An integrated approach for estimation and reduction of measurement variation (and its components) through a single parameter design experiment is developed. Systems with a linear signal-response relationship are considered. The noise factors are classified into a few distinct categories based on their impact on the measurement system. A random coefficients model that accounts for the effect of ...

Purpose – The purpose of this paper is to explore how performance measurement and management (PMM) practices are implicated in the exercise of power in organizations through the theoretical lens of institutional entrepreneurship. Design/methodology/approach – The paper builds on a longitudinal field study in a Chinese local government department conducted over a period of six years. Findings – ...

This paper discusses and integrates the concept of complexity in industrial performance measurement management systems (PMM) theory, providing a comprehensive overview different methodologies used within decision research area. It also importance introducing Key Activity Indicators (KAI) PMM, specifically related to Operations Supply Chain areas. Moreover, it provides validation methodology thr...

A major computational challenge in analyzing metagenomics sequencing reads is to identify unknown sources of massive and heterogeneous short DNA reads. A promising approach is to efficiently and sufficiently extract and exploit sequence features, i.e., k-mers, to bin the reads according to their sources. Shorter k-mers may capture base composition information while longer k-mers may represent r...

The practice of 5S principles in manufacturing small and medium enterprises (SMEs) has been widely discussed scientific studies related to the critical success factors (CSFs) challenges for improving organizational performance. importance performance measurement sustaining becomes a strategic value improvement process elimination waste. In previous studies, practices is commonly viewed as an au...

In this paper, we proposed a novel probabilistic generative model to deal with explicit multiple-topic documents: Parametric Dirichlet Mixture Model(PDMM). PDMM is an expansion of an existing probabilistic generative model: Parametric Mixture Model(PMM) by hierarchical Bayes model. PMM models multiple-topic documents by mixing model parameters of each single topic with an equal mixture ratio. P...

BACKGROUND The appropriate handling of missing covariate data in prognostic modelling studies is yet to be conclusively determined. A resampling study was performed to investigate the effects of different missing data methods on the performance of a prognostic model. METHODS Observed data for 1000 cases were sampled with replacement from a large complete dataset of 7507 patients to obtain 500...

Abstract The trend of globalization has led to a structural change in the sales and procurement markets manufacturing companies recent decades. In order not be left behind by this change, have internationalized their production structures. Global networks with diverse supply service interdependencies are result. However, management global is highly complex. Key performance indicator (KPI) alrea...

BACKGROUND Multiple imputation is a commonly used method for handling incomplete covariates as it can provide valid inference when data are missing at random. This depends on being able to correctly specify the parametric model used to impute missing values, which may be difficult in many realistic settings. Imputation by predictive mean matching (PMM) borrows an observed value from a donor wit...

Purpose This paper proposes an SME oriented Industry 4.0 maturity framework to explore the interplay between manufacturing processes, performance measurement system and management practices (PMM). Given that fourth industrial revolution, famously referred as 4.0, is a new paradigm for firms, it crucial know ‘as-is’ state or of SMEs' processes link with their PMM. Despite availability numerous m...