Relationships among JIT practices: an interpretive modeling approach

Abstract

This research develops a model of relationships among components of Total-JIT, including JIT-information, JIT-manufacturing, JIT-purchasing, and JIT-selling, to establish an implementation hierarchy based on relative importance. The data collected relates to the relationships among JIT components and two performance measures, supply chain competency and organizational performance. Two groups are used in the research, one group of five operations management academics and another group of 30 practicing operations managers working in U.S. manufacturing firms. An interpretive structural modelling methodology is used to develop alternative structural models. The academics’ data show JIT-information emerging as lynchpin of relationships, directly impacting all other JIT practices and both performance measures. The practitioners’ data indicates that all JIT practices and performance measures are interactive as components and outcomes. This study is the first to apply interpretive structural modelling to investigate the interplay among total-JIT components and the performance measures of supply chain competency and organizational performance.     

舆论领袖传播影响力的阶段式建模分析

在网络社会,无论是网络推手炒作谣言,还是官方微博积极应对,都可视作舆论领袖在舆情演化过程中发挥传播影响力。本文将舆情演化过程分为两个阶段,在两个阶段分别应用不同的仿真模型对不同作用舆论领袖的传播影响力进行分析。第一个阶段是舆情危机爆发阶段,即舆情危机"从无到有",分析网络推手在该阶段的扩散影响力,以SIR经典传染病模型为基础,构建包含有网络推手作用的扩散影响力模型;第二个阶段是舆情危机平息阶段,即舆情危机"从有到无",分析官方微博在该阶段的证伪影响力,以Lotka-Volterra竞争关系模型为基础,研究官方微博如何发挥证伪影响力与网络推手进行博弈。结合具体舆情实例对阶段式模型进行验证分析,并提出如何根据舆论领袖不同传播作用应对舆情危机相关政策建议,以期帮助决策者打击网络谣言、平息舆情危机。     

Inference in Semi‐Parametric Dynamic Models for Repeated Count Data

This paper deals with a longitudinal semi‐parametric regression model in a generalised linear model setup for repeated count data collected from a large number of independent individuals. To accommodate the longitudinal correlations, we consider a dynamic model for repeated counts which has decaying auto‐correlations as the time lag increases between the repeated responses. The semi‐parametric regression function involved in the model contains a specified regression function in some suitable time‐dependent covariates and a non‐parametric function in some other time‐dependent covariates. As far as the inference is concerned, because the non‐parametric function is of secondary interest, we estimate this function consistently using the independence assumption‐based well‐known quasi‐likelihood approach. Next, the proposed longitudinal correlation structure and the estimate of the non‐parametric function are used to develop a semi‐parametric generalised quasi‐likelihood approach for consistent and efficient estimation of the regression effects in the parametric regression function. The finite sample performance of the proposed estimation approach is examined through an intensive simulation study based on both large and small samples. Both balanced and unbalanced cluster sizes are incorporated in the simulation study. The asymptotic performances of the estimators are given. The estimation methodology is illustrated by reanalysing the well‐known health care utilisation data consisting of counts of yearly visits to a physician by 180 individuals for four years and several important primary and secondary covariates.     

一基于SEM的经济发展与社会发展关系——以济南市为例

利用济南市2001—2013年的数据,构建济南市经济与社会发展关系的结构方程模型(SEM),考量经济发展的4个潜变量(经济水平、经济结构、经济增速、经济物耗)与社会发展的4个潜变量(居民的生活质量、社会稳定、人口素质、生态环境)间的相互影响,结果表明,居民生活质量正相关于人口素质和经济水平,人口素质正相关于经济水平;居民生活质量作用于社会稳定,而社会稳定是生活质量的固有要求;生态环境负相关于经济增速和经济物耗。     

An Automatic Test for the Umbrella Alternatives

The paper proposes a new test for detecting the umbrella pattern under a general non‐parametric scheme. The alternative asserts that the umbrella ordering holds while the hypothesis is its complement. The main focus is put on controlling the power function of the test outside the alternative. As a result, the asymptotic error of the first kind of the constructed solution is smaller than or equal to the fixed significance level α on the whole set where the umbrella ordering does not hold. Also, under finite sample sizes, this error is controlled to a satisfactory extent. A simulation study shows, among other things, that the new test improves upon the solution widely recommended in the literature of the subject. A routine, written in R, is attached as the Supporting Information file.     

Influence diagnostics for the Weibull-Negative-Binomial regression model with cure rate under latent failure causes

In this paper, we propose a flexible cure rate survival model by assuming that the number of competing causes of the event of interest follows the Negative Binomial distribution and the time to event follows a Weibull distribution. Indeed, we introduce the Weibull-Negative-Binomial (WNB) distribution, which can be used in order to model survival data when the hazard rate function is increasing, decreasing and some non-monotonous shaped. Another advantage of the proposed model is that it has some distributions commonly used in lifetime analysis as particular cases. Moreover, the proposed model includes as special cases some of the well-know cure rate models discussed in the literature. We consider a frequentist analysis for parameter estimation of a WNB model with cure rate. Then, we derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and present some ways to perform global influence analysis. Finally, the methodology is illustrated on a medical data.     

Functional Partial Linear Single‐index Model

This paper deals with the problem of predicting the real‐valued response variable using explanatory variables containing both multivariate random variable and random curve. The proposed functional partial linear single‐index model treats the multivariate random variable as linear part and the random curve as functional single‐index part, respectively. To estimate the non‐parametric link function, the functional single‐index and the parameters in the linear part, a two‐stage estimation procedure is proposed. Compared with existing semi‐parametric methods, the proposed approach requires no initial estimation and iteration. Asymptotical properties are established for both the parameters in the linear part and the functional single‐index. The convergence rate for the non‐parametric link function is also given. In addition, asymptotical normality of the error variance is obtained that facilitates the construction of confidence region and hypothesis testing for the unknown parameter. Numerical experiments including simulation studies and a real‐data analysis are conducted to evaluate the empirical performance of the proposed method.     

曲线分类建模方法及其在GDP预测中的应用

传统方法解决大规模时序曲线的预测建模问题,需要对每条曲线逐一建模,使得建模工作量相当庞大,在实际应用中缺乏可操作性。文章提出一种解决此问题的新方法——曲线分类建模方法。该方法先减少曲线的模型种类,再进行曲线分类和分类建模,在尽可能保留原始信息的前提下较大程度地降低了建模的工作量。文章阐述了该方法的原理和计算过程,并以应用于多地区GDP曲线的预测案例说明该方法的实用性和有效性。     

Migration Decisions of Dual-earner Families: An Application of Multilevel Modeling

A comprehensive framework for guiding analyses of internal migration is lacking. This study contributes to the family migration literature in three important ways. We develop a multilevel theoretical framework emphasizing an integration of individual-, family-, and neighborhood-level effects; introduce multilevel statistical modeling; and explicitly assess how effects of economic-based explanatory variables vary by gender. Our data are from the Panel Study of Income Dynamics (PSID). We find that the likelihood of a family migrating is affected by economic and non-economic factors, some of which vary by gender. We add to the dual-earner migration literature by finding that wives are not likely to be tied-movers, but husbands are likely to be tied-stayers. Neighborhood factors also are important to the decision to migrate.

Construction Safety Risk Modeling and Simulation

By building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.