A comparative study on production ramp-up: state-of-the-art and new challenges

In the context of globalisation, the companies are urged to innovate the products and the services they provide to their customers, in order to keep their competitive edge. New product introduction in an already existing production environment generates numerous perturbations to handle in the supply chain and operations management during the production ramp-up. The purpose of this article is to present a thorough state of the art on the production ramp-up. Different overviews of the research body are given: comparing definitions, characteristics and problems of the ramp-up phase, classifying research papers according to their industrial context and organising them in different classifications (by keywords, focus extent …). Interesting issues that could be addressed in future research about the production ramp-up phase are identified.     

Set Identified Linear Models

We analyze the identification and estimation of parameters β satisfying the incomplete linear moment restrictions E(z^⊤(xβ−y)) = E(z^⊤u(z)), where z is a set of instruments and u(z) an unknown bounded scalar function. We first provide empirically relevant examples of such a setup. Second, we show that these conditions set identify β where the identified set B is bounded and convex. We provide a sharp characterization of the identified set not only when the number of moment conditions is equal to the number of parameters of interest, but also in the case in which the number of conditions is strictly larger than the number of parameters. We derive a necessary and sufficient condition of the validity of supernumerary restrictions which generalizes the familiar Sargan condition. Third, we provide new results on the asymptotics of analog estimates constructed from the identification results. When B is a strictly convex set, we also construct a test of the null hypothesis, β₀∈B, whose size is asymptotically correct and which relies on the minimization of the support function of the set B− {β₀}. Results of some Monte Carlo experiments are presented.     

Hierarchical Bayesian models to assess between- and within-batch variability of pathogen contamination in food

Assessing within-batch and between-batch variability is of major interest for risk assessors and risk managers in the context of microbiological contamination of food. For example, the ratio between the within-batch variability and the between-batch variability has a large impact on the results of a sampling plan. Here, we designed hierarchical Bayesian models to represent such variability. Compatible priors were built mathematically to obtain sound model comparisons. A numeric criterion is proposed to assess the contamination structure comparing the ability of the models to replicate grouped data at the batch level using a posterior predictive loss approach. Models were applied to two case studies: contamination by Listeria monocytogenes of pork breast used to produce diced bacon and contamination by the same microorganism on cold smoked salmon at the end of the process. In the first case study, a contamination structure clearly exists and is located at the batch level, that is, between batches variability is relatively strong, whereas in the second a structure also exists but is less marked.     

INVESTMENT CHOICE ARCHITECTURE IN TRUST GAMES: WHEN “ALL‐IN” IS NOT ENOUGH

While many economic interactions feature “All‐or‐Nothing” options nudging investors towards going “all‐in,” such designs may unintentionally affect reciprocity. We manipulate the investor's action space in two versions of the “trust game.” In one version investors can invest either “all” their endowment or “nothing.” In the other version, they can invest any amount of the endowment. Consistent with our intentions‐based model, we show that “all‐or‐nothing” designs coax more investment but limit investors' demonstrability of intended trust. As a result, “all‐in” investors are less generously reciprocated than when they can invest any amount, where full investments are a clearer signal of trustworthiness. (JEL C72, C90, C91, D63, D64, L51)     

Ethical issues in the translation of social neuroscience: a policy analysis of current guidelines for public dialogue in human research

Social neuroscience and its potential implications create an interesting case study for examining human research ethics policies on the topic of public communication of research. We reviewed mainstream national and international human research ethics guidelines and policies on issues of public communication of research. Our analysis relied on five thematic nets to capture the interactions between research and the public: public understanding, knowledge translation, public participation, social outcomes, and dual use. Coverage of these topics is sparse and inconsistent in mainstream policies and guidelines. We identify three options to address these gaps and analyze their strengths and weaknesses.     

Public Organizational Birth and Death: Understanding the Exigencies of an African Political Environment

This research examines the application of theories of organizational birth and death in transitional and undemocratic political settings. Through the case study of the birth and death of the Ministry of Supplies and Marketing in Kenya, the author determines that theoretical explanations of organizational formation and demise do not necessarily fit a uniform profile. Under unstable and undemocratic environments, public organizations that are brought to life through decrees may also be unexpectedly vanished without following a logical and predictable cyclical sequence.

Further Evidence on the Great Crash,the Oil-Price Shock,and the Unit-Root Hypothesis

Recently, Perron has carried out tests of the unit-root hypothesis against the alternative hypothesis of trend stationarity with a break in the trend occurring at the Great Crash of 1929 or at the 1973 oil-price shock. His analysis covers the Nelson–Plosser macroeconomic data series as well as a postwar quarterly real gross national product (GNP) series. His tests reject the unit-root null hypothesis for most of the series. This article takes issue with the assumption used by Perron that the Great Crash and the oil-price shock can be treated as exogenous events. A variation of Perron's test is considered in which the breakpoint is estimated rather than fixed. We argue that this test is more appropriate than Perron's because it circumvents the problem of data-mining. The asymptotic distribution of the estimated breakpoint test statistic is determined. The data series considered by Perron are reanalyzed using this test statistic. The empirical results make use of the asymptotics developed for the test statistic as well as extensive finite-sample corrections obtained by simulation. The effect on the empirical results of fat-tailed and temporally dependent innovations is investigated, in brief, by treating the breakpoint as endogenous, we find that there is less evidence against the unit-root hypothesis than Perron finds for many of the data series but stronger evidence against it for several of the series, including the Nelson-Plosser industrial-production, nominal-GNP, and real-GNP series.     

Periodic Autoregressive Conditional Heteroscedasticity

Most high-frequency asset returns exhibit seasonal volatility patterns. This article proposes a new class of models featuring periodicity in conditional heteroscedasticity explicitly designed to capture the repetitive seasonal time variation in the second-order moments. This new class of periodic autoregressive conditional heteroscedasticity, or P-ARCH, models is directly related to the class of periodic autoregressive moving average (ARMA) models for the mean. The implicit relation between periodic generalized ARCH (P-GARCH) structures and time-invariant seasonal weak GARCH processes documents how neglected autoregressive conditional heteroscedastic periodicity may give rise to a loss in forecast efficiency. The importance and magnitude of this informational loss are quantified for a variety of loss functions through the use of Monte Carlo simulation methods. Two empirical examples with daily bilateral Deutschemark/British pound and intraday Deutschemark/U.S. dollar spot exchange rates highlight the practical relevance of the new P-GARCH class of models. Extensions to discrete-time periodic representations of stochastic volatility models subject to time deformation are briefly discussed.