Search for Alternatives and Collaboration with Incumbents: Two-Sided Sourcing Behavior in Business Markets*

Sourcing strategies in business markets have been considered separately and the practice of two-sided sourcing behavior—engaging in search for alternative suppliers and collaboration with an incumbent supplier—has not been examined. To fill that gap, we first identify boundary conditions under which the poor performance of an incumbent supplier intensifies an original equipment manufacturer's (OEM) search and collaboration. Then, we examine how an OEM's two-sided sourcing behavior influences one of the critical elements of sourcing performance: the responsiveness of the incumbent supplier. Our proposed hypotheses were tested with data from a national survey of 539 OEM purchasing managers in the Japanese electronics industry. The analysis results indicate three main findings. First, two environmental conditions—pace of technological change and volume uncertainty—have contrasting influences on the link between incumbent supplier performance and an OEM's search and collaboration. While uncertainty from the upstream channel (pace of technological change) enhances an OEM's search and collaboration, uncertainty from the downstream channel (volume uncertainty) lowers an OEM's search and collaboration. Second, an OEM's dependence on its incumbent supplier has differential effects: an OEM reduces search as its dependence on incumbent supplier increases, while it enhances collaboration as its dependence on incumbent supplier increases. Third, while search alone has a negative effect on responsiveness of an incumbent supplier, engaging in two-sided sourcing behavior (i.e., combining search with collaboration) has a positive effect on responsiveness of the incumbent supplier.     

Simulation-Based Inspection Policies for a One-Shot System in Storage Over a Finite Time Span

In this paper, we consider an inspection policy problem for a one-shot system with two types of units over a finite time span and want to determine inspection intervals optimally with given replacement points of Type 2 units. The interval availability and life cycle cost are used as optimization criteria and estimated by simulation. Two optimization models are proposed to find the optimal inspection intervals for the exponential and general distributions. A heuristic method and a genetic algorithm are proposed to find the near-optimal inspection intervals, to satisfy the target interval availability and minimize the life-cycle cost. We study numerical examples to compare the heuristic method with the genetic algorithm and investigate the effect of model parameters to the optimal solutions.     

An Efficient Algorithm for the Least-Squares Cross-Validation with Symmetric and Polynomial Kernels

The least-squares cross-validation is a completely automatic method for choosing the smoothing parameter in probability density estimation but this method consume large amounts of computer time. This article concerns an efficient computational algorithm for this method when the kernel is symmetric and polynomial functions.     

Influential subsets on the variable selection

When one or few observations are deleted in the multiple linear regression model, they can affect the variable selection. In this paper we derived the formula for the Mallows C_p criterion when k observations are deleted and express it as a functionn of basic building blocks such as residuals and leverages. Also, two real date sets are used to see how the selected model changes as few observations re deleted.     

A Note on the Fisher Information in Exponential Distribution

ABSTRACT

It is known that the Fisher information in any set of order statistics can be simplified to a sum of double integrals. In this article, we show that it can be further simplified to a sum of single integrals for the scale parameter of an exponential distribution. Moreover, we use the result and provide a simple method of obtaining the optimal spacing of order statistics.     

Accelerated Life Test Data Analysis for Repairable Systems

In this article, the imperfect maintenance model and proportional intensity model are used to analyze failure data of repairable systems in accelerated life testing. In the design and development phase of products, we should collect and analyze failure data quickly with small proto-type products. Thus, we test the products under accelerated conditions and if the products fail, then we repair and use those continuously in the life testing. Acceleration and repair models are needed to analyze the failure data. An age reduction model (Brown et al.'s Brown et al. 1983 Brown , J. F. , Mahoney , J. F. , Sivazlian , B. D. ( 1983 ). Hysteresis repair in discounted replacement problems . IIE Trans. 15 : 156 – 165 . [CSA] [Taylor & Francis Online], [Web of Science ®] , [Google Scholar], model) and relationship between scale parameter and stress level are assumed. The stress acts multiplicatively on the baseline cumulative intensity. The maximum likelihood method is used, the log-likelihood function is derived, and a maximizing procedure is proposed. In simulation studies, we investigate the accuracy and trends of the maximum likelihood estimator.     

Constrained Bayes and Empirical Bayes Estimation with Balanced Loss Functions

This article develops constrained Bayes and empirical Bayes estimators under balanced loss functions. In the normal-normal example, estimators of the mean squared errors of the EB and constrained EB estimators are provided which are correct asymptotically up to O(m ^?1), m denoting the number of strata.     

Nonparametric Two-Sample Tests of Longitudinal Data with Termination Events

In longitudinal data, observations of response variables occur at fixed or random time points, and can be stopped by a termination event. When comparing longitudinal data for two groups, such irregular observation behavior must be considered to yield suitable results. In this article, we propose the use of nonparametric tests based on the difference between weighted cumulative mean functions for comparing two mean functions with an adjustment for difference in the timing of termination events. We also derive the asymptotic null distributions of the test statistics and examine their small sample properties through simulations. We apply our method to data from a study of liver cirrhosis.     

Computing Critical Values of Exact Tests by Incorporating Monte Carlo Simulations Combined with Statistical Tables

Various exact tests for statistical inference are available for powerful and accurate decision rules provided that corresponding critical values are tabulated or evaluated via Monte Carlo methods. This article introduces a novel hybrid method for computing p‐values of exact tests by combining Monte Carlo simulations and statistical tables generated a priori. To use the data from Monte Carlo generations and tabulated critical values jointly, we employ kernel density estimation within Bayesian‐type procedures. The p‐values are linked to the posterior means of quantiles. In this framework, we present relevant information from the Monte Carlo experiments via likelihood‐type functions, whereas tabulated critical values are used to reflect prior distributions. The local maximum likelihood technique is employed to compute functional forms of prior distributions from statistical tables. Empirical likelihood functions are proposed to replace parametric likelihood functions within the structure of the posterior mean calculations to provide a Bayesian‐type procedure with a distribution‐free set of assumptions. We derive the asymptotic properties of the proposed nonparametric posterior means of quantiles process. Using the theoretical propositions, we calculate the minimum number of needed Monte Carlo resamples for desired level of accuracy on the basis of distances between actual data characteristics (e.g. sample sizes) and characteristics of data used to present corresponding critical values in a table. The proposed approach makes practical applications of exact tests simple and rapid. Implementations of the proposed technique are easily carried out via the recently developed STATA and R statistical packages.