Abstract
Shorter product lifecycles, shrinking time-to-market and increasing global competition, drive companies to premature transitions from the development laboratory to full-scale commercial production. This ramp-up period is usually considered as a transient phenomenon and often ignored by a large body of literature. Hence, the current push for accelerated development and quality manufacturing of new products, has increased the need to model and measure production performance during ramp-up. Despite this need for a concrete framework of these early stages of the product life cycle, a useful model of ramp-up, formalizing this tradeoff between product design and process modeling during the execution phase, is missing. In this context the present work deals with this issue throught a structured methodology that highlights the system sensitivities by decoupling process and product design, proposing an algorithm that uses empirical evaluation measures of manufacturing complexity.
Highlights
In the primary phases of product development, when the production process is not established, many people are involved with different attitudes, competencies and agendas working in different contexts
In this paper proposes a dynamic approach with information pathways that take under consideration the design and process modifications that occur during ramp-up, and the learning effects that appear in the early production stages
Despite the need for a concrete and useful model of learning during early development stages, a fundamental basis of the concept is missing in the literature
Summary
In the primary phases of product development, when the production process is not established, many people are involved with different attitudes, competencies and agendas working in different contexts. During ramp-up experienced engineers often employ intuitive methods to solve pre-production problems and act according to their instinct to make cost-effective changes, to improve the manufacturability and overall quality In this framework, and while recent efforts have been focused on predicting component quality levels through variability and process capability measures at the design stage, the variability associated with assembly operations is rarely considered in the context of quality [1]. Despite well-documented articles on the functional performance of components (effects of material on process capability, geometric constraints, process precision etc.) [2] there is no work on the relationship between the rate of production ramp-up and product design decision making [3] To fill this gap, in this paper proposes a dynamic approach with information pathways that take under consideration the design and process modifications that occur during ramp-up, and the learning effects that appear in the early production stages. Despite the fact that learning precedes understanding, there are no other known studies combining learning curves of manufacturing systems with complexity
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