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Big data in humanitarian supply chain management: a review and further research directions

  • Applications of OR in Disaster Relief Operations, Part II
  • Published:
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Abstract

Humanitarian organizations work diligently to save lives using scarce resources, competing for donor money, and operating in complex environments. It is no surprise that they need information to effectively execute their task. As there have been tremendous developments in data analytics it is imperative that the domain of humanitarian supply chain management leverage the benefits offered by the advancement of big data. In this study, we have conducted a systematic literature review in the field of big data and humanitarian supply chain. The data was collected using Scopus which is the largest digital database. After careful screening, only 28 journal papers were selected for literature review. These papers have been classified and grouped into various categorizations. Future research directions in this field have been suggested that are based on various organizational theories.

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Fig. 1

Source: Author’s compilation

Fig. 2

Source: Author’s compilation

Fig. 3

Source: Author’s compilation

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Author information

Authors and Affiliations

  1. Montpellier Research in Management, Montpellier Business School, 2300 Avenue des Moulins, 34080, Montpellier, France

    Shivam Gupta

  2. Department of Management and Entrepreneurship, Driehaus College of Business, DePaul University, 1 East Jackson Blvd. DePaul Center 7100, Chicago, IL, 60604, USA

    Nezih Altay

  3. Department of Computer Science and Engineering, Southern University of Science and Technology of China, 1088 Xueyuan Blvd, Nanshan District, Shenzhen, 518055, Guangdong, China

    Zongwei Luo

Authors
  1. Shivam Gupta
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  2. Nezih Altay
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  3. Zongwei Luo
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Corresponding author

Correspondence to Zongwei Luo.

Appendices

Appendix A

Source title versus year.

Source title

2005

2008

2009

2010

2012

2013

2014

2015

2016

2017

Total

ACM transactions on embedded computing systems

        

1

 

1

Advances in intelligent and soft computing

   

1

      

1

Annals of operations research

        

1

 

1

Applied geography

    

1

     

1

Automation in construction

  

1

       

1

Big data

        

1

 

1

Computers and geosciences

   

1

      

1

CyberGEO

 

1

        

1

Environmental earth sciences

       

1

  

1

Eos

    

1

     

1

Expert systems with applications

        

1

 

1

IEEE Cloud computing

      

1

   

1

IEEE Transactions on smart grid

       

1

  

1

Intelligent automation and soft computing

         

2

2

International journal of critical infrastructures

1

       

1

 

2

International journal of digital earth

     

1

    

1

International journal of emergency management

       

1

  

1

Journal of applied remote sensing

  

1

       

1

Journal of cleaner production

         

1

1

Journal of decision systems

        

1

 

1

Journal of humanitarian logistics and supply chain management

        

1

 

1

Proceedings of the IEEE

        

1

 

1

Science China information sciences

       

1

  

1

SpringerPlus

        

1

 

1

Transportation research record

        

1

 

1

Wiley interdisciplinary reviews: Computational statistics

    

1

     

1

Total

1

1

2

2

3

1

1

4

10

3

28

Appendix B

Classification of literature (from Fig. 4).

Theory building

Application based research

Rationalist approach

Alternative methods

Cases

Theory building

Theory building and Theory testing

Critical review

Conceptual Framework through Survey

Industry focused research/ Survey to explore the existing scenario

Amaye et al. (2016)

Papadopoulos et al. (2017), Prasad et al. (2016), Puthal et al. (2016)

Dusse et al. (2016), Wybo et al. (2015), Yang et al. (2013)

Bealt et al. (2016), Gao et al. (2008), Houghton et al. (2012), Ofli et al. (2016), Onorati and Diaz, (2016), Sarkar et al. (2014), Schultz, (2012), Thekdi and Joshi (2016), Wang et al. (2015), Wang et al. (2017), Zhu et al. (2016)

Chi et al. (2016), El-Anwar et al. (2009), El-Askary et al. (2012), Fan et al. (2009), Kulawiak et al. (2010), Lu et al. (2005), Lu et al. (2015), Ma and Zhang (2017), Moran et al. (2010), Onyewuchi et al. (2015)

Fig. 4
figure 4

Source: Author’s compilation

Classification of literature.

Full size image

Appendix C

Enablers of big data and Humanitarian supply chain.

Enablers for big data and humanitarian supply chain

Measures

References

Volume

(1) Scale of data, (2) Stored data, (3) Continuous creation of data

Chi et al. (2016), Dusse et al. (2016), El-Askary et al. (2012), Fan et al. (2009), Gao et al. (2008), Kulawiak et al. (2010), Lu et al. (2005), Lu et al. (2015), Ma and Zhang (2017), Ofli et al. (2016), Onorati and Diaz (2016), Onyewuchi et al. (2015), Papadopoulos et al. (2017), Prasad et al. (2016), Puthal et al. