Supply chain operation strategies and risk management with working capital consideration: a case study of the supply chain of lightning protection products in China

JEL: G32; D21With the advent of economic globalization, competition is increasingly hinged on supply chain. Meanwhile, working capital becomes a key element of a successful supply chain. This thesis researches the supply chain of a typical lightning protection products manufacturer in China, i.e. Company Z. The thesis starts with the working capital issues in the supply chain of Company Z; then, with the help of questionnaires and a sensible indicator system and weight assignments; analyzes and summarizes the status quo of the working capital and related key issues in the supply chain consisting of Company Z and its suppliers and customers. Building on such analysis, a two-dimensional classification matrix is created to divide suppliers and customers into four groups (namely, strategic-type, partner-type, general-type, and bottleneck-type) and supply chain operation strategies are devised for each group. Furthermore, based on such supply chain operations strategies of Company Z, a working capital risk management mechanism with an early warning system is developed, and a supply chain-based financing platform is designed to help the supply chain participants seek financing and share the risks with working capital.Com o advento da era da globalização económica, a cadeia de suprimentos tornou-se cada vez mais importante para a concorrência empresarial, e ao mesmo tempo, o fundo de maneio tornou-se num elemento chave para o sucesso da gestão da cadeia de suprimentos. Neste trabalho, a cadeia de suprimentos de uma empresa chinesa de fabricação de produtos típicos de proteção contra relâmpagos, a empresa Z, é o objeto de estudo. Tomando como ponto de partida os problemas de fundo de maneio existentes na cadeia de suprimentos da empresa Z, por meio de questionários combinados com o estabelecimento de um sistema de indexação e de ponderação, foram realizadas análises precisas sobre problemas-chaves existentes e da situação atual da gestão do fundo de maneio da cadeia de suprimentos a montante e a jusante da empresa Z. Estabeleceram-se matrizes bidimensionais de classificação para respectivamente subdividir os fornecedores e clientes em quatro categorias, a saber, categoria de fornecedores/clientes estratégicos, categoria de fornecedores/clientes parceiros, categoria de fornecedores/clientes comuns e categoria de fornecedores/clientes críticos (“engarrafamentos”) e propor estratégias diferentes na cadeia de suprimentos para diferentes categorias. Por fim, o nosso estudo indica que segundo a estratégia de operação da cadeia de suprimentos da empresa Z, deve ser estabelecido um mecanismo de controle e gestão de risco de fundo de maneio, um sistema de alerta de risco e, ainda, projetar uma plataforma de financiamento a fim de prover o financiamento emergente da cadeia de suprimentos da empresa Z e a partilha dos riscos de gestão do fundo de maneio

Application of Frequency-dependent Traveltime Tomography and Full Waveform Inversion to Realistic Near-surface Seismic Refraction Data

We present a synthetic test that uses a workflow consisting of a new frequency-dependent traveltime tomography (FDTT) method to provide a starting model for full waveform inversion (FWI) for near-surface seismic velocity estimation from refraction data. Commonly used ray-theory-based traveltime tomography methods may not be valid in the near surface given the likelihood of relatively large seismic wavelengths compared to the length scales of heterogeneities that are possible in the near surface. FDTT makes use of the frequency content in the seismic waves in both the forward and inverse modeling steps. In this application to a near-surface benchmark model, the results show that FDTT can better recover the magnitude of velocity anomalies than infinite frequency (ray-theory) traveltime tomography (IFTT). FWI can fail by converging to a local minimum when there is an absence of sufficiently low frequency data and an accurate starting model, either of which, if present, can provide long-wavelength constraints on the inverted velocity model. Both IFTT and FDTT models can serve as adequate starting models for FWI. However, FWI produces significantly better results starting from the FDTT model as compared to the IFTT model when low frequency data are not available. The final FWI models provide wavelength-scale structures allowing for direct geologic interpretation from the velocity model itself, demonstrating the effectiveness of FDTT and FWI in near-surface studies given the modest experiment and data requirements of refraction surveys

Adaptive Multi-Feature Budgeted Profit Maximization in Social Networks

Online social network has been one of the most important platforms for viral marketing. Most of existing researches about diffusion of adoptions of new products on networks are about one diffusion. That is, only one piece of information about the product is spread on the network. However, in fact, one product may have multiple features and the information about different features may spread independently in social network. When a user would like to purchase the product, he would consider all of the features of the product comprehensively not just consider one. Based on this, we propose a novel problem, multi-feature budgeted profit maximization (MBPM) problem, which first considers budgeted profit maximization under multiple features propagation of one product. Given a social network with each node having an activation cost and a profit, MBPM problem seeks for a seed set with expected cost no more than the budget to make the total expected profit as large as possible. We consider MBPM problem under the adaptive setting, where seeds are chosen iteratively and next seed is selected according to current diffusion results. We study adaptive MBPM problem under two models, oracle model and noise model. The oracle model assumes conditional expected marginal profit of any node could be obtained in O(1) time and a (1-1/e) expected approximation policy is proposed. Under the noise model, we estimate conditional expected marginal profit of a node by modifying the EPIC algorithm and propose an efficient policy, which could return a (1-exp({\epsilon}-1)) expected approximation ratio. Several experiments are conducted on six realistic datasets to compare our proposed policies with their corresponding non-adaptive algorithms and some heuristic adaptive policies. Experimental results show efficiencies and superiorities of our policies.Comment: 12 pages, 6 figure