Workload Control and Order Release: A Lean Solution for Make‐to‐Order Companies

Protecting throughput from variance is the key to achieving lean. Workload control (WLC) accomplishes this in complex make‐to‐order job shops by controlling lead times, capacity, and work‐in‐process (WIP). However, the concept has been dismissed by many authors who believe its order release mechanism reduces the effectiveness of shop floor dispatching and increases work center idleness, thereby also increasing job tardiness results. We show that these problems have been overcome. A WLC order release method known as “LUMS OR” (Lancaster University Management School order release) combines continuous with periodic release, allowing the release of work to be triggered between periodic releases if a work center is starving. This paper refines the method based on the literature (creating “LUMS COR” [Lancaster University Management School corrected order release]) before comparing its performance against the best‐performing purely periodic and continuous release rules across a range of flow directions, from the pure job shop to the general flow shop. Results demonstrate that LUMS COR and the continuous WLC release methods consistently outperform purely periodic release and Constant WIP. LUMS COR is considered the best solution in practice due to its excellent performance and ease of implementation. Findings have significant implications for research and practice: throughput times and job tardiness results can be improved simultaneously and order release and dispatching rules can complement each other. Thus, WLC represents an effective means of implementing lean principles in a make‐to‐order context.     

A 1,408 km bicycle tour with prostate cancer patients—results of a pilot study

Negative psychological and physical effects of prostate cancer and its medical treatment may persist many years after diagnosis. The influence of a long cycling tour on rehabilitative or health-related effects with prostate cancer patients has not yet been studied. In practice, physicians and therapists rarely recommend cycling to prostate cancer. In May 2010, eight prostate cancer patients rode their bikes for over 1,408 km from Cologne to Marseille within 5 weeks. Endurance test, blood examinations (prostate-specific antigen (PSA), total testosterone, interleukin-6, oxidative stress, and antioxidant capacity) and quality of life questionnaires were completed before and after the tour. All eight subjects reached Marseille. Significant improvements could be observed in physical performance and certain quality of life scores (p?=?0.008), as well as a reduction of total testosterone (p?=?0.19). PSA levels did not change. This pilot study suggests that long bicycle tours with prostate cancer patients are feasible. Due to the missing control group and the small sample size, the results of this pilot study are limited.     

Housing Conditions and Risk: Reporting on a European Study of Housing Quality and Risk of Accidents for Older People

The declining capacities in older people often lead to limitations in activities of daily living and a simultaneous rising demand for care services. Many of these limitations in daily activities are to a varying degree caused, enhanced or facilitated by disadvantageous housing and environmental conditions. Home modifications have been credited as an important part of the solution, allowing for an extended and safer use of the home. In order to examine this issue in more detail, this paper provides empirical evidence on the type and extent of risks associated with accidents around the home. It reports on findings from a European case study on the difficulties using the home and risks of accidents which may lead to injury. It is argued that healthy housing conditions for older persons are important to maximize the supportive capacity of one's home, to stimulate an active and healthy lifestyle, and to avoid unnecessary institutionalization.     

Media orientation of German university decision makers and the executive influence of public relations

This study investigated the executive influence of public relations managers in the German higher education system. The study is based on a whole-population survey of German university decision makers (N = 1619). It provides evidence that the mediatization of German higher education offers an important opportunity for the empowerment of university PR departments. They can benefit from the New Public Management reforms in recent years as power shifts to management-oriented administrators and the public image of universities turns into a competitive asset. Nevertheless, an indispensable precondition for PR departments’ organizational advancement resides in professionalization. PR workers need to claim a status of expert boundary spanners between their universities and the public stakeholders.     

Does signaling childcare support on job applications reduce the motherhood penalty?

Review of Economics of the Household - There is substantial evidence that due to perceived childcare obligations, mothers are disadvantaged in labor markets. To what extent can childcare support...     

Maximum-likelihood estimation of the random-clumped multinomial model as a prototype problem for large-scale statistical computing

Numerical methods are needed to obtain maximum-likelihood estimates (MLEs) in many problems. Computation time can be an issue for some likelihoods even with modern computing power. We consider one such problem where the assumed model is a random-clumped multinomial distribution. We compute MLEs for this model in parallel using the Toolkit for Advanced Optimization software library. The computations are performed on a distributed-memory cluster with low latency interconnect. We demonstrate that for larger problems, scaling the number of processes improves wall clock time significantly. An illustrative example shows how parallel MLE computation can be useful in a large data analysis. Our experience with a direct numerical approach indicates that more substantial gains may be obtained by making use of the specific structure of the random-clumped model.     

Efficient Semiparametric Estimation of the Fama–French Model and Extensions

This paper develops a new estimation procedure for characteristic‐based factor models of stock returns. We treat the factor model as a weighted additive nonparametric regression model, with the factor returns serving as time‐varying weights and a set of univariate nonparametric functions relating security characteristic to the associated factor betas. We use a time‐series and cross‐sectional pooled weighted additive nonparametric regression methodology to simultaneously estimate the factor returns and characteristic‐beta functions. By avoiding the curse of dimensionality, our methodology allows for a larger number of factors than existing semiparametric methods. We apply the technique to the three‐factor Fama–French model, Carhart's four‐factor extension of it that adds a momentum factor, and a five‐factor extension that adds an own‐volatility factor. We find that momentum and own‐volatility factors are at least as important, if not more important, than size and value in explaining equity return comovements. We test the multifactor beta pricing theory against a general alternative using a new nonparametric test.     

Online signal extraction by robust regression in moving windows with data-adaptive width selection

Online (also ‘real-time’ or ‘sequential’) signal extraction from noisy and outlier-interfered data streams is a basic but challenging goal. Fitting a robust Repeated Median (Siegel in Biometrika 69:242–244, 1982) regression line in a moving time window has turned out to be a promising approach (Davies et al. in J. Stat. Plan. Inference 122:65–78, 2004; Gather et al. in Comput. Stat. 21:33–51, 2006; Schettlinger et al. in Biomed. Eng. 51:49–56, 2006). The level of the regression line at the rightmost window position, which equates to the current time point in an online application, is then used for signal extraction. However, the choice of the window width has a large impact on the signal extraction, and it is impossible to predetermine an optimal fixed window width for data streams which exhibit signal changes like level shifts and sudden trend changes. We therefore propose a robust test procedure for the online detection of such signal changes. An algorithm including the test allows for online window width adaption, meaning that the window width is chosen w.r.t. the current data situation at each time point. Comparison studies show that our new procedure outperforms an existing Repeated Median filter with automatic window width selection (Schettlinger et al. in Int. J. Adapt. Control Signal Process. 24:346–362, 2010).     

Multivariate Real-Time Signal Extraction by a Robust Adaptive Regression Filter

We propose a new regression-based filter for extracting signals online from multivariate high frequency time series. It separates relevant signals of several variables from noise and (multivariate) outliers.

Unlike parallel univariate filters, the new procedure takes into account the local covariance structure between the single time series components. It is based on high-breakdown estimates, which makes it robust against (patches of) outliers in one or several of the components as well as against outliers with respect to the multivariate covariance structure. Moreover, the trade-off problem between bias and variance for the optimal choice of the window width is approached by choosing the size of the window adaptively, depending on the current data situation.

Furthermore, we present an advanced algorithm of our filtering procedure that includes the replacement of missing observations in real time. Thus, the new procedure can be applied in online-monitoring practice. Applications to physiological time series from intensive care show the practical effect of the proposed filtering technique.