Accelerating the Process of Engineering Change Orders: Capacity and Congestion Effects

Engineering change orders (ECOs) are important drivers of development costs and lead time. This article analyzes the process of administering engineering change orders in the case of the climate control system development within a large vehicle development project. This administrative process encompasses the emergence of a change (e.g., a problem or a market-driven feature change), its management approval, and final implementation. Despite strong time pressure, this process can take several weeks, several months, and, in extreme cases, even over 1 year. Such a long lead time is especially remarkable as the actual processing time for the change typically does not exceed 2 weeks. Based on our case study, we develop an analytical framework that explains how such an extreme ratio between theoretical processing time and actual lead time is possible. The framework identifies congestion, stemming from scarce capacity coupled with processing variability, as a major lead time contributor. We outline five improvement strategies that an organization can use in order to reduce ECO lead time, namely, flexible capacity, balanced workloads, merged tasks, pooling, and reduced setups and batching

When Product Development Performance Makes a Difference: A Statistical Analysis in the Electronics Industry

Throughout the pages of JPIM and other publications, researchers and practitioners devote considerable effort to identifying the dimensions of new-product development (NPD) performance that relate most closely to business success. Although we may hope to unveil a set of universal truths about the relationship between NPD performance and business success, the relevant NPD performance measures appear to depend on the industry in which a firm competes. In fact, Christian Terwiesch, Christoph Loch, and Martin Niederkofler suggest that the overall relevance of NPD performance to business success depends on the firm\u27s competitive market environment. In a study of 86 business units operating in 12 different electronics industries worldwide, they develop a market contingency framework for understanding the impact of NPD performance on a firm\u27s profitability. Their study uses data from the “Excellence in Electronics” project, a joint research effort by Stanford University, the University of Augsburg, and McKinsey & Co. They describe market context in terms of three dimensions: market share, market growth, and external stability—that is, the average product life cycle duration in the market. Looking at all 86 business units in the study, they find that industry membership accounts for 23% of the variance in profits, with 18 percent of the variance determined by industry profitability and 5% by the three dimensions of market context. For the firms in the study, development performance has the most significant effect in slow-growth markets and in markets with long product life cycles. In these stable industries, low development intensity, product line freshness, and technical product performance increase profitability. The results indicate that NPD performance plays a much more important role for explaining the profitability of dominant firms than that of the low-market-share firms in the study. NPD performance explains 30% of the profitability variance among the high-market-share business units in the study, but none of the variance for the low-market-share business units. Although the profitability of the smaller firms in the study is driven primarily by the industry environment, these firms can compete on the basis of superior technical performance

Managing the Process of Engineering Change Orders: The Case of the Climate Control System in Automobile Development

Engineering change orders (ECOs) are part of almost every development process, consuming a significant part of engineering capacity and contributing heavily to development and tool costs. Many companies use a support process to administer ECOs, which fundamentally determines ECO costs. This administrative process encompasses the emergence of a change (e.g., a problem or a market-driven feature change), the management approval of the change, up to the change\u27s final implementation. Despite the tremendous time pressure in development projects in general and in the ECO process in particular, this process can consume several weeks, several months, and in extreme cases even over 1 year. Based on an in-depth case study of the climate control system development in a vehicle, we identify five key contributors to long ECO lead times: a complex approval process, snowballing changes, scarce capacity and congestion, setups and batching, and organizational issues. Based on the case observations, we outline a number of improvement strategies an organization can follow to reduce its ECO lead times

Communication and Uncertainty in Concurrent Engineering

We present an analytical model of concurrent engineering, where an upstream and a down-stream task are overlapped to minimize time-to-market. The gain from overlapping activities must be weighed against the delay from rework that results from proceeding in parallel based on preliminary information. Communication reduces the negative effect of rework at the expense of communication time. We derive the optimal levels of concurrency combined with communication, and we analyze how these two decisions interact in the presence of uncertainty and dependence. Uncertainty is modeled via the average rate of engineering changes, and its reduction via the change of the modification rate over time. In addition, we model dependence by the impact the modifications impose on the downstream task. The model yields three main results. First, we present a dynamic decision rule for determining the optimal meeting schedule. The optimal meeting frequency follows the frequency of engineering changes over time, and it increases with the levels of uncertainty and dependence. Second, we derive the optimal concurrency between activities when communication follows the optimal pattern described by our decision rule. Uncertainty and dependence make concurrency less attractive, reducing the optimal overlap. However, the speed of uncertainty reduction may increase or decrease optimal overlap. Third, choosing communication and concurrency separately prevents achieving the optimal time-to-market, resulting in a need for coordination

