Developing a business analytics methodology: A case study in the foodbank sector

Hindle, Giles A.; Vidgen, Richard

doi:10.1016/j.ejor.2017.06.031

Cited by 65 publications

(51 citation statements)

References 41 publications

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“…Prescriptive analytics goes beyond the first two kinds of analytics and targets at participating in the decision-making process by providing useful and actionable suggestions for business problems. The implementation of descriptive, predictive, and prescriptive analytics can help companies make smarter decisions and generate more business value (Hindle & Vidgen, 2018).…”

Section: Definition Of Business Analyticsmentioning

confidence: 99%

“…Analytics, the short form of business analytics in a business context (Gorman & Klimberg, 2014;Holsapple, Lee-Post & Pakath, 2014), can be considered as data analytics in business domain (Duan & Xiong, 2015;Shen & Tzeng, 2016). Furthermore, business analytics can also be viewed as the employment of big data analytics in business (Shen & Tzeng, 2016) since the data that business analytics processes can be big and unstructured (Hindle & Vidgen, 2018). Also, business analytics is enabled by techniques and methods such as data mining, data warehousing, statistical modeling, quantitative analysis, visualization, and other information technologies (Davenport & Harris, 2007;Shen & Tzeng, 2016).…”

Section: Definition Of Business Analyticsmentioning

confidence: 99%

“…First and foremost, business analytics helps enterprises to objectify the decision-making process rather than making decisions based on pure experience and assumptions (Schläfke, Silvi & Möller, 2013;Troilo, Bouchet, Urban & Sutton, 2016). Secondly, business analytics can accelerate the decision-making process (Hindle & Vidgen, 2018;Sharma, Mithas & Kankanhalli, 2014). Thirdly, using business analytics can have a better understanding of customers and markets so that enterprises can improve customer services and the quality of customer marketing (Schläfke et al, 2013;Troilo et al, 2016).…”

Section: Functionality Of Business Analyticsmentioning

confidence: 99%

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A systematic review on business analytics

Yin

Fernández

2020

JIEM

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Purpose: Business analytics, a buzzword of the recent decade, has been applied by thousands of enterprises to help generate more values and enhance their business performance. However, many aspects of business analytics remain unclear. This study clarifies the definition of business analytics combined with its functionality and the relation between business analytics and business intelligence. Moreover, we illustrate the applications of business analytics in both business areas and industry sectors and shed light on the education in business analytics. Ultimately, to facilitate future research, we summarize several research techniques used in the literature reviewed.Design/methodology/approach: We set well-established selection criteria to select relevant literature from two widely recognized databases: Scopus and Web of Science. Afterward, we reviewed the literature and coded relevant sections in an inductive way using MAXQDA. Then we compared and synthesized the coded information.Findings: There are mainly four findings. Firstly, according to the bibliometric analysis, literature about business analytics is growing exponentially. Secondly, business analytics is a system that enabled by machine learning techniques aiming at promoting the efficiency and performance of an organization by supporting the decision-making process. Thirdly, the application of business analytics is comprehensive, not only in specific areas of a company but also in different industry sectors. Finally, business analytics is interdisciplinary, and the successful training should involve technical, analytical, and business skills.Originality/value: This systematic review, as a synthesis of the current research on business analytics, can serve as a quick guide for new researchers and practitioners in the field, while experienced scholars can also benefit from this work, taking it as a practical reference.

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Section: Definition Of Business Analyticsmentioning

confidence: 99%

Section: Definition Of Business Analyticsmentioning

confidence: 99%

Section: Functionality Of Business Analyticsmentioning

confidence: 99%

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A systematic review on business analytics

Yin

Fernández

2020

JIEM

View full text Add to dashboard Cite

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“…Next is a discussion, by Hindle and Vidgen (2018), of their development of a new business analytics methodology (motivated by systems thinking, problem structuring, Community OR, data science and decision science) that enables people to develop a multidimensional view of business practice. The methodology is composed of a top-down analysis process that focuses on the business model of an organization (in this case a charity managing foodbanks), and it seeks to develop a business analytics portfolio.…”

Section: Theoretical and Methodological Innovationsmentioning

confidence: 99%

Community Operational Research: Innovations, internationalization and agenda-setting applications

Johnson

Midgley

Wright

et al. 2018

European Journal of Operational Research

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We are proud and excited to be introducing this special issue to you, for several reasons. First, the quantity, quality and diversity of the papers all exceeded our expectations. Second, for us, this collection of papers exemplifies what it is possible for OR to aspire to in terms of both benefit to society and academic publishing. In the context of this last comment, we want to say something about how we approached our editorial role, which is consistent with our normative vision of OR. The Editorial Process: Towards a Normative Vision of ORWe received 90 proposals of potential papers in response to our special issue callmany more than expected.

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“…Several papers in the CBOR literature are related to ours. For example, Hindle and Vigden (2017) adopt an action research framework to help a UK charity that runs a foodbank network develop their analytics strategy. They used a combination of geo-spatial data (specifically, open-source Census data on poverty and the charity's own data) to develop predictive demand models.…”

Section: Approaching the Problem Using A Cbor Approachmentioning

confidence: 99%

Efficient frontiers in a frontier state: Viability of mobile dentistry services in rural areas

Thorsen

McGarvey

2018

European Journal of Operational Research

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This study investigates the implications of adding mobile dentistry services to a community health center (CHC) in a rural area. CHCs are not-for-profit healthcare organizations which provide comprehensive primary care services to patients in the US, primarily for under-served and uninsured populations. We estimate the demand for the service in a five-county region in southwestern Montana, USA and work with stakeholders to determine a set of potential service locations. A mixed-integer optimization model is formulated to determine the frequency of stops in each location over a finite (six month) planning horizon with the goal of improving accessibility and availability of dental services while maintaining financial sustainability of the CHC. The financial considerations and social impact of offering a mobile dentistry service in southwestern Montana are assessed. Computational results based on a case study demonstrate the challenges facing mobile dentistry operations to increase access to under-served populations in a financially viable manner. Hybrid solutions, in which care is offered at a mix of fixed locations and mobile locations, appear to best balance the objectives of financial sustainability and expanded access to care.

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Developing a business analytics methodology: A case study in the foodbank sector

Cited by 65 publications

References 41 publications

A systematic review on business analytics

A systematic review on business analytics

Community Operational Research: Innovations, internationalization and agenda-setting applications

Efficient frontiers in a frontier state: Viability of mobile dentistry services in rural areas

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