Strategic Lean Organizational Design: Towards Lean World-Small World Configurations through Discrete Dynamic Organizational Motifs

Organizations face strong international competition in the global market arena in achieving strategic goals such as high quality of product or service at lower cost while increasing their ability to respond quickly to requirements of the market. These challenges concern strategically designing organizations that can meet global challenges and specialize locally to meet performance constraints. After introducing the concept of organizational functional and structural motifs as small organizational building block, our findings suggest the hypothesis that a strategic organizational design (SOD) approach to meet these challenges involves maximizing the number and diversity of functional motifs, while minimizing the repertoire of structural motifs. By detecting characteristic structural motifs, we provide organizational leaders with specific Lean SOD solutions with which to meet local and global challenges simultaneously. As a matter of application, we show the implementation of such an SOD approach in nine US hospitals that form one large health care holding

Information system for image classification based on frequency curve proximity

With the size digital collections are currently reaching, retrieving the best match of a document from large collections by comparing hundreds of tags is a task that involves considerable algorithm complexity, even more so if the number of tags in the collection is not fixed. For these cases, similarity search appears to be the best retrieval method, but there is a lack of techniques suited for these conditions. This work presents a combination of machine learning algorithms put together to find the most similar object of a given one in a set of pre-processed objects based only on their metadata tags. The algorithm represents objects as character frequency curves and is capable of finding relationships between objects without an apparent association. It can also be parallelized using MapReduce strategies to perform the search. This method can be applied to a wide variety of documents with metadata tags. The case-study used in this work to demonstrate the similarity search technique is that of a collection of image objects in JavaScript Object Notation (JSON) containing metadata tags.This work has been done in the context of the project “ASASEC (Advisory System Against Sexual Exploitation of Children)” (HOME/2010/ISEC/AG/043) supported by the European Union with the program “Prevention and fight against crime”.info:eu-repo/semantics/publishedVersio

Insights into the Prevalence of Software Project Defects

This paper analyses the effect of the effort distribution along the software development lifecycle on the prevalence of software defects. This analysis is based on data that was collected by the International Software Benchmarking Standards Group (ISBSG) on the development of 4,106 software projects. Data mining techniques have been applied to gain a better understanding of the behaviour of the project activities and to identify a link between the effort distribution and the prevalence of software defects. This analysis has been complemented with the use of a hierarchical clustering algorithm with a dissimilarity based on the likelihood ratio statistic, for exploratory purposes. As a result, different behaviours have been identified for this collection of software development projects, allowing for the definition of risk control strategies to diminish the number and impact of the software defects. It is expected that the use of similar estimations might greatly improve the awareness of project managers on the risks at hand

Improvement of quantum approximate optimization algorithm for max–cut problems

The objective of this short letter is to study the optimal partitioning of value stream networks into two classes so that the number of connections between them is maximized. Such kind of problems are frequently found in the design of different systems such as communication network configuration, and industrial applications in which certain topological characteristics enhance value–stream network resilience. The main interest is to improve the Max–Cut algorithm proposed in the quantum approximate optimization approach (QAOA), looking to promote a more efficient implementation than those already published. A discussion regarding linked problems as well as further research questions are also reviewed

Computer simulation of the laminar nozzle flow of a non-Newtonian fluid in a rubber extrusion process by the finite volume method and experimental comparison

The aim of this work is to describe the computer simulation of the laminar flow through a nozzle in a rubber extrusion process by the finite volume method (FVM). The liquid rubber is a highly compliant material, whose behavior is not described by the Newtonian constitutive relations, and whose underlying physics is not yet completely understood. The processing and transport of such fluids are central problems in the polymer, plastics and automotive industries. Non-Newtonian behavior manifests itself in a number of different ways. This fluid exhibits a shear rate dependent viscosity, with 'shear thinning', that is, decreasing viscosity with increasing shear rate, being the most prevalent behavior. We have taken the power-law model in order to simulate this rubber extrusion process, which has the form = KI2(n - 1) / 2, where , I2, n and K are termed the dynamic viscosity, the second invariant of the rate of deformation tensor, the power-law index and the consistency, respectively. These last two parameters were obtained from experimental tests and used in a computer simulation. In this work we have modeled two types of rubbers and different inlet pressures, for a type of nozzle, in order to calculate the outlet velocity distribution of the rubber jet in this extrusion process. Finally we have compared the numerical and experimental results, so that this model is consistent with the experimental evidence. © 2007 Elsevier B.V. All rights reserved