A grey approach to predicting healthcare performance

© 2018 Elsevier Ltd The success of an organization or a particular activity is evaluated through the measurement of key performance indicators (KPIs). The aim of this paper is to analyze and predict the indicators of healthcare performance using grey systems theory. Recent advancements in science and technology have made the healthcare industry extremely efficient at collecting data using electronic claims systems such as electronic health records. Therefore, collecting field level primary data becomes easier and accumulate them to generate secondary data for research purpose and to get an insight of the organization performance is absolutely necessary. Our research analyzes the KPIs of a hospital based on a secondary data source. Since, secondary data contains uncertainty and sometimes poor information, grey prediction model suits best to make a prediction model in this regard. Conventional grey model has considerable drawbacks while making a rigorous prediction model. For this, we apply an improved grey prediction model to predict the KPIs of the healthcare performance indicators. Several error measures in our model give a best fit of the data and allow prediction of the KPIs. The prediction model gives good estimates of the quantitative indicators and produced error rate within an acceptable range. We observe that the KPIs of bed turnover rate (BTR) and bed occupancy rate (BOR) have an increasing trend, whereas the KPIs of average length of stay (ALOS), hospital death rate (HDR) and hospital infection rate (HIR) show a decreasing trend over time. The main contribution of this research is a grey-based prediction model that can provide managers with the information they need to evaluate and predict the performance of a hospital. The research indicates that managers should give greater priority to the indicators which will result in better patients’ satisfaction and improved profit margin. Healthcare managers striving towards better performance will now have an empirical basis upon which to formulate and adjust their strategies, after analyzing the predicted value

Examining price and service competition among retailers in a supply chain under potential demand disruption

© 2017 Elsevier Ltd Supply chain disruptions management has attracted significant attention among researchers and practitioners. The paper aims to examine the effect of potential market demand disruptions on price and service level for competing retailers. To investigate the effect of potential demand disruptions, we consider both a centralized and a decentralized supply chain structure. To analyze the decentralized supply chain, the Manufacturing Stackelberg (MS) game theoretical approach was undertaken. The analytical results were tested using several numerical analyses. It was shown that price and service level investment decisions are significantly influenced by demand disruptions to retail markets. For example, decentralized decision makers tend to lower wholesale and retail prices under potential demand disruptions, whereas a proactive retailer needs to increase service level with an increased level of possible disruptions. This research may aid managers to analyze disruptions prone market and to make appropriate decision for price and service level. The manufacturer or the retailers will also be able to better determine when to close a market based on the proposed analysis by considering anticipated disruptions. The benefits and usefulness of the proposed approach are explained through a real-life case adopted from a toy supply chain in Bangladesh

Barriers to green supply chain management: An emerging economy context

© 2019 Elsevier Ltd Green supply chain management is attracting increasing attention as a way to decrease the adverse environmental effects of industries worldwide. However, considering the context of an emerging economy like Bangladesh, green supply chain management is still in its inception and has not been widely embraced in the textile industry, and therefore barriers hindering its adoption in emerging economy context demand a comprehensive investigation. This research reviews the viewpoints and hurdles in adopting green supply chain management practices in the context of the Bangladeshi textile industry. A questionnaire survey of Bangladeshi textile practitioners of operations and supply chain management division, having a sample size of thirty, was undertaken to identify the barriers, and a hierarchical cluster analysis technique was used in the detailed analysis of this data. Opinions were sought from experts on the significance of the resulting clusters, considering the relative importance of the barriers. Fifteen barriers to the adoption of green supply chain management were identified in the review of the literature, with these barriers then analyzed by using the data collected from Bangladeshi textile industry practitioners. The research indicates that the most important barrier is that there is low demand from customers and financial constraint resulting from short term little financial benefit to businesses, with lack of government regulations also a commonly faced barrier in adopting green supply chain initiatives. This study will provide valuables insights to practitioners and relevant policy makers about the barriers prevailing in the emerging economies towards the adoption of green supply chain management practices, which, in turn, can guide to undertake appropriate steps for alleviating those barriers