Strategic Management of Operations in the Emergency Department

I ncreased data availability is poised to shape both business practice and supply chain management (SCM) research. This article addresses an issue that can arise when trying to use big data to answer academic research questions. This issue is that distilled data often have a panel structure whereby repeated measurements are available on one or more variables for a substantial number of subjects. Thus, to fully leverage the richness of big data for academic research, SCM scholars need an understanding regarding the different types of research questions answerable with panel data. In this article, we devise a framework detailing different types of research questions SCM scholars can answer with panel data. This framework provides a basis to categorize how SCM scholars have examined the services supply chain setting of health care with public data regarding hospital-level patient satisfaction. We extend prior research by testing a series of three questions not yet examined in this area by fitting a series of structured latent curve models to seven years of hospital-level patient satisfaction for nearly 4,000 hospitals. The discussion highlights theoretical and methodological challenges SCM scholars are likely to encounter as they use the panel data in their research.

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“…; Venkat et al. ). Moreover, patient satisfaction is a significant predictor of important clinical outcomes (Trzeciak et al.…”

Section: Empirical Examplementioning

confidence: 99%

Leveraging Big Data to Develop Supply Chain Management Theory: The Case of Panel Data

Miller

Ganster

Griffis

2018

J of Business Logistics

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“…Emergency management is a popular research topic for scholars. Haghani [2], Green [3], Özdamar [4], Craft [5], Rawls [6], Holguín-Veras [7], Sheu [8], Venkat [9], and Ye et al [10] studied emergency management for platform systems, decision analysis, simulations, emergency plans, emergency disposal and top-level designs, and other aspects, obtaining rich research results. Numerous scholars have examined logistics applications in the emergency supply chain (Celik et al, [11]; Day, [12]), emergency logistics network (Holguin-Veras et al, [7]; Ransikarbum, [13]), emergency resource storage (Bell et al, [14]; Roni et al, [15]), selection of emergency center sites (Gutjahr et al, [16]), selection of emergency distribution routes (Bozorgi-Amiri et al, [17]; Yang et al, [18]; Ye et al, [19]), emergency transportation (Ball et al, [20]; Gralla et al, [21]), emergency resource allocation (Jacobson et al, [22]; Chakravarty, [23]; Zhan et al, [24]), and other aspects.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Planning Method for Drug Distribution in Earthquake Response Based on Sliding Time Window Series

Fang

Hou

Pan³

et al. 2020

IEEE Access

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Creating an effective and efficient distribution plan is a considerable challenge owing to urgency, incomplete information, and surging demands. In this study, a planning method based on a sliding time window series is designed to solve the problem of drug distribution in earthquake responses. First, this study presents a method designed to generate a series of sliding time windows considering time-varying demands. Second, a method is proposed to determine the effectiveness of the drug distribution plan according to its evaluation standards. Third, a dynamic planning model is established considering the sliding time window series and group information updates. Fourth, a simulation study is conducted to test the models and algorithms. Simulation results show that specific drug distribution plans should be provided to emergency planners in the event of an earthquake. The sliding time window series and group information updates are key factors in creating an effective drug distribution plan as part of an earthquake response.

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“…Strategic decision making in operations management and re-engineering was noted by current operations research (Venkat et al, 2015). Planning for QM was highlighted in Juran's (Njeru and Omondi, 2016).…”

Section: Total Quality Management (Tqm) Model Approachesmentioning

confidence: 99%

Investigating the view of quality management success factors amongst future early career operations leaders

Shokri¹

2019

IJQSS

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Purpose The study aims to investigate the gap between the current vision and knowledge of future early career operations leaders (OL) and common strategic total quality management (TQM) frameworks such as Malcolm Baldrige National Quality Awards and competing value framework. Design/methodology/approach A survey questionnaire was developed for different groups of participants as current higher education students to identify the gap and analyse the significance of these groups on the factors in TQM framework. The Kruskal–Wallis test as the non-parametric quantitative analysis technique was adopted for this study. Findings A new set of TQM factors with necessity of more knowledge and understanding of future generation was identified, followed by highlighting clear differences amongst different groups of this generation in terms of their demographic measures, perceived leadership style and organisational culture. Research limitations/implications This research study contributed significantly to the existing study about common QM models and their integration with theories relevant to organisational culture and leadership. The data collection can be extended further in the higher education sector or beyond that. Practical implications A sustainable operations leadership practice needs managers and leaders with a sustainable knowledge development of quality management; and as a result of this study, the current vision of future young OL would not echo this. Originality/value This study has a systematic, non-parametric approach towards currently fragmented QM analysis, and is integrated with human resource and visionary elements of future young OL and universal QM models and theories.

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Strategic Management of Operations in the Emergency Department

Cited by 18 publications

References 31 publications

Leveraging Big Data to Develop Supply Chain Management Theory: The Case of Panel Data

Leveraging Big Data to Develop Supply Chain Management Theory: The Case of Panel Data

Dynamic Planning Method for Drug Distribution in Earthquake Response Based on Sliding Time Window Series

Investigating the view of quality management success factors amongst future early career operations leaders

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