49,007 research outputs found

    A generalised dynamic factor model for the Belgian economy - Useful business cycle indicators and GDP growth forecasts

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    This paper aims to extract the common variation in a data set of 509 conjunctural series as an indication of the Belgian business cycle. The data set contains information on business and consumer surveys of Belgium and its neighbouring countries, macroeconomic variables and some worldwide watched indicators such as the ISM and the OECD confidence indicators. The statistical framework used is the One-sided Generalised Dynamic Factor Model developed by Forni, Hallin, Lippi and Reichlin (2005). The model splits the series in a common component, driven by the business cycle, and an idiosyncratic component. Well-known indicators such as the EC economic sentiment indicator for Belgium and the NBB overall synthetic curve contain a high amount of business cycle information. Furthermore, the richness of the model allows to determine the cyclical properties of the series and to forecast GDP growth all within the same unified setting. We classify the common component of the variables into leading, lagging and coincident with respect to the common component of quarter-on-quarter GDP growth. 22% of the variables are found to be leading. Amongst the most leading variables we find asset prices and international confidence indicators such as the ISM and some OECD indicators. In general, national business confidence surveys are found to coincide with Belgian GDP, while they lead euro area GDP and its confidence indicators. Consumer confidence seems to lag. Although the model captures the dynamic common variation contained in the data set, forecasts based on that information are insufficient to deliver a good proxy for GDP growth as a result of a nonnegligible idiosyncratic part in GDP's variance. Lastly, we explore the dependence of the model's results on the data set and show through a data reduction process that the idiosyncratic part of GDP's quarter-on-quarter growth can be dramatically reduced. However, this does not improve the forecasts.Dynamic factor model, business cycle, leading indicators, forecasting, data reduction.

    A prescriptive approach to qualify and quantify customer value for value-based requirements engineering

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    Recently, customer-based product development is becoming a popular paradigm. Customer expectations and needs can be identified and transformed into requirements for product design with the help of various methods and tools. However, in many cases, these models fail to focus on the perceived value that is crucial when customers make the decision of purchasing a product. In this paper, a prescriptive approach to support value-based requirements engineering (RE) is proposed, describing the foundations, procedures and initial applications in the context of RE for commercial aircraft. An integrated set of techniques, such as means-ends analysis, part-whole analysis and multi-attribute utility theory is introduced in order to understand customer values in depth and width. Technically, this enables identifying the implicit value, structuring logically collected statements of customer expectations and performing value modelling and simulation. Additionally, it helps to put in place a system to measure customer satisfaction that is derived from the proposed approach. The approach offers significant potential to develop effective value creation strategies for the development of new product

    Mutation testing on an object-oriented framework: An experience report

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    This is the preprint version of the article - Copyright @ 2011 ElsevierContext The increasing presence of Object-Oriented (OO) programs in industrial systems is progressively drawing the attention of mutation researchers toward this paradigm. However, while the number of research contributions in this topic is plentiful, the number of empirical results is still marginal and mostly provided by researchers rather than practitioners. Objective This article reports our experience using mutation testing to measure the effectiveness of an automated test data generator from a user perspective. Method In our study, we applied both traditional and class-level mutation operators to FaMa, an open source Java framework currently being used for research and commercial purposes. We also compared and contrasted our results with the data obtained from some motivating faults found in the literature and two real tools for the analysis of feature models, FaMa and SPLOT. Results Our results are summarized in a number of lessons learned supporting previous isolated results as well as new findings that hopefully will motivate further research in the field. Conclusion We conclude that mutation testing is an effective and affordable technique to measure the effectiveness of test mechanisms in OO systems. We found, however, several practical limitations in current tool support that should be addressed to facilitate the work of testers. We also missed specific techniques and tools to apply mutation testing at the system level.This work has been partially supported by the European Commission (FEDER) and Spanish Government under CICYT Project SETI (TIN2009-07366) and the Andalusian Government Projects ISABEL (TIC-2533) and THEOS (TIC-5906)

    Commonality analysis as a knowledge acquisition problem

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    Commonality analysis is a systematic attempt to reduce costs in a large scale engineering project by discontinuing development of certain components during the design phase. Each discontinued component is replaced by another component that has sufficient functionality to be considered an appropriate substitute. The replacement strategy is driven by economic considerations. The System Commonality Analysis Tool (SCAT) is based on an oversimplified model of the problem and incorporates no knowledge acquisition component. In fact, the process of arriving at a compromise between functionality and economy is quite complex, with many opportunities for the application of expert knowledge. Such knowledge is of two types: general knowledge expressible as heuristics or mathematical laws potentially applicable to any set of components, and specific knowledge about the way in which elements of a given set of components interrelate. Examples of both types of knowledge are presented, and a framework is proposed for integrating the knowledge into a more general and useable tool

