A strategy for third-party logistics systems: A case analysis using the blue ocean strategy

One of today's most frequently discussed topics in the business world is how to escape from the intense Red Ocean and how to create an uncontested Blue Ocean. However, because there are few practical guidelines available on this topic, we will introduce a case study of a third-party logistics (3PL) provider, CJ-Global Logistics Service (CJ-GLS), to show how it aspires to be a leader in the newly introduced 3PL industry in South Korea. CJ-GLS is a latecomer in the logistics industry, and its resources, such as the number of trucks and warehouses, are relatively small in comparison to those of established companies. But, it has achieved a distinct competitive advantage through innovative information technology (i.e., RFID—radio frequency identification), which has enabled it to create an uncontested market space, electronic logistics business. One remarkable fact about CJ-GLS is that its swift growth comes not from attracting competitors’ customers from the existing Red Ocean market but from creating a Blue Ocean market (3PL market), which previously existing incumbents ignored, and also from constructing a new business model founded on a RFID-based, ubiquitous-oriented 3PL system. Analyzed through a Four Actions Framework and characterized as Blue Ocean, this case study provides valuable information on how a company reinforces its competitive advantage from the Red Ocean while it transitions into a Blue Ocean by utilizing advanced information communication technologies.     

Data skeletons: simultaneous estimation of multiple quantiles for massive streaming datasets with applications to density estimation

We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic. We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation. For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation study to perform well and to have several distinct advantages over existing methods.     

Goodness‐of‐fit methods for matched case‐control studies

The authors propose graphical and numerical methods for checking the adequacy of the logistic regression model for matched case‐control data. Their approach is based on the cumulative sum of residuals over the covariate or linear predictor. Under the assumed model, the cumulative residual process converges weakly to a centered Gaussian limit whose distribution can be approximated via computer simulation. The observed cumulative residual pattern can then be compared both visually and analytically to a certain number of simulated realizations of the approximate limiting process under the null hypothesis. The proposed techniques allow one to check the functional form of each covariate, the logistic link function as well as the overall model adequacy. The authors assess the performance of the proposed methods through simulation studies and illustrate them using data from a cardiovascular study.     

A “brer rabbit” case for reverse mandated benefits

Reverse mandated benefits is a government-mandated policy that requires employees to provide their employers with benefits that workers would not provide otherwise. Of course, only those benefits would be mandated that are worth more to employers than they cost, as determined by political authorities. My case for such a policy argues that it is at least as sensible as policies mandating that employers provide benefits to their employees that would not be provided otherwise.