Material planning with uncertain product quality†

Traditional material requirement planning (MRP) is a system procedure to determine input quantities and ordering times to meet a specified demand in a deterministic framework. A variety of modifications to MRP have been proposed to incorporate different forms of uncertainty in production systems. In this paper, wc consider the case where the uncertainty is due to quality variations in the production process, and we develop an approach which links material planning with quality variations and control.     

Frugal innovation for supply chain sustainability in SMEs: multi-method research design

Abstract

In this study, we attempt to establish the missing links between supply chain sustainability and frugal innovation. Our study motivations stem from two facets of the emerging markets: first, the institutional barriers and, second, the resource constraints. We argue that there is a synergy in the concepts of frugal innovation and sustainability in supply chains and there is a need to further explore this synergy. Furthermore, we claim that even in the wake of many success stories in the frugal innovative supply chain management practices from emerging markets such as India, there are very few, if any, attempts made to understand the implications of a sustainability oriented frugal innovations in the particular context. To address this gap, we develop a model to establish the linkage between sustainable supply chains and frugal innovations. Our proposed conceptual framework depicts the hierarchy and interlinks of the identified enablers in developing sustainability oriented frugal innovative capabilities in supply chains. Furthermore, we have empirically validated our theoretical framework using survey data. We observed that most of the interpretive links are supported. These findings extend the understanding of frugal innovation for supply chain sustainability using multi-method research design, while also providing theoretically guidance to managers in the development of frugal innovation capability to achieve sustainability in supply chain in resource constrained environment.     

Meta-analysis using multilevel models with an application to the study of class size effects

Meta-analysis is formulated as a special case of a multilevel (hierarchical data) model in which the highest level is that of the study and the lowest level that of an observation on an individual respondent. Studies can be combined within a single model where the responses occur at different levels of the data hierarchy and efficient estimates are obtained. An example is given from studies of class sizes and achievement in schools, where study data are available at the aggregate level in terms of overall mean values for classes of different sizes, and also at the student level.     

A New Approach to Hazardous Materials Transportation Risk Analysis: Decision Modeling to Identify Critical Variables

We take a novel approach to analyzing hazardous materials transportation risk in this research. Previous studies analyzed this risk from an operations research (OR) or quantitative risk assessment (QRA) perspective by minimizing or calculating risk along a transport route. Further, even though the majority of incidents occur when containers are unloaded, the research has not focused on transportation-related activities, including container loading and unloading. In this work, we developed a decision model of a hazardous materials release during unloading using actual data and an exploratory data modeling approach. Previous studies have had a theoretical perspective in terms of identifying and advancing the key variables related to this risk, and there has not been a focus on probability and statistics-based approaches for doing this. Our decision model empirically identifies the critical variables using an exploratory methodology for a large, highly categorical database involving latent class analysis (LCA), loglinear modeling, and Bayesian networking. Our model identified the most influential variables and countermeasures for two consequences of a hazmat incident, dollar loss and release quantity , and is one of the first models to do this. The most influential variables were found to be related to the failure of the container. In addition to analyzing hazmat risk, our methodology can be used to develop data-driven models for strategic decision making in other domains involving risk.     

An integrated functional representation of concurrent engineering

A time-based competitive strategy is a driving force for enterprises that are striving to be competitive on a global basis. One of the methods to help in the time compression of the product development life cycle is concurrent engineering. Concurrent engineering is not only an approach that is used by the engineering function of the organization, but it is also one that crosses organizational boundaries. In this article an integrated functional model of the concurrent engineering process and its relationship with the product development life cycle is presented. A functional comparison to the traditional engineering approach for product development is also provided.     

A GENEALOGY OF CORRESPONDENCE ANALYSIS

Over the past half a century correspondence analysis has grown from a little known statistical technique designed to graphically depict the association structure of categorical variables that form a contingency table to a very popular tool used in a wide variety of disciplines. Despite this growth, correspondence analysis remains relatively unknown in some parts of the world, including the Australasian statistical community. This paper provides a non‐technical, bibliographic exploration of correspondence analysis. We take a step back to view the development of this statistical technique and provide a brief account of its genealogy with a selection of over 270 key publications that have contributed to its growth. We also look at its maturity over the decades.     

Viewpoint: model selection uncertainty,pre‐specification,and model averaging

Scientific progress in all empirical sciences relies on selecting models and performing inferences from selected models. Standard statistical properties (e.g., repeated sampling coverage probability of confidence intervals) cannot be guaranteed after a model selection. This viewpoint reviews this dilemma, puts the role that pre‐specification can play into perspective and illustrates model averaging as a way to relax the problem of model selection uncertainty. Copyright © 2015 John Wiley & Sons, Ltd.     

Assessing the bias due to non-coverage of residential movers in the German Microcensus Panel: an evaluation using data from the Socio-Economic Panel

The German Microcensus (MC) is a large scale rotating panel survey over three years. The MC is attractive for longitudinal analysis over the entire participation duration because of the mandatory participation and the very high case numbers (about 200000 respondents). However, as a consequence of the area sampling that is used for the MC, residential mobility is not covered and consequently statistical information at the new residence is lacking in the MC sample. This raises the question whether longitudinal analyses, like transitions between labour market states, are biased and how different methods perform that promise to reduce such a bias. Similar problems occur also for other national Labour Force Surveys (LFS) which are rotating panels and do not cover residential mobility, see Clarke and Tate (2002). Based on data of the German Socio-Economic Panel (SOEP), which covers residential mobility, we analysed the effects of missing data of residential movers by the estimation of labour force flows. By comparing the results from the complete SOEP sample and the results from the SOEP, restricted to the non-movers, we concluded that the non-coverage of the residential movers can not be ignored in Rubin’s sense. With respect to correction methods we analysed weighting by inverse mobility scores and log-linear models for partially observed contingency tables. Our results indicate that weighting by inverse mobility scores reduces the bias to about 60% whereas the official longitudinal weights obtained by calibration result in a bias reduction of about 80%. The estimation of log-linear models for non-ignorable non-response leads to very unstable results.     

Analysis of longitudinal data with drop-out: objectives, assumptions and a proposal

Summary. The problem of analysing longitudinal data that are complicated by possibly informative drop-out has received considerable attention in the statistical literature. Most researchers have concentrated on either methodology or application, but we begin this paper by arguing that more attention could be given to study objectives and to the relevant targets for inference. Next we summarize a variety of approaches that have been suggested for dealing with drop-out. A long-standing concern in this subject area is that all methods require untestable assumptions. We discuss circumstances in which we are willing to make such assumptions and we propose a new and computationally efficient modelling and analysis procedure for these situations. We assume a dynamic linear model for the expected increments of a constructed variable, under which subject-specific random effects follow a martingale process in the absence of drop-out. Informal diagnostic procedures to assess the tenability of the assumption are proposed. The paper is completed by simulations and a comparison of our method and several alternatives in the analysis of data from a trial into the treatment of schizophrenia, in which approximately 50% of recruited subjects dropped out before the final scheduled measurement time.