Business Analytics for Flexible Resource Allocation Under Random Emergencies

Angalakudati, Mallik; Balwani, Siddharth; Calzada, Jorge; Chatterjee, Bikram; Perakis, Georgia; Raad, Nicolas; Uichanco, Joline

doi:10.1287/mnsc.2014.1919

Cited by 35 publications

(25 citation statements)

References 17 publications

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“…Obviously, these plans need to be aligned to ensure the smooth operation and staffing of A&E departments (Izady and Worthington, 2012). Similar problems are faced by other hospital operations, where "urgent" and "regular" patient demand must be satisfied (Truong, 2015), or more broadly in services where both scheduled and emergency jobs or repairs need to be conducted (Angalakudati et al, 2014). A&E departments in the UK record a number of demand statistics, classified under three types: major A&E, single specialty and other/minor A&E. Each of these types requires different resources and staffing.…”

Section: Case Study: Predicting Accident and Emergency Service Demandmentioning

confidence: 99%

Forecasting with temporal hierarchies

Athanasopoulos

Kourentzes

Petropoulos

2017

European Journal of Operational Research

178

164

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This paper introduces the concept of Temporal Hierarchies for time series forecasting. A temporal hierarchy can be constructed for any time series by means of non-overlapping temporal aggregation. Predictions constructed at all aggregation levels are combined with the proposed framework to result in temporally reconciled, accurate and robust forecasts. The implied combination mitigates modelling uncertainty, while the reconciled nature of the forecasts results in a unified prediction that supports aligned decisions at different planning horizons: from shortterm operational up to long-term strategic planning. The proposed methodology is independent of forecasting models. It can embed high level managerial forecasts that incorporate complex and unstructured information with lower level statistical forecasts. Our results show that forecasting with temporal hierarchies increases accuracy over conventional forecasting, particularly under increased modelling uncertainty. We discuss organisational implications of the temporally reconciled forecasts using a case study of Accident & Emergency departments.

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Section: Case Study: Predicting Accident and Emergency Service Demandmentioning

confidence: 99%

Forecasting with temporal hierarchies

Athanasopoulos

Kourentzes

Petropoulos

2017

European Journal of Operational Research

178

164

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“…The breadth of Business Analytics applications is illustrated by the variety of recent publications. Topics include the relationship between advertisements and purchasing decisions [36], resource allocation in emergency response scenarios [37], energy security in a changing world climate [38], as well as decision support for mergers and acquisitions [39].…”

Section: Business Analyticsmentioning

confidence: 99%

In free float: Developing Business Analytics support for carsharing providers

Wagner

Brandt

Neumann

2016

Omega

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“…Big Data Analytics (BDA) has been described as the set of applications and tools that provides more knowledge to improve the quality and relevance of the information used in decision making (Portela, Lima, & Santos, ). In the last decade, a large number of studies have been reported in the field of Big Data Analytics (Angalakudati et al, ; Bronzo et al, ; Chen, Chiang, & Storey, etc.) and the field is gaining fast traction in industry, academia and the government (Batarseh & Latif, ).…”

Section: Introductionmentioning

confidence: 99%

“…Big Data Analytics (BDA) has been described as the set of applications and tools that provides more knowledge to improve the quality and relevance of the information used in decision making (Portela, Lima, & Santos, 2016). In the last decade, a large number of studies have been reported in the field of Big Data Analytics (Angalakudati et al, 2014;Bronzo et al, 2013; *Correspondence: Korina Katsaliaki, International Hellenic University, 14th km Thessaloniki-N.Moudania 57001, Thessaloniki, Greece. E-mail: k.katsaliaki@ihu.edu.gr Chen, Chiang, & Storey, 2012 etc.…”

Section: Introductionmentioning

confidence: 99%

Big data analytics in health: an overview and bibliometric study of research activity

Galetsi

Katsaliaki

2019

Health Info Libraries J

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Objective The study presents an overview of the research activity in Big Data Analytics (BDA) in the field of health and demonstrates the existing knowledge through related examples. The objective is to inform health librarians about the nature and magnitude of the technological innovations in health information analysis tools, its influence, and where and how further material could be searched. Methods We performed a bibliometric and co‐citation analysis within a total of 804 papers published between 2000 and 2016 and retrieved from the Web of Science and Scopus databases. Using the NVivo text analysis software, we identified the stakeholders of BDA in health and innovative decision support systems in the field. Results Our findings show a tremendous increase in published papers after 2014. Most of them are relevant to neurology and medical oncology. The stakeholders are clinicians, researchers, patients, administrators, IT specialists, vendors and policymakers. New BDA tools in medicine are mostly developed for disease monitoring purposes while they utilise visualisation to identify disease patterns and statistical analysis of past data for making predictions. Conclusions Health analytics provide a unique opportunity for advancing health information research and medical decision making. It provides health information professionals with new tools in problem‐solving offering new perspectives in prognosis and diagnosis of diseases.

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Business Analytics for Flexible Resource Allocation Under Random Emergencies

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Cited by 35 publications

References 17 publications

Forecasting with temporal hierarchies

Forecasting with temporal hierarchies

In free float: Developing Business Analytics support for carsharing providers

Big data analytics in health: an overview and bibliometric study of research activity

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