintenance system requires strategic planning, dedicated budgets, relevant investments in terms of money and human resources, equipment, and spare parts too. In particular, the availability and commit- ment of personnel at all levels of an organization also includes the application of the maintenance pro- cess.
An effective maintenance system provides support- ing decision-making techniques, models, and method- ologies, and enables maintenance personnel to apply them in order to set the global production costs at a minimum and to ensure high levels of customer ser- vice. To achieve this purpose in a production system, those elements such as the ability, skill, and knowl- edge required by the whole organization and in partic- ular by product designers, production managers, and people who directly operate in the production plants, are crucial.
In conclusion, as illustrated in Fig. 1.8, mainte- nance techniques, including also quality and safety as- sessment tools and procedures, represent very effec- tive instruments for research into productivity, safety, and quality as modern companies are now forced to pursue them relentlessly. This issue will be demon- strated and supported in detail in the following chap- ters.
The following chapters are organized as follows:
• Chapter 2 introduces quality assessment and presents statistical quality control models and methods and Six Sigma theory and applications.
A brief illustration and discussion of European standards and specifications for quality assessment is also presented.
• Chapter 3 deals with safety assessment and risk as- sessment with particular attention being given to
Fig. 1.8 Maintenance engineering, safety assessment, and quality assessment
risk analysis and risk reduction procedures. Some exemplifying standards and specifications are illus- trated.
• Chapter 4 introduces maintenance and maintenance management in production systems. An illustration of total productive maintenance production philos- ophy is also presented.
• Chapter 5 introduces the main reliability and main- tenance terminology and nomenclature. It presents and applies basic statistics and reliability models for the evaluation of failure (and repair) activities in repairable (and nonrepairable) elementary com- ponents.
• Chapter 6 illustrates some effective statistics-based models and methods for the evaluation and predic- tion of reliability. This chapter also discusses the el- ementary reliability configurations of a production system, the so-called reliability block diagrams.
• Chapter 7 discusses the maintenance information systems and their strategic role in maintenance management. A discussion on computer mainte- nance management software (CMMS) is also pre- sented. Finally, failure rate prediction models are illustrated and applied.
• Chapter 8 presents and applies models for the analysis and evaluation of failure mode, effects, and criticality in modern production systems. Then models, methods, and tools (failure modes and ef- fects analysis and failure mode, effects, and criti-
cality analysis, fault tree analysis, Markov chains, Monte Carlo dynamic simulation) for the evalua- tion of reliability in complex production systems are illustrated and applied to numerical examples and case studies.
• Chapter 9 presents several models and methods to plan and conduct maintenance actions in accor- dance with corrective, preventive, and inspection
strategies and rules. Several numerical examples and applications are illustrated.
• Chapter 10 illustrates advanced models and meth- ods for maintenance management.
• Chapter 11 discusses spare parts management and fulfillment models and tools.
• Chapter 12 presents and discusses significant case studies on reliability and maintenance engineering.
and Statistical Quality Control 2
Contents
2.1 Introduction to Quality Management Systems . . . 17 2.2 International Standards and Specifications . . . . 19 2.3 ISO Standards for Quality Management
and Assessment . . . . 19 2.3.1 Quality Audit, Conformity, and Certification 19 2.3.2 Environmental Standards . . . . 21 2.4 Introduction to Statistical Methods
for Quality Control . . . . 23 2.4.1 The Central Limit Theorem . . . . 23 2.4.2 Terms and Definition in Statistical Quality
Control . . . . 24 2.5 Histograms . . . . 25 2.6 Control Charts . . . . 25 2.7 Control Charts for Means . . . . 26 2.7.1 The R-Chart . . . . 26 2.7.2 Numerical Example, R-Chart . . . . 29 2.7.3 Thex-Chart . . . .N 29 2.7.4 Numerical Example,x-Chart . . . .N 30 2.7.5 The s-Chart . . . . 30 2.7.6 Numerical Example,s-Chart andx-Chart . . .N 33 2.8 Control Charts for Attribute Data . . . . 33 2.8.1 The p-Chart . . . . 35 2.8.2 Numerical Example, p-Chart . . . . 36 2.8.3 The np-Chart . . . . 37 2.8.4 Numerical Example, np-Chart . . . . 37 2.8.5 The c-Chart . . . . 37 2.8.6 Numerical Example, c-Chart . . . . 39 2.8.7 The u-Chart . . . . 40 2.8.8 Numerical Example, u-Chart . . . . 40 2.9 Capability Analysis . . . . 40
2.9.1 Numerical Example, Capability Analysis and Normal Probability . . . . 42 2.9.2 Numerical Examples, Capability Analysis
and Nonnormal Probability . . . . 46
2.10 Six Sigma . . . . 48 2.10.1 Numerical Examples . . . . 51 2.10.2 Six Sigma in the Service Sector. Thermal
Water Treatments for Health and Fitness . . . . 51 Organizations depend on their customers and there- fore should understand current and future customer needs, should meet customer requirements and strive to exceed customer expectations... Identifying, un- derstanding and managing interrelated processes as a system contributes to the organization’s effective- ness and efficiency in achieving its objectives (EN ISO 9000:2006 Quality management systems – fun- damentals and vocabulary).
Nowadays, user and consumer assume their own choices regarding very important competitive factors such as quality of product, production process, and production system. Users and consumers start making their choices when they feel they are able to value and compare firms with high quality standards by them- selves.
This chapter introduces the reader to the main prob- lems concerning management and control of a qual- ity system and also the main supporting decision mea- sures and tools for so-called statistical quality control (SQC) and Six Sigma.