How Megaprojects Are Damaging Nigeria and How to Fix It

Since 1960, two-thirds of very large governmental projects in Nigeria have not only failed, but been abandoned mid-course. This presents a bigger failure rate than mega projects elsewhere, and yet there is no available data or analysis to help us understand the reasons behind such failures. This book provides an authoritative examination into why very large projects in Nigeria have failed so badly, and provides practical recommendations on how the Nigerian government can improve its project performance. Drawing on data from 38 very large projects (19 completed and 19 abandoned) with a total budget of over $25B, this book presents detailed analysis of these projects and in-depth case studies 11 of the projects, and presents lessons for improvement. Through this, the authors have identified a small number of key success drivers, and argue that making moderate improvements on any of them would, on average, save hundreds of millions of dollars on one large project alone. This book is a game-changer in the management of government mega projects in Nigeria. With clear implications for other developing economies, this is a vital resource for project management practitioners, executives and civil servants. This is an open access book

How Megaprojects Are Damaging Nigeria and How to Fix It

Since 1960, two-thirds of very large governmental projects in Nigeria have not only failed, but been abandoned mid-course. This presents a bigger failure rate than mega projects elsewhere, and yet there is no available data or analysis to help us understand the reasons behind such failures. This book provides an authoritative examination into why very large projects in Nigeria have failed so badly, and provides practical recommendations on how the Nigerian government can improve its project performance. Drawing on data from 38 very large projects (19 completed and 19 abandoned) with a total budget of over $25B, this book presents detailed analysis of these projects and in-depth case studies 11 of the projects, and presents lessons for improvement. Through this, the authors have identified a small number of key success drivers, and argue that making moderate improvements on any of them would, on average, save hundreds of millions of dollars on one large project alone. This book is a game-changer in the management of government mega projects in Nigeria. With clear implications for other developing economies, this is a vital resource for project management practitioners, executives and civil servants. This is an open access book

Selecting R&D Projects at BMW: A Case Study of Adopting Mathematical Programming Models

Research and development (R&D) project selection is a critical interface between the product development strategy of an organization and the process of managing projects day-to-day. This article describes the project selection problem faced by an R&D group of BMW (Munich, Germany). The problem was structured as minimizing the gap between target performance of the technology to be developed and actual performance of the current technology along chosen criteria. A mathematical programming model helped this organization to increase the transparency of their selection process, which previously had been based on experience coupled with evaluation of individual projects in isolation Implementation was a success in that the predevelopment group continues to use the model to make better decisions. However, the organization did not use the model for its intended purpose: constrained optimization. The traditional explanation for this partial implementation is that the analytical model did not capture all considerations relevant to optimization (e.g., uncertainty or strategic fit), and that further model refinements are required to achieve further implementation. We offer an alternative explanation, one based on the technology transfer literature. The diffusion of the analytical model from academia to industry faced the same problems as any technology transfer: Significant tacit knowledge had to be transferred along with the codified knowledge of the analytical model. This required iterated problem solving, which required the limited time and resources of the diffusing agents (academia) as well as the adopting agents (industry). Thus, the organization adopted only those elements of the modeling method that could be transferred within the resource constraints, focusing on those elements offering the highest benefit per effort invested

How BMW Is Defusing the Demographic Time Bomb

The article focuses on how the German car manufacturer, Bayerische Motoren Werke AG or BMW, is managing the effects of population aging on the industry. The redesign of BMW's factories to accommodate older workers and increase productivity is mentioned. Research on a pilot production line, which is based on a mix of employees who have an average age of 47 in the year 2017, is discussed. The roles of Peter Jürschick, Helmut Mauermann, Günther Stadler, and Kurt Dickert in the development of the pilot project are mentioned. The costs associated with an aging population are noted

Supervising projects you don’t (fully) understand: lessons for effective project governance by steering committees

Strategically important projects involve high stakes, uncertainty, and stakeholder complexity, with contingencies and risks typically surfacing repeatedly as the project evolves. This is challenging not only for the project team (PT) but also in particular for the steering committee (SC), the top management oversight structure typically used to align a project with the organization’s strategic goals. This article explores how senior executives on SCs can exercise leadership and effective oversight of strategic projects, although they have only limited time and often incomplete expertise. The SC can keep a project aligned, even with limited time, through focused understanding of the key logic and drivers of the project. The SC needs to manage the surprises and crises that inevitably arise in a difficult project through proactive analysis that goes to the bottom of the problem and by working with the PT to generate solutions