ABSTRACT
The objectives of this chapter are to provide an overview of the methods used in the product life cycle phases and to provide information on problem-solving approaches used for product evaluations The methods (also called techniques or tools) provide the capabilities to organize, visualize, and analyze the collected data and to understand relationships between different variables such as the product characteristics, product attributes, performance measures, customer ratings, and customer satisfaction measures The relationships shown by the methods and supported by statistical inference methods provide important information to make decisions regarding product configurations, function allocations, and trade-offs between different variables related to product attributes, material selection, technology development, process selection, costs, and so forth
Implementation of Systems Engineering and Project Management in the product programs involves use of many methods In general, the methods facilitate performing the following tasks: (1) organize or sort the collected data, (2) display the data, (3) analyze the data, and (4) facilitate applications of statistical data analysis techniques The statistical data analysis techniques provide support in evaluating problems such as determining (1) underlying statistical distributions of variables in the collected data, (2) relationships between independent variables and response variables, (3) cause and effect relationships, (4) groups (or clusters) of variables that have common issues, and (5) combinations of variables that have the largest or least amount of effect on certain dependent variables
Most of these methods can be used to solve a number of problems that are encountered during the entire life cycle of the products Some methods are especially useful in the early phases of the product development process such as product conceptualization and detailed engineering Other specialized engineering analysis tools such as structural analysis, aerodynamic analysis, thermal analysis, electrical analysis, and control systems analysis are especially useful to solve some specialized design issues Whereas, other tools in production planning, control, and product distribution are useful in later stages of the product program
Table 121 presents a summary of various methods used in the product programs The methods thus form a “tool box” for the systems engineers and related fields covered in the preceding chapters The major fields or areas (where the methods originate and are applied) are presented in the first column of the table The second column presents the names of the methods (or tools) used in these fields The third and the succeeding columns present phases in the product life cycle-from pre-concept planning to product
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retirement/disposal The “x” marks in the cells of the table indicate application possibilities of the methods The methods shown in this table are described in Chapters 13 through 16 The specialized engineering analysis methods such as those used in mechanical engineering, production engineering, electrical engineering, chemical engineering, and materials engineering are outside the scope of this book and hence not covered in this book Here, the emphasis is placed primarily on the tools used to perform data-driven decision-making tasks involving product planning, product quality, human factors, safety engineering, systems engineering, and program management
In addition to the classification of the methods by fields as shown in Table 121, the methods can be also classified by how the data are gathered (ie, their data gathering methods) and data presentation formats The data gathering methods can be classified as follows: (1) observation methods, (2) communication methods, and (3) experimentation methods The data presentation methods are as follows: (1) charting methods and (2) plotting methods (Note that it is important to realize here that: (1) data are needed for use in application of all the methods and (2) the data are processed, organized, and results obtained from applications of all the methods need to be displayed and presented to facilitate decision making See also Chapter 6, which presents methods of evaluations [that generate data] and Table 61 that organizes the methods of data collection and types of measurements) The following part of this section provides description of the methods of data gathering and data presentation methods
In the observation methods, information is gathered by direct or indirect observations of the product usage situations to determine how the product performs and how the product users react to the product One or more observers can directly observe or video cameras can be set up (or data acquisition systems can be installed in a product being evaluated) and their recordings can be played back at a later time for observation and analysis The observers need to be trained to identify and classify different types of predetermined behaviors of the product and its users, events, problems, or errors that the users commit during the observation period The observers can also record durations of different types of events, number of attempts made to perform an operation, number and sequence of controls used, number of glances made, and so forth Some events such as accidents are rare and they cannot be measured through direct observations due to excessive amount of direct observation time needed until sufficient numbers of accidents occur and the accident data are collected However, information about such events can be obtained “indirectly,” for example, through reports of near accidents (ie, situations where accidents almost occurred but were averted) and observations gathered after such events through witnesses or from material evidence (eg, spillage of materials, damaged materials, skid marks) Therefore, the information gathered through indirect observations may not be very reliable due to a number of reasons (eg, a witness may be guessing or even deliberately falsifying or objects associated with the event of interest may have been displaced or removed)
The communication methods involve asking the users or the customers to provide information about their impressions and experiences with the product or a process The most common technique involves a personal interview where an interviewer asks each user a series of questions The questions can be asked before, during, or after the product usage The user can be asked questions that will require the user to (1) describe the product or the impressions about the product and its attributes (eg, usability), (2) describe the problems experienced while using the product (eg, could not locate or view a critical item), (3) categorize the product or its performance using a nominal scale (eg, acceptable or unacceptable, comfortable or uncomfortable), (4) rate the product on one or more scales describing its characteristics and/or overall impressions (eg, workload ratings), or (5) compare the product with other competitors’ products presented in pairs based on a given attribute (eg, ease of use, comfort) Interviews can also be conducted with a group of individuals, such as in a focus group, that includes about 8-12 individuals with similar background and are led by an interviewer to brainstorm through a series of questions, and the participants are asked to provide opinions or suggest issues related to one or more products
