The demand forecast is the most influential input into enterprise activities planning thus creating a challenging issue for Demand Planning experts in model selection. Models including quantitative, qualitative and hybrid forecasting methods have been developed and are widely used. The literature reveals the use of several case-dependent error measures to evaluate forecasting accuracy, however, these performance indicators may at times differentiate in results making it more difficult in determining the most appropriate forecasting model for the users’ needs. This paper presents the development of two hybrid multi-criteria decision making approaches, AHP-TOPSIS and ANP-TOPSIS, to evaluate and rank the relative performances based on error measures of alternate forecasting models. Validation is provided through an industrial application using empirical data from a plastic bag manufacturer based on five models
We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, not being a priori limited to additive models. This is the first kernel-based variable selec- tion method applicable to large datasets. It sidesteps the typical poor scaling properties of kernel methods by mapping the inputs into a relatively low-dimensional space of random features. The algorithm discovers the variables relevant for the regression task together with learning the prediction model through learning the appropriate nonlinear random feature maps. We demonstrate the outstanding performance of our method on a set of large-scale synthetic and real datasets.
This paper studies the effects of estate recovery, estate taxation and long-term care (LTC) subsidies on the incentives both to buy LTC insurance and to transfer wealth by elderly parents and to provide informal care by children to their dependent elderly parents. We also investigate the effect of these three fiscal policies on public budgets allocated to finance LTC needs. Estate recovery is found to dominate the two other fiscal policies as it is more likely to incentivise both LTC insurance purchase and informal care supply while impacting positively government budget.
Based on qualitative research that was conducted on enterprises from the industrial sector, we acquired new knowledge on the use and the needs of management accounting and control practices. The focus of the study was small and middle industries based in the French-speaking part of Switzerland using an ERP (Enterprise Resources Planning) system. The research aims to highlight the best practices in management accounting and control, as well as underlining missing information and tools. The collected data will be used in further research to design a simplified framework of management accounting and controlling that will be useful in identifying and developing the necessary ERP system functionalities.
The current architecture for VANET related services relies on a Client-Server approach and leads to numerous drawbacks, such as network congestion due to the bottleneck problem or, more importantly, data privacy concerns. Indeed, because of the network topology, traffic must go through nodes which limit the bandwidth and thus bound the overall network capacity. Finally, user data is collected and stored in servers, used by third party services. However, these providers are known to treat lightly user privacy by selling or using the data for their own purposes (Beresford and Stajano, 2004). By use of a decentralized and distributed communication protocol (i.e. D2D), one can overcome these problems by spreading the communication burden to all nodes in the mesh. By means of cryptographic techniques, we can ensure that the shared data is secured and controlled at the end-user side. This paper presents a study and proposes a proof of concept of a decentralized and distributed information system by means of a dynamic navigation system for VANET, using a low-cost solution such as Wifi or LTE-direct new 3GPPP protocol. This system preserves user privacy and is augmented with forensics capabilities.
Intra-family moral hazard refers to the disincentive for informal caregivers to provide care to their dependent relatives in the presence of long-term care (LTC) insurance. This article uses crosssectional data from the Survey of Health, Ageing, and Retirement in Europe (SHARE) database to test the effect of both public and private LTC insurance on the reception of informal care in Italy and Spain. The results support the hypothesis of intra-family moral hazard for Spain in the case of public LTC insurance while they reject it for Italy. They confirm recent theoretical findings showing that fixed insurance benefits provide much less disincentives to offer informal care than proportional insurance benefits.
Dans cet article, les auteurs décrivent l’étude de la gestion du cycle de vie des données en cours en Suisse avec un accent sur la préservation à long terme. Basé sur une grande analyse de documents, ainsi que des entretiens semi-structurés, le projet décrit ci-dessous vise à fournir des services au niveau suisse pour les besoins les plus importants dans la gestion des données tout au long de leur cycle de vie, y compris un point d’accès et de contact, de formation et de conseil unique, des solutions de gestion des données actives, des options de stockage et de conservation à long terme, ainsi qu’une gouvernance pour tous les aspects. Mots-clés: Services de gestion de données de recherche, la préservation des données numériques, la gestion des données de cycle de vie inclusifs.
