Exploring the financial consequences of the servitization of manufacturing

Commentators suggest that to survive in developed economies manufacturing firms have to move up the value chain, innovating and creating ever more sophisticated products and services, so they do not have to compete on the basis of cost. While this strategy is proving increasingly popular with policy makers and academics there is limited empirical evidence to explore the extent to which it is being adopted in practice. And if so, what the impact of this servitization of manufacturing might be. This paper seeks to fill a gap in the literature by presenting empirical evidence on the range and extent of servitization. Data are drawn from the OSIRIS database on 10,028 firms incorporated in 25 different countries. The paper presents an analysis of these data which suggests that: [i] manufacturing firms in developed economies are adopting a range of servitization strategies-12 separate approaches to servitization are identified; [ii] these 12 categories can be used to extend the traditional three options for servitization-product oriented Product-Service Systems, use oriented Product-Service Systems and result oriented Product-Service Systems, by adding two new categories "integration oriented Product-Service Systems" and "service oriented Product-Service Systems"; [iii] while the manufacturing firms that have servitized are larger than traditional manufacturing firms in terms of sales revenues, at the aggregate level they also generate lower profits as a % of sales; [iv] these findings are moderated by firm size (measured in terms of numbers of employees). In smaller firms servitization appears to pay off while in larger firms it proves more problematic; and [v] there are some hidden risks associated with servitization-the sample contains a greater proportion of bankrupt servitized firms than would be expected. © Springer Science + Business Media, LLC 2009.

Random function priors for exchangeable arrays with applications to graphs and relational data

A fundamental problem in the analysis of structured relational data like graphs, networks, databases, and matrices is to extract a summary of the common structure underlying relations between individual entities. Relational data are typically encoded in the form of arrays; invariance to the ordering of rows and columns corresponds to exchangeable arrays. Results in probability theory due to Aldous, Hoover and Kallenberg show that exchangeable arrays can be represented in terms of a random measurable function which constitutes the natural model parameter in a Bayesian model. We obtain a flexible yet simple Bayesian nonparametric model by placing a Gaussian process prior on the parameter function. Efficient inference utilises elliptical slice sampling combined with a random sparse approximation to the Gaussian process. We demonstrate applications of the model to network data and clarify its relation to models in the literature, several of which emerge as special cases.

Dynamic Covariance Models for Multivariate Financial Time Series

The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To address these problems we introduce a novel dynamic model for time-changing covariances. Over-fitting and local optima are avoided by following a Bayesian approach instead of computing point estimates. Changes in market conditions are captured by assuming a diffusion process in parameter values, and finally computationally efficient and scalable inference is performed using particle filters. Experiments with financial data show excellent performance of the proposed method with respect to current standard models.

Protective immunity induced by CpG ODNs against white spot syndrome virus (WSSV) via intermediation of virus replication indirectly in Litopenaeus vannamei

The worldwide shrimp culture is beset with diseases mainly caused by white spot syndrome virus (WSSV) and suffered huge economic losses, which bring out an urgent need to develop the novel strategies to better protect shrimps against WSSV. In the present study, CpG-rich plasmid pUC57-CpG, plasmid pUC57 and PBS were employed to pretreat shrimps comparatively to evaluate the protective effects of CpG ODNs on shrimps against WSSV. The survival rates, WSSV copy numbers, and antiviral associated factors (Dicer, Argonaute, STAT and ROS) were detected in Litopenaeus vannamei. There were higher survival proportion, lower WSSV copy numbers, and higher mRNA expression of Dicer and STAT in pUC57-CpG-pretreatment shrimps than those in pUC57- and PBS-pretreatment shrimps after WSSV infection. The Argonaute mRNA expression in pUC57-CpG-, pUC57- and PBS-pretreatment shrimps after WSSV infection was significantly higher than that of shrimps post PBS stimulation on the first day. The ROS levels in pUC57-CpG-pretreatment shrimps post secondary stimulation of PBS were significantly higher than those post WSSV infection on the first day. These results together demonstrated that pUC57-CpG induced partial protective immunity in shrimps against WSSV via intermediation of virus replication indirectly and could be used as a potential candidate in the development of therapeutic agents for disease control of WSSV in L. vannamei. (C) 2009 Elsevier Ltd. All rights reserved.

Grey relational analysis on the relation between marine environmental factors and oxidation-reduction potential

The effects of marine environmental factors-temperature (T), dissolved oxygen (DO), salinity (S) and pH-on the oxidation-reduction potential (ORP) of natural seawater were studied in laboratory. The results show an indistinct relationship between these four factors and the ORP, but they did impact the ORP Common mathematical methods were not applicable for describing the relationship. Therefore, a grey relational analysis (GRA) method was developed. The degrees of correlation were calculated according to GRA and the values of T, pH, DO and S were 0.744, 0.710, 0.692 and 0.690, respectively. From these values, the relations of these factors to the ORP could be described and evaluated, and those of T and pH were relatively major. In general, ORP is influenced by the synergic effect of T, DO, pH and S, with no single factor having an outstanding role.

A Radial Basis Function Approach to Financial Time Series Analysis

Nonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of practical techniques to address important estimation and confidence issues for Radial Basis Function networks arising from such a data driven approach, including efficient methods for parameter estimation and pruning, a pointwise prediction error estimator, and a methodology for controlling the "data mining'' problem. Novel applications in the finance area are described, including customized, adaptive option pricing and stock price prediction.

The impact of interconnectedness in a triadic relationship between a financial services provider, IFAs, and customers.

The aim of this study is to investigate the impact of interconnectedness between a long-term savings and investments provider, Independent Financial Advisers (IFAs) and customers. Ritter’s (2000) framework of the effect of interconnectedness was used to analyse this triadic relationship. Conceptual studies of triadic business relationships are scarce in marketing and organisational research (Blakenburg & Johanson, 1992; Havila, Johnson & Thilenius, 2004; Ritter, 2000). However, the applicability of a triadic relationship has been tested in a number of case studies (Andersson & Mattsson, 2004; Cunningham & Pyatt, 1989; Jaaskelainen, Kuivalainen & Saarenketo, 2000; Narayandas, 2002; Odorici & Corrado, 2004; Pardo & Salle, 1994; Trimachi, 2002). This study was conducted in collaboration with one of the UK’s largest long-term savings and investments providers. A substantial proportion of the provider’s business is conducted through IFAs and thus their significance as a major stakeholder. Indeed, the majority of sales in the long-term savings and investments industry in the UK are realised through IFAs. Academic studies (Gough, 2005; Gough & Nurullah, 2009) have indicated that IFAs are the strongest distribution channel in the industry. Thus, by analysing the impact of the interconnectedness in this relationship, a strategy that can increase the relationship performance can be proposed. However, to the best of the authors’ knowledge, a study that investigates the effect of the interconnectedness in this triadic relationship has not been established. In addition, the regulatory environment which continues to face change such as the recent implementation of Retail Distribution Review (RDR) on 1st January 2013 will make the relationship more rather than less complex.

Scalable Multi-Relational Association Mining

Clare, A., Williams, H. E. and Lester, N. M. (2004) Scalable Multi-Relational Association Mining. In proceedings of the 4th International Conference on Data Mining ICDM '04.

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.