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Quality Function Deployment (QFD) proposes to take into account the “voice of the customer,” through a list of customer needs, which are (qualitatively) mapped to technical requirements in House One. But customers do not perceive products in this space, nor do they not make purchase decisions in this space. Marketing specialists use statistical models to map between a simpler space of customer perceptions and the long and detailed list of needs. For automobiles, for example, the main axes in perceptual space might be categories such as luxury, performance, sport, and utility. A product’s position on these few axes determines the detailed customer requirements consistent with the automobiles’ position such as interior volume, gauges and accessories, seating type, fuel economy, door height, horsepower, interior noise level, seating capacity, paint colors, trim, and so forth. Statistical models such as factor analysis and principal components analysis are used to describe the mapping between these spaces, which we call House Zero.

This paper focus on House One. Two important steps of the product development process using House One are: (1) setting technical targets; (2) identifying the inherent tradeoffs in a design including a position of merit. Utility functions are used to determine feature preferences for a product. Conjoint analysis is used to capture the product preference and potential market share. Linear interpolation and the slope point formula are used to determine other points of customer needs. This research draws from the formal mapping concepts developed by Nam Suh and the qualitative maps of quality function deployment, to present unified information and mapping paradigm for concurrent product/process design. This approach is the virtual integrated design method that is tested upon data from a business design problem.

Purpose – We make a case for bridging two types of logics – analytic and dialectic – for explaining processes of social-historical change, and maintain that a successful bridge between these two logics depends on a variety of conditions and most especially the type of analytic logic or model one employs for capturing dynamic processes.

Methodology/approach – Conventional models of social change processes typically presuppose ergodic social worlds and are problematic as analytic approaches generally and most certainly are not fertile grounds for feeding dialectic theorization. Instead, we propose modeling dynamic processes that begin by assuming a nonergodic social world – one in flux, one that is nonrepeating, one within which model process and parameter structures are historically contingent and change with time, one that is autocatalytic, creating and changing its own possibilities.

Findings – We develop the line of thinking adumbrated above and illustrate these modeling strategies with empirical examples from US labor movement history. Results from these examples lend much weight to our proposals. Thus, this chapter demonstrates that concerns about the use of ergodic assumptions and about greater use of dialectical reasoning when studying social processes are not idle speculations within theoretical commentaries but have practical consequences in the conduct of research and the building of better theory.

Research limitations/implications – To approximate such an approach, social scientists should avoid cross-sectionalist and longitudinal modeling strategies that presuppose stability and homogeneity in parameter and process structures. Homogeneity and stability in parameter and process structures should be demonstrated, not assumed.

Originality/value – Rather than accepting the alienated spheres of social science analytics and dialectic theory, our proposal presupposes nonergodic social worlds and takes pragmatic steps for estimating analytic models that are more amenable to dialectic reasoning. Models that take nonergodicity seriously not only have the potential to produce better, historically grounded analytics but are also best suited to bridge with dialectic logic, thus taking advantage of the strengths of both forms of logic.

Processes, in practice, may involve more than one quality characteristic that are of interest. It is quite possible for such quality characteristics to not be independent of each other since the magnitude of one of the characteristics may influence the magnitude of the other characteristics. Under this setting, it is of interest to determine the optimal settings of the process parameters (usually the process mean and the process standard deviation of each quality characteristic) under various objectives. Some of the objectives may be conflicting to each other. In general, it may be possible for the decision-maker to prioritize the objectives. Using such a prioritized scheme, it is of interest to determine the optimal settings of the process mean and standard deviation for each quality characteristic that is being monitored. Such solutions could be labeled as “satisficing” solutions. Sensitivity analyses of the decision variables to the chosen objectives and parameter values are also investigated.

One crucial but sometimes overlooked fact regarding the difference between observation in the cross-section and observation over time must be stated before proceeding further. Tempting though it is to draw conclusions about the dynamics of a process from cross-sectional observations taken as a snapshot of that process, it is a fallacious practice except under a very precise condition that is highly unlikely to obtain in processes of interest to the social scientist. That condition is known as ergodicity.

Despite certain studies in the given sphere, the issues of systemic organization of management of business decisions, aimed at increase of competitiveness of products of the studied sphere, are not elaborated in the Russian theory and practice. At present, the task of formation of a completely new theoretical and methodological basis of production management, improvement of existing and development of modern mechanisms, methods, and technologies of design and efficiency management of business decisions, which allow increasing competitiveness, becomes a top priority in activities of any company. This is confirmed by active development in international practice of the systems of management and support for products’ life cycle. These technologies are oriented at flexible high-tech production with wide usage of automatized systems of production and complex technological processes. A special role in scientific development of the issue of increase of effectiveness of management of industrial companies belongs to studies on creation of integrated production systems and application of new technologies of management, which are based on increase of products’ quality. These issues are especially topical for domestic manufacturer. The key components of competitiveness of an industrial company are innovational and technological development of productions, creation, and development of new organizational and institutional forms, strategic partnership of participants of production and technological chain, and emphasis on quality and safety of manufactured products.

Six sigma is a structured methodology that uses mathematical and statistical tools to increase productivity and business efficiency. In order to apply it, it is necessary to have a very broad knowledge in the fields of mathematics, management, and sector to which the process or the product under consideration (mechanical, electronic, chemical, etc.) refers. It is based on guidelines with a rigid and formalized approach to deal with misunderstandings and minimize subjectivity as much as possible.

This chapter will provide the basic information regarding the birth of the methodology and the composition of the work team; moreover, the procedure define, measure, analyze, improve, and control (DMAIC) will be presented and some tools will be described in order to obtain the improvements desired for the product or the process.