Implementation of just-in-time methodology in a small company

This paper describes the implementation of JIT in a small manufacturing company and the benefits that resulted for the company's operations. Preliminary analysis identified various problems in the existing manufacturing operations. The pre-implementation and post-implementation conditions of the company arc detailed. The achievements of the JIT implementation included a reduction in material traversing, reduced lead limes and inventories leading to an overall reduction in the cost of manufacturing. A smooth flow of material from the raw material stage to the finished product stage was established. Three separate product lines were combined into a flexible manufacturing assembly line. With the adoption of a holistic approach to JIT implementation, it was found that even a small company can make significant strides towards world class manufacturing status. The experience gained by the company can encourage and benefit other small companies to embrace the JIT approach.     

政府采购与经济增长的实证研究——基于ARMA最优模型

本文采用基于ARMA模型适用于小样本的最优预测的建模方法,来分析政府采购和经济增长之间的因果关系。研究结果表明政府采购和经济增长之间存在非对称、单向因果关系,即政府采购对经济增长的影响不显著,而经济增长对政府采购具有促进作用。因此,从政府采购影响路径的角度分析认为,在后金融危机时代应该采取一些可行的措施扭转政府采购对经济增长的不显著状况。     

Exploring Parameter Relations for Multi‐Stage Models in Stage‐Wise Constant and Time Dependent Hazard Rates

Single cohort stage‐frequency data are considered when assessing the stage reached by individuals through destructive sampling. For this type of data, when all hazard rates are assumed constant and equal, Laplace transform methods have been applied in the past to estimate the parameters in each stage‐duration distribution and the overall hazard rates. If hazard rates are not all equal, estimating stage‐duration parameters using Laplace transform methods becomes complex. In this paper, two new models are proposed to estimate stage‐dependent maturation parameters using Laplace transform methods where non‐trivial hazard rates apply. The first model encompasses hazard rates that are constant within each stage but vary between stages. The second model encompasses time‐dependent hazard rates within stages. Moreover, this paper introduces a method for estimating the hazard rate in each stage for the stage‐wise constant hazard rates model. This work presents methods that could be used in specific types of laboratory studies, but the main motivation is to explore the relationships between stage maturation parameters that, in future work, could be exploited in applying Bayesian approaches. The application of the methodology in each model is evaluated using simulated data in order to illustrate the structure of these models.     

西南山区小城市城镇文化建设SWOT分析——以重庆市城口县为例

文化建设带动区域发展对西南山区小城镇具有一定参考,是实现新形势下的特色城镇化发展的有益尝试。文章选择重庆市城口县为研究对象,运用SWOT研究方法,系统分析城口县在建设城镇文化进程中的优势、局限、机遇和挑战,归纳西南山区小城镇发展城镇文化的内外部环境,研究结果显示,城口县发展城镇文化总体上具有资源丰富的优势和空间受限等局限,国内政策、地方发展机遇等略大于区域竞争的挑战,且个别挑战和局限可以转化为发展机遇和优势。因此,以文化建设带动西南山区小城镇新型城镇化发展的思路具有一定可行性,但要结合小城市当地实际,打造有特色的文化空间格局和构建地方文化体系。     

新型农业经营主体嵌入式培育及其带动作用——以石城为例

发挥新型农业经营主体对小农户的带动作用,是促进小农户与现代农业发展有机衔接的关键。江西石城县着眼于“培养农村致富带头人,打造一支不走的扶贫工作队”,立足县域脱贫与发展需要实施了“千人铸造计划”,该计划发挥县级行政动员优势并扎根乡土社会,从培育方案、产业选择、社会基础、组织设置与政策支持等多方面着手,推动了新型农业经营主体的嵌入式培育及其内生带动作用激发,形成了新型农业经营主体带动小农户发展的实践范例。石城经验体现了我国县域政府独特的行政动员机制及其嵌入乡土社会的本土创新,具有重要的推广价值,可为乡村振兴战略下培育扎根乡土致富带头人及实现小农户与现代农业发展有机衔接提供有益启示。     

完善辽宁中小企业信用担保体系的思考

中小企业在辽宁社会经济发展中发挥着重要的作用,但融资难严重阻碍着中小企业的发展。要从建立中小企业信用担保机构、建立中小企业信用信息系统、为中小企业发展创造良好的外部环境等方面入手,建立和完善中小企业信用担保体系,促进中小企业的发展和老工业基地的振兴。     

小企业融资缺口分析及对策研究

小企业在发展中通常会遇到市场难以解决的融资缺口问题 ,即借贷资金缺口与资本缺口。由于小企业规模较小 ,融资成本相对较高、信用担保机制缺乏以及企业自身“存活性”差等条件约束 ,引致小企业在有组织金融市场上的局部市场失效 ,市场融资难以有效形成 ,从而产生小企业的融资缺口。针对小企业存在融资功能的缺陷 ,应强化弥补融资缺口的金融支持政策 ,从法律法规层面加大对小企业的支持力度 ;建立完善的信用担保体系 ,形成小企业融资担保机制 ;规范小企业金融机构 ,构筑小企业融资体系 ;开发资本市场 ,加大对小企业的金融支持     

Small‐sphere distributions for directional data with application to medical imaging

We propose novel parametric concentric multi‐unimodal small‐subsphere families of densities for p ? 1 ≥ 2‐dimensional spherical data. Their parameters describe a common axis for K small hypersubspheres, an array of K directional modes, one mode for each subsphere, and K pairs of concentrations parameters, each pair governing horizontal (within the subsphere) and vertical (orthogonal to the subsphere) concentrations. We introduce two kinds of distributions. In its one‐subsphere version, the first kind coincides with a special case of the Fisher–Bingham distribution, and the second kind is a novel adaption that models independent horizontal and vertical variations. In its multisubsphere version, the second kind allows for a correlation of horizontal variation over different subspheres. In medical imaging, the situation of p ? 1 = 2 occurs precisely in modeling the variation of a skeletally represented organ shape due to rotation, twisting, and bending. For both kinds, we provide new computationally feasible algorithms for simulation and estimation and propose several tests. To the best knowledge of the authors, our proposed models are the first to treat the variation of directional data along several concentric small hypersubspheres, concentrated near modes on each subsphere, let alone horizontal dependence. Using several simulations, we show that our methods are more powerful than a recent nonparametric method and ad hoc methods. Using data from medical imaging, we demonstrate the advantage of our method and infer on the dominating axis of rotation of the human knee joint at different walking phases.     

On comparing,classifying and clustering several dependent regression models

Classifying several regression models fitted on a dataset is one of the most problems in data analysis. In other words, scientists are interested in comparing several regression models that can be used for a dataset. In this paper, an approach will be used to compare and classify several dependent regression models. Then the performance of the proposed method is investigated using simulation study and real example.     

The effect of small sample on optimal designs for logistic regression models

Asymptotic methods are commonly used in statistical inference for unknown parameters in binary data models. These methods are based on large sample theory, a condition which may be in conflict with small sample size and hence leads to poor results in the optimal designs theory. In this paper, we apply the second order expansions of the maximum likelihood estimator and derive a matrix formula for the mean square error (MSE) to obtain more precise optimal designs based on the MSE. Numerical results indicate the new optimal designs are more efficient than the optimal designs based on the information matrix.