Ramp-up performance in consumer electronics

Auteur Andreas Alexander Pufall
Ramp-up performance in consumer electronics

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Product informatie

  • ISBN 9789088915765
  • ISBN 10 9088915768
  • Druk 1
  • Bindwijze Paperback

Samenvatting

New product development (NPD) is particularly challenging in the high-technology sector, increasingly characterized by shortening product lifecycles, rising market fragmentation, and rapid technological changes (Bowersox et al. 1999, Mallick and Schroeder 2005, Wildemann 2007). If firms want to succeed in this environment, short development lead times and efficient ramp-ups are invaluable for several reasons. First, the faster a company can ramp-up production of a new product, the more quickly it can begin to earn significant revenues from the new product and recoup its development investments (Pisano and Wheelwright 1995). Secondly, fast ramp-ups enable firms to profit from early market opportunities, set technology standards and accumulate experience with volume production. Finally, scarce ... product development and manufacturing engineering resources can be released to support subsequent product development projects instead of solving production problems.Yet many companies fail to meet their new product introduction targets regarding time-to-market and time-to-volume. Schuh et al. (2005) show that 47% of automotive new product ramp-ups were neither technically nor economically successful. Kuhn et al. (2002) indicate that not a single company in their study claimed its production ramp-up was under control. Thus, the ramp-up phase remains a major challenge, even as it provides a significant opportunity for competitive advantages. Despite multiple studies that have identified a vast number of influential factors, we know very little about the quantitative and causal relationships between these factors and ramp-up performance. Krishnan and Ulrich (2001) argue that essentially no work has investigated the relationship between the rate of production ramp-up and product design decisions. This research project addresses the above mentioned limitation with a set of three research studies that are based on operational data from Nokia, a mobile device and service company headquartered in Espoo, Finland. Based on the individual cell phone designed and manufactured by Nokia as the unit of analysis, we investigate four research questions: (1) How can product characteristics (i.e., product complexity) of consumer electronics devices, and specifically of cell phones, be modeled in quantitative terms? (2) How do product complexity characteristics interact with each other and subsequently influence manufacturing and total product ramp-up performance? (3) What are the interrelationships between product complexity characteristics, product development process characteristics (i.e., development lead time) and manufacturing ramp-up performance over the course of the ramp-up lifecycle and (4) What supply-chain structure characteristics uncover plant specific effects in the context of manufacturing ramp-up performance? In order to address these questions, we introduce a conceptual framework in an introductory paper (Pufall et al. 2007) that is based on an extensive literature review. This framework summarizes the key factors that influence ramp-up performance and serves as our guiding principle for the subsequent empirical studies. Based on this framework, our first study uses a set of multiple linear regression (MLR) models that relate quantitative product complexity characteristics ñ represented by software and hardware complexity variables ñ and manufacturing ramp-up performance variables to total product ramp-up performance. We demonstrate that new cell phone features are gradually shifting towards software based implementations that can be implemented on generic hardware. Beyond the fact that software characteristics are gaining importance in driving hardware complexity, we also find that certain hardware characteristics (i.e., product specific component count and parts coupling) have a significant impact on the performance of the manufacturing system in terms of final yield and effective utilization. We also find that effective utilization together with the novelty aspects of software and hardware complexity (i.e., SW novelty and product novelty) are the key determinants of total product ramp-up performance. Our second study uses a partial least squares (PLS) path modeling approach to examine the impact of additional variables and different time horizons on manufacturing ramp-up performance. Compared to MLR and covariance based structural equation modeling approaches (e.g., LISREL), PLS is particularly well suited for studies using operational data and if the primary research objective is the simultaneous maximization of explained variance in all endogenous constructs (i.e., prediction) instead of achieving model ìfitî. An additional result of this study is the finding that development lead time is an important predictor for manufacturing ramp-up performance. While longer development lead times facilitate higher product maturity and thus sustained manufacturing ramp-up performance, later market introductions of new products imply a negative impact on revenue inflows. Additionally, our results suggest that late schedule slips, although disastrous for customer relations in which due dates are crucial, provide the opportunity to build up (semi-finished) product buffers which in turn increase the initial manufacturing ramp-up performance. Finally, our third study highlights that the internal organization of suppliers, the level of automation within a plant and the time period in which a new product is ramped-up are also important drivers of manufacturing ramp-up performance. Because it is the main objective of the high-tech industry to achieve full-scale production and thereby time-to-volume targets as quickly as possible, our results suggest that the selection of the ramp-up factory is a crucial factor for ramp-up success. In summary, our study contributes to the field of operations management by demonstrating the relevant characteristics of product complexity, development lead time and manufacturing that are associated with manufacturing and total product ramp-up performance and by offering a substantially enhanced and more detailed understanding of the entire ramp-up process.

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