    Formation of machine groups and part families in cellular manufacturing systems using a correlation analysis approach

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    The important step in the design of a cellular manufacturing (CM) system is to identify the part families and machine groups and consequently to form manufacturing cells. The scope of this article is to formulate a multivariate approach based on a correlation analysis for solving cell formation problem. The proposed approach is carried out in three phases. In the first phase, the correlation matrix is used as similarity coefficient matrix. In the second phase, Principal Component Analysis (PCA) is applied to find the eigenvalues and eigenvectors on the correlation similarity matrix. A scatter plot analysis as a cluster analysis is applied to make simultaneously machine groups and part families while maximizing correlation between elements. In the third stage, an algorithm is improved to assign exceptional machines and exceptional parts using respectively angle measure and Euclidian distance. The proposed approach is also applied to the general Group Technology (GT) problem in which exceptional machines and part are considered. Furthermore, the proposed approach has the flexibility to consider the number of cells as a dependent or independent variable. Two numerical examples for the design of cell structures are provided in order to illustrate the three phases of proposed approach. The results of a comparative study based on multiple performance criteria show that the present approach is very effective, efficient and practical.cellular manufacturing; cell formation; correlation matrix; Principal Component Analysis; exceptional machines and parts

    Turning-point indicators from business surveys: real-time detection for the euro area and its major member countries

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    We present tools for real-time detection of turning points in the industrial production growth-cycle of the euro area and its four largest economies. In particular, we apply a multivariate hidden Markov model to national survey results – i.e. to the earliest information about current economic developments - in order to estimate the probability of expansionary and recessionary phases. The balances of opinions used as inputs of the model are selected by ranking them according to their degree of commonality with respect to the cyclical fluctuations of the industrial sector, as estimated with the Generalized Dynamic Factor Model. The indicators appear reliable and stable.business cycle, hidden Markov model, business surveys

    Forecasting inflation and tracking monetary policy in the euro area: does national information help?

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    The ECB objective of price stability is given a quantitative content as a year-on-year growth rate in the euro area HICP close but below 2% over the medium term. While this objective is referred to area-wide price developments, in anticipating monetary policy moves, market analysts pay considerable attention to national data. In this paper we use the Generalized Dynamic Factor Model to derive a set of core inflation indicators that, combining national with area-wide data, allow us to answer two related questions: whether country-specific data are actually relevant to the future path of area-wide inflation once the information contained in area-wide data has been exploited, and whether it is useful, in order to track ECB monetary policy decisions, to factor in national and not only area-wide statistics. In both cases, our findings suggest that, when area-wide information is properly taken into account, there is little to be gained by considering national idiosyncratic developments.Forecast, Dynamic factor model, inflation, monetary policy

    Variety Management in Assemble-to-Order Supply Chains

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    Assemble-to-order refers to a supply chain strategy in which products are not assembled until customer order arrives. It is based on the so-called form postponement that is to hold components at a generic form and to delay the point of product differentiation. The performance of an assem-ble-to-order supply chain depends on two main dimensions, which are responsiveness and achievement level of scale economies. Responsiveness refers to the capability of fulfilling customer requirements in a fast-paced manner, whereas the achievement of scale economies reflects the degree of operations efficiency. Assemble-to-order supply chains induce high product variety, which has adverse effects on performance. We use demand volumes as a proxy for scale economies and lead times as a proxy for responsiveness. A matrix that consists of both dimensions can be defined, in which we distinguish between short/long lead times and low/high demand volumes. This matrix is called performance matrix. On the other hand, the consequence that results from product variety is a high demand variability of end products, which also affects the demand variability of components. An analysis of component demand variability enables one to identify the components with low/high demand variability. These components can further be classified into supplied and in-house made components. Thus, a second matrix (called component matrix) with two dimensions, namely variability (low/high) and supply source (in-house/supplier) can be defined. Due to the supply source dimension in the component matrix, the supply chain perspective is also taken into ac-count. The combination of both matrixes into a single one provides the performance/component matrix for assemble-to-order supply chains. To use the final matrix, it is necessary to compute lead times, demand volumes and demand variability of the supplied and in-house made components. By plotting the components in the matrix, one can determine the problems induced by variety. In order to improve the performance of the assemble-to-order supply chain, the implementation of variety management strategies is necessary. The identified strategies are: commonality, component families, modularity, and platforms. Based on the performance/component matrix, we discuss how these strategies or a combination of them can contribute to derive recommendations that aim to alleviate variety impacts on the as-semble-to-order supply chain.Assemble-to-order; Supply Chain Management; Variety Management
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