The contribution of measurement and information infrastructure to TQM success

There is currently some debate about which TQM practices contribute most to superior performance outcomes. Several proponents argue that softer TQM practices such as leadership, human resource management, and customer focus have more impact than benchmarking, process analysis or performance measurement. The evidence for which TQM factors contribute most to improved performance is not yet conclusive, and sometimes contradictory. Using data from a longitudinal study of 67 TQM firms we contribute to this debate. Our central hypothesis is that measurement of key TQM practices and performance outcomes is essential for TQM success. We examine the measurement practices of this cohort of firms, and report on the changes in their measurement behavior over time. Specifically, we analyze seven dimensions of measurement relating to customer satisfaction, employee satisfaction, process performance, impact of TQM on costs, impact of TQM on sales, self-assessment, and benchmarking. We calculate a measurement-intensity score for each firm, based on how many of these seven parameters were being measured, and we show that increased measurement intensity is strongly associated with perceived TQM success. Finally, using multivariate discriminant analysis, we identify eight variables that explain the level of TQM success with a classification accuracy of almost 90%. We conclude that to attain the highest levels of TQM success, firms need to engage in the measurement practices of self-assessment and benchmarking, but our data suggest that an appropriate measurement framework needs to be in place beforehand.     

Efficient computation of nonparametric survival functions via a hierarchical mixture formulation

We propose a new algorithm for computing the maximum likelihood estimate of a nonparametric survival function for interval-censored data, by extending the recently-proposed constrained Newton method in a hierarchical fashion. The new algorithm makes use of the fact that a mixture distribution can be recursively written as a mixture of mixtures, and takes a divide-and-conquer approach to break down a large-scale constrained optimization problem into many small-scale ones, which can be solved rapidly. During the course of optimization, the new algorithm, which we call the hierarchical constrained Newton method, can efficiently reallocate the probability mass, both locally and globally, among potential support intervals. Its convergence is theoretically established based on an equilibrium analysis. Numerical study results suggest that the new algorithm is the best choice for data sets of any size and for solutions with any number of support intervals.     

A HEURISTIC PROCEDURE FOR DETERMINING IN-PROCESS INVENTORIES

Most prior studies that examine in-process inventory requirements associated with a manufacturing facility employ an optimizing algorithm; this article proposes a heuristic procedure for determining such requirements. A procedure is given for determining “active” and “banked” work-in-process requirements. The two are combined to give total in-process requirements. A key factor presented in the paper is the relationship between in-process inventory requirements and the “balancing” of a production facility. Most prior researchers examine these problems independently. It will be shown that both problems can be resolved concurrently. The concepts are applicable to both an existing as well as a new manufacturing facility.