Supply chain configurations in a global environment: A longitudinal perspective

In the last few years, companies have paid growing attention to the management of their supply chain at a global level. The need for better suppliers, international competition and research of specific competences have forced companies to improve their ability to cope with suppliers and customers located in different countries around the world. This paper aims to provide an overview of how manufacturing companies use global supply chains and how their behaviour changes over time. Longitudinal data from a sample of companies from the last two editions of the International Manufacturing Strategy Survey (IMSS) are used. A contingent analysis of manufacturing localization and globalization is also considered in order to identify factors influencing supply chain globalization strategies. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.

General closed-form basket option pricing bounds

This article presents lower and upper bounds on the prices of basket options for a general class of continuous-time financial models. The techniques we propose are applicable whenever the joint characteristic function of the vector of log-returns is known. Moreover, the basket value is not required to be positive. We test our new price approximations on different multivariate models, allowing for jumps and stochastic volatility. Numerical examples are discussed and benchmarked against Monte Carlo simulations. All bounds are general and do not require any additional assumption on the characteristic function, so our methods may be employed also to non-affine models. All bounds involve the computation of one-dimensional Fourier transforms; hence, they do not suffer from the curse of dimensionality and can be applied also to high-dimensional problems where most existing methods fail. In particular, we study two kinds of price approximations: an accurate lower bound based on an approximating set and a fast bounded approximation based on the arithmetic-geometric mean inequality. We also show how to improve Monte Carlo accuracy by using one of our bounds as a control variate.     

Relocation of second degree: Moving towards a new place or returning home?

We study the “Relocations of Second Degree” (RSDs), i.e., the location decisions that modify the country of destination of a previous offshoring investment. Specifically, we distinguish between two types of RSDs, i.e., “Relocation to the Home Country (RHC)”, also known as back-reshoring, and “Relocation to a Third Country (RTC)”, i.e., the choice to move to a second host country.Specifically, we explore how the location advantages underlying the previous offshoring decision affect the probability to undertake an RHC, rather than an RTC. Location advantages reflect the favourable conditions that a foreign country offers with respect to the home one, in terms of market-seeking, asset-seeking and efficiency-seeking (i.e., cost-saving and productivity-enhancing) opportunities. Using data from the European Restructuring Monitor, we focus on the RSDs regarding manufacturing activities, implemented across European countries between 2002 and 2015. We find that, on the one hand, when a previous offshoring investment is driven by market-seeking location advantage, firms undertaking the RSD are more likely to opt for an RHC, except during the economic crisis where market-seeking European firms seem to prefer RTCs. On the other hand, RTC is a preferred choice when the location advantage is of efficiency-seeking type. In addition to offering a broader characterization of RSDs, our study provides empirical evidence of the relationship between the offshoring and relocation decisions. Managers should be aware of this connection when designing their manufacturing internationalization strategies.     

Maximum likelihood estimation based on the Laplace approximation for p2 network regression models

The class of p₂ models is suitable for modeling binary relation data in social network analysis. A p₂ model is essentially a regression model for bivariate binary responses, featuring within‐dyad dependence and correlated crossed random effects to represent heterogeneity of actors. Despite some desirable properties, these models are used less frequently in empirical applications than other models for network data. A possible reason for this is due to the limited possibilities for this model for accounting for (and explicitly modeling) structural dependence beyond the dyad as can be done in exponential random graph models. Another motive, however, may lie in the computational difficulties existing to estimate such models by means of the methods proposed in the literature, such as joint maximization methods and Bayesian methods. The aim of this article is to investigate maximum likelihood estimation based on the Laplace approximation approach, that can be refined by importance sampling. Practical implementation of such methods can be performed in an efficient manner, and the article provides details on a software implementation using R . Numerical examples and simulation studies illustrate the methodology.     

Medication adherence and persistence in the treatment of rheumatoid arthritis with adalimumab and etanercept. Six years of analysis

Objective:

The aim of this study was to evaluate medication adherence and persistence of patients treated with Etanercept and Adalimumab for Rheumatoid Arthritis, also giving economic evaluations on therapy costs for Received Daily Dose (RDD).

Materials and methods:

This retrospective study took into account 6 years from January 1, 2007 to December 31, 2012. Medication adherence was quantified utilizing the ratio between RDD and Prescribed Daily Dose (PDD). Persistence has been reckoned taking into account the actual days of therapy comparing posology with supplied dose. The persistence has been graphed according to Kaplan-Meier method. The cost per RDD was reckoned starting from the expense incurred by Pescara General Hospital.

Results:

Medication adherence gave results in values between 0.88–0.97 for Etanercept and 0.83–0.90 for Adalimumab. The value of persistence was 100% for Etanercept and 90% for Adalimumab for the first year, and 70% for Etanercept and 80% for Adalimumab for the second year. In the 3rd year the persistence for Etanercept was 50% while for Adalimumab it was 60%. In the fourth year the persistence for Etanercept was 21% while for Adalimumab it was 27%. The statistical analysis was conducted using the Log rank test. The average cost per RDD was €32.97 for Etanercept and for Adalimumab it was €32.00 as an average of 6 years.

Conclusion:

The medication adherence was good for both Etanercept and Adalimumab. The rate of persistence decreased strictly in the fourth year of treatment. This data suggests the need for continuous monitoring of patients in treatment with TNF blockers.     

Decomposing seasonality in an urban destination: the case of Milan

ABSTRACT

This paper analyses demand fluctuations in the field of urban destinations, decomposing Milan’s hotel seasonality. Four different seasonal periods are identified and two research questions are explored. The first aims to verify how seasonality evolved over the period 2004–2015 and the effectiveness of analysing tourism flow in an urban destination using daily data. The second aim explores the effects generated by the Milan Expo 2015 on seasonality. Using Smith Travel Research data, a longitudinal analysis (2004–2015) is carried out, decomposing the annual Theil index. Evidence is reported and discussed.     

Modern Likelihood‐Frequentist Inference

We offer an exposition of modern higher order likelihood inference and introduce software to implement this in a quite general setting. The aim is to make more accessible an important development in statistical theory and practice. The software, implemented in an R package, requires only that the user provide code to compute the likelihood function and to specify extra‐likelihood aspects of the model, such as stopping rule or censoring model, through a function generating a dataset under the model. The exposition charts a narrow course through the developments, intending thereby to make these more widely accessible. It includes the likelihood ratio approximation to the distribution of the maximum likelihood estimator, that is the p^? formula, and the transformation of this yielding a second‐order approximation to the distribution of the signed likelihood ratio test statistic, based on a modified signed likelihood ratio statistic r^?. This follows developments of Barndorff‐Nielsen and others. The software utilises the approximation to required Jacobians as developed by Skovgaard, which is included in the exposition. Several examples of using the software are provided.     

What is the value of an IT e-procurement system?

E-procurement is gaining in popularity in business practice and its benefits encourage its adoption for a variety of areas, including IT (Information Technology) purchases. The problem with assessing the value associated with e-procurement has been addressed by researchers and practitioners, but a clear methodology to determine the benefits related to e-procurement adoption is still missing, especially for IT. This paper defines e-procurement and identifies the six most significant drivers for e-procurement adoption, which are the pillars of the proposed value assessment methodology. The authors have also applied the developed methodology to real cases in order to verify its validity and robustness. Finally, although the developed model takes into account the peculiarities of IT purchases, it also raises more research opportunities for other purchasing categories.     

Supply chain management in the luxury industry: A first classification of companies and their strategies

This paper aims at analysing the supply management strategies in the luxury industry in order to find out (1) whether different clusters of companies could be identified within the luxury industry, on the base of a set of contingent variables derived from previous research, and (2) which supply chain strategy is currently applied within each cluster. Case study methodology is used. A sample of 15 Italian luxury companies, belonging to different industrial sectors, was taken into account.On the basis of five classification variables (company size, selling volume, product complexity, product fashionableness and brand reputation) four clusters are identified; these clusters present differences in terms of Supply Chain strategy, as well as in terms of practices for managing manufacturing, sourcing and distribution processes.