Last Time Buy and Control Policies With Phase-Out Returns: A Case Study in Plant Control Systems

This research involves the combination of spare parts management and reverse logistics. At the end of the product life cycle, products in the field (so called installed base) can usually be serviced by either new parts, obtained from a Last Time Buy, or by repaired failed parts. This paper, however, introduces a third source: the phase-out returns obtained from customers that replace systems. These returned parts may serve other customers that do not replace the systems yet. Phase-out return flows represent higher volumes and higher repair yields than failed parts and are cheaper to get than new ones. This new phenomenon has been ignored in the literature thus far, but due to increased product replacements rates its relevance will grow. We present a generic model, applied in a case study with real-life data from ConRepair, a third-party service provider in plant control systems (mainframes). Volumes of demand for spares, defects returns and phase-out returns are interrelated, because the same installed base is involved. In contrast with the existing literature, this paper explicitly models the operational control of both failed- and phase-out returns, which proves far from trivial given the nonstationary nature of the problem. We have to consider subintervals within the total planning interval to optimize both Last Time Buy and control policies well. Given the novelty of the problem, we limit ourselves to a single customer, single-item approach. Our heuristic solution methods prove efficient and close to optimal when validated. The resulting control policies in the case-study are also counter-intuitive. Contrary to (management) expectations, exogenous variables prove to be more important to the repair firm (which we show by sensitivity analysis) and optimizing the endogenous control policy benefits the customers. Last Time Buy volume does not make the decisive difference; far more important is the disposal versus repair policy. PUSH control policy is outperformed by PULL, which exploits demand information and waits longer to decide between repair and disposal. The paper concludes by mapping a number of extensions for future research, as it represents a larger class of problems.spare parts;reverse logistics;phase-out;PUSH-PULL repair;non stationary;Last Time Buy;business case

A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology

The widespread availability of high-dimensional biological data has made the simultaneous screening of numerous biological characteristics a central statistical problem in computational biology. While the dimensionality of such datasets continues to increase, the problem of teasing out the effects of biomarkers in studies measuring baseline confounders while avoiding model misspecification remains only partially addressed. Efficient estimators constructed from data adaptive estimates of the data-generating distribution provide an avenue for avoiding model misspecification; however, in the context of high-dimensional problems requiring simultaneous estimation of numerous parameters, standard variance estimators have proven unstable, resulting in unreliable Type-I error control under standard multiple testing corrections. We present the formulation of a general approach for applying empirical Bayes shrinkage approaches to asymptotically linear estimators of parameters defined in the nonparametric model. The proposal applies existing shrinkage estimators to the estimated variance of the influence function, allowing for increased inferential stability in high-dimensional settings. A methodology for nonparametric variable importance analysis for use with high-dimensional biological datasets with modest sample sizes is introduced and the proposed technique is demonstrated to be robust in small samples even when relying on data adaptive estimators that eschew parametric forms. Use of the proposed variance moderation strategy in constructing stabilized variable importance measures of biomarkers is demonstrated by application to an observational study of occupational exposure. The result is a data adaptive approach for robustly uncovering stable associations in high-dimensional data with limited sample sizes

Robust and Flexible Estimation of Stochastic Mediation Effects: A Proposed Method and Example in a Randomized Trial Setting

Causal mediation analysis can improve understanding of the mechanisms underlying epidemiologic associations. However, the utility of natural direct and indirect effect estimation has been limited by the assumption of no confounder of the mediator-outcome relationship that is affected by prior exposure---an assumption frequently violated in practice. We build on recent work that identified alternative estimands that do not require this assumption and propose a flexible and double robust semiparametric targeted minimum loss-based estimator for data-dependent stochastic direct and indirect effects. The proposed method treats the intermediate confounder affected by prior exposure as a time-varying confounder and intervenes stochastically on the mediator using a distribution which conditions on baseline covariates and marginalizes over the intermediate confounder. In addition, we assume the stochastic intervention is given, conditional on observed data, which results in a simpler estimator and weaker identification assumptions. We demonstrate the estimator's finite sample and robustness properties in a simple simulation study. We apply the method to an example from the Moving to Opportunity experiment. In this application, randomization to receive a housing voucher is the treatment/instrument that influenced moving to a low-poverty neighborhood, which is the intermediate confounder. We estimate the data-dependent stochastic direct effect of randomization to the voucher group on adolescent marijuana use not mediated by change in school district and the stochastic indirect effect mediated by change in school district. We find no evidence of mediation. Our estimator is easy to implement in standard statistical software, and we provide annotated R code to further lower implementation barriers.Comment: 24 pages, 2 tables, 2 figure

Population Intervention Models in Causal Inference

Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a] treatment variable or risk variable on the distribution of a disease in a population. These models, as originally introduced by Robins (e.g., Robins (2000a), Robins (2000b), van der Laan and Robins (2002)), model the marginal distributions of treatment-specific counterfactual outcomes, possibly conditional on a subset of the baseline covariates, and its dependence on treatment. Marginal structural models are particularly useful in the context of longitudinal data structures, in which each subject\u27s treatment and covariate history are measured over time, and an outcome is recorded at a final time point. In addition to the simpler, weighted regression approaches (inverse probability of treatment weighted estimators), more general (and robust) estimators have been developed and studied in detail for standard MSM (Robins (2000b), Neugebauer and van der Laan (2004), Yu and van der Laan (2003), van der Laan and Robins (2002)). In this paper we argue that in many applications one is interested in modeling the difference between a treatment-specific counterfactual population distribution and the actual population distribution of the target population of interest. Relevant parameters describe the effect of a hypothetical intervention on such a population, and therefore we refer to these models as intervention models. We focus on intervention models estimating the effect on an intervention in terms of a difference of means, ratio in means (e.g., relative risk if the outcome is binary), a so called switch relative risk for binary outcomes, and difference in entire distributions as measured by the quantile-quantile function. In addition, we provide a class of inverse probability of treatment weighed estimators, and double robust estimators of the causal parameters in these models. We illustrate the finite sample performance of these new estimators in a simulation study

Nonparametric population average models: deriving the form of approximate population average models estimated using generalized estimating equations

For estimating regressions for repeated measures outcome data, a popular choice is the population average models estimated by generalized estimating equations (GEE). We review in this report the derivation of the robust inference (sandwich-type estimator of the standard error). In addition, we present formally how the approximation of a misspecified working population average model relates to the true model and in turn how to interpret the results of such a misspecified model

Semiparametric theory and empirical processes in causal inference

In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. We begin with a brief introduction to the general problem of causal inference, and go on to discuss estimation and inference for causal effects under semiparametric models, which allow parts of the data-generating process to be unrestricted if they are not of particular interest (i.e., nuisance functions). These models are very useful in causal problems because the outcome process is often complex and difficult to model, and there may only be information available about the treatment process (at best). Semiparametric theory gives a framework for benchmarking efficiency and constructing estimators in such settings. In the second part of the paper we discuss empirical process theory, which provides powerful tools for understanding the asymptotic behavior of semiparametric estimators that depend on flexible nonparametric estimators of nuisance functions. These tools are crucial for incorporating machine learning and other modern methods into causal inference analyses. We conclude by examining related extensions and future directions for work in semiparametric causal inference