Some commonly used tools in communication methods in product evaluations include the following: (1) rating scales: using numeric scales, scales with adjectives (eg, acceptance ratings and semantic differential scales) and (2) paired comparisonbased scales (eg, using Thurstone’s method of paired comparisons and analytical hierarchical method) These tools are described in Chapter 6
In addition, many tools used in fields such as Industrial Engineering, Quality Engineering and Design for Six Sigma, and Safety Engineering can be used Some examples of such tools are process charts, task analysis, arrow diagrams, interface diagrams, matrix diagrams, quality function deployment (QFD), Pugh analysis, failure modes and effects analysis (FMEA), and fault tree analysis The above mentioned tools rely heavily on the information obtained through the methods of observation and communication involving the users/customers and members of the multifunctional design teams Additional information on many of these tools is presented in Chapters 13 through 16 and also in other books such as those by Kolarik (1995), Besterfield et al (2003), Creveling et al, (2003), and Yang and El-Haik (2003)
The purpose of experimental research is to allow the investigator to control a research situation (eg, selecting a product design, performing a task or a test condition) so that causal relationships between the response variable and independent variables may be evaluated An experiment includes a series of controlled observations (or measurements of response variables) undertaken in artificial (test) situations with deliberate manipulations of combinations of independent variables in order to answer one or more hypotheses related to the effect of (or differences due to) the independent variables Thus, in an experiment, one or more variables (called independent variables) are manipulated, and their effect on another variable (called dependent or response
variable) is measured, while all other variables that may confound the relationships are eliminated or controlled
The importance of the experimental methods is as follows: (1) they help identify the best combination of independent variables and their levels to be used in designing the product and thus provide the most desired effect on the users and (2) when the competitors’ products are included in the experiment along with the manufacturer’s product, the superior product can be determined To assure that this method provides valid information, the researcher designing the experiment needs to ensure that the experimental situation is not missing any critical factor related to performance of the product or the task being studied Additional information on the experimental methods can be obtained from Kolarik (1995) or other text books on Design of Experiments
Experiments can be also conducted by exercising computer models using various combinations of input variables (or configurations) The computer modeling methods can be classified as follows: (1) mathematical models, (2) simulation models, (3) visualization or animation models, and (4) prototyping using a combination of hardware and software
The data presentation methods allow us to organize and visualize important aspects (eg, trends, groups, relationships) within the data These methods can be classified as follows:
1 Charting methods (eg, flow diagram, process charts, fish diagram, interface diagram, time charts, tree diagrams [event trees, fault trees, decision trees])
2 Plotting methods (eg, histograms, scatter diagrams, pie charts, Pareto charts, polar plots, 3D charts, control charts)
3 Tabular formatted methods (eg, spreadsheet, matrix diagrams, Pugh diagram, design structure matrix, FMEA)
The important methods used during product development (see top part of Table 121) covered in Chapter 13 are as follows: (1) Benchmarking and breakthrough, (2) Pugh diagram, (3) QFD, (4) FMEA, (5) program status chart, and (6) business plan
The methods used in the Quality Engineering area are categorized as New Quality Tools and Traditional Quality Tools The tools are listed in the second and third sections of Table 121 Chapter 14 presents descriptions and examples of these tools
The methods used in the Human Factors Engineering area listed in Table 121 are presented in Chapter 15 Similarly, methods used in the Safety Engineering area listed in Table 121 are presented in Chapter 16
Other production planning and enterprise management tools such as computerassisted design (CAD), computer-assisted process planning (CAPP), materials
requirements planning (MRP), and enterprise resource planning (ERP) are outside the scope of this book and therefore not covered in this book They are typically covered in computer-assisted manufacturing books such as the one by Groover (2008)
It should be noted that methods used in Systems Engineering implementation and management are covered in Section I of this book
Most of the tools covered in this chapter and described in Chapters 13 through 16 can be used with any other tools The selection of tools and their sequence of applications largely depends on the past experiences and backgrounds of the individuals involved in the product programs However, there is a common underlying sequence of steps in problem solving (see Chapter 3, Table 31), which typically includes (1) defining the problem, (2) collecting data to understand issues and variables affecting the problem, (3) developing alternate solutions, (4) evaluating the solutions (by use of a model or an experiment), and (5) applying the selected solution
Tables 122 and 123 present various methods that can be used in each of the phases of the Design, Analyze, Measure, Improve, and Control (DAMIC) and Identify, Design, Optimize, and Verify (IDOV) processes of the Six-sigma improvement projects and the Design for Six-Sigma projects, respectively The methods presented in the second columns of these tables are described in Chapter 14 A trained Black belt
TABLE 12.2 DAMIC Process Used Solving Six-Sigma Improvement Projects
(a professional certified to apply various quality engineering tools) usually selects a set of tools that can best help in accomplishing tasks to be performed to solve the problem in different phases of the quality-related projects
The typical sequence of tools used in the DAMIC process includes cause and effect diagram, Pareto chart, and experiment design Whereas the IDOV process uses benchmarking, QFD, FMEA, Pugh diagram, experiment design, and requirements compliance testing (verification) Some examples of the applications of the tools and the problem-solving processes are provided in Chapter 18
Use of specialized and proven methods is very important in understanding and solving many problems encountered in the product programs The methods help in identifying and estimating strengths of effects and relationships between different variables, and thus, provide information on decision making Succeeding
TABLE 12.3 IDOV Process Used in Design for Six Sigma (DFSS)
chapters in Section III of the book provide descriptions of the methods/tools along with a few examples illustrating their applications Section IV provides several case studies where many of the tools provided important information for making key decisions
