Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

Task analysis of healthcare delivery: A case study

Human error and medical error are highly known as contributors to patient safety [Institute of Medicine (IOM), November 1999. To err is human: building a safer health system. Available at: http://www.nap.edu/openbook/0309068371/html/11.html (accessed 05.03.07); Institute of Medicine (IOM), March 2001. Crossing the quality chasm: a new health system for the 21st century. Available at: http://www.nap.edu/openbook/0309072808/html (accessed 05.03.07); Joint Commission on Accreditation of Healthcare Organizations (JCAHO), 2007. Failure mode, effect, and criticality analysis (FMECA) worksheet. Available at: http://www.jcaho.org (accessed 24.06.07)]. A study was performed to identify the process flow affiliated with elder patients transitioning through different continuums of emergency and non-emergency care. This research is part of a larger research effort to develop and implement a web-based healthcare system that enables hospitals and nursing homes to share patient information resulting in increased knowledge of a patient's medical history, decreased errors and enhanced patient safety. Future research efforts for this study are also presented.     

主动性-被动性员工创新行为:基于分布式领导的作用机制研究

员工创新是企业创新驱动的重要推动力和提升其核心竞争力的关键。当前中国组织情境中，存在着两种不同性质的员工创新行为：主动性员工创新行为和被动性员工创新行为，两者所产生的创新绩效存在巨大差异。然而，现有文献对被动性员工创新行为研究不足，理论研究已经无法适应实践的需要，因此，有必要将两种不同性质的员工创新行为放在一个框架下进行研究，以丰富创新行为的研究内涵。本文以新兴的领导发展取向——分布式领导为切入点，引入组织支持感作为中介变量，上下级关系和价值观匹配作为调节变量，构建了分布式领导对主动性-被动性员工创新行为的作用模型。为了验证该模型，以408名知识型员工为研究对象，通过多元回归分析的方法进行检验，研究结果发现：分布式领导对主动性员工创新行为具有正向影响，对被动性员工创新行为具有负向影响；组织支持感在分布式领导与主动性-被动性员工创新行为之间起中介作用；上下级关系和价值观匹配正向调节分布式领导与组织支持感之间的关系，并进一步验证了有调节的中介作用。同时，上下级关系和价值观匹配交互调节了分布式领导与组织支持感之间的关系。本文研究为解构中国创新管理实践中纷杂多样的创新行为提供了理论诠释，丰富了中国情境下的创新管理理论。