Lean product development in practice: Insights from 4 companies

This paper aims to elucidate practitioners' understanding and implementation of Lean in Product Development (LPD). We report on a workshop held in the UK during 2012. Managers and engineers from four organizations discussed their understanding of LPD and their ideas and practice regarding management and assessment of value and waste. The study resulted in a set of insights into current practice and lean thinking from the industry perspective. Building on this, the paper introduces a balanced value and waste model that can be used by practitioners as a checklist to identify issues that need to be considered when applying LPD. The main results indicate that organizations tend to focus on waste elimination rather than value enhancement in LPD. Moreover, the lean metrics that were discussed by the workshop participants do not link the strategic level with the operational one, and poorly reflect the value and waste generated in the process. Future directions for research are explored, and include the importance of a balanced approach considering both value and waste when applying LPD, and the need to link lean metrics with value and waste levels.

Linear dimensionality reduction: Survey, insights, and generalizations

© 2015 John P. Cunningham and Zoubin Ghahramani. Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational properties. These methods capture many data features of interest, such as covariance, dynamical structure, correlation between data sets, input-output relationships, and margin between data classes. Methods have been developed with a variety of names and motivations in many fields, and perhaps as a result the connections between all these methods have not been highlighted. Here we survey methods from this disparate literature as optimization programs over matrix manifolds. We discuss principal component analysis, factor analysis, linear multidimensional scaling, Fisher's linear discriminant analysis, canonical correlations analysis, maximum autocorrelation factors, slow feature analysis, sufficient dimensionality reduction, undercomplete independent component analysis, linear regression, distance metric learning, and more. This optimization framework gives insight to some rarely discussed shortcomings of well-known methods, such as the suboptimality of certain eigenvector solutions. Modern techniques for optimization over matrix manifolds enable a generic linear dimensionality reduction solver, which accepts as input data and an objective to be optimized, and returns, as output, an optimal low-dimensional projection of the data. This simple optimization framework further allows straightforward generalizations and novel variants of classical methods, which we demonstrate here by creating an orthogonal-projection canonical correlations analysis. More broadly, this survey and generic solver suggest that linear dimensionality reduction can move toward becoming a blackbox, objective-agnostic numerical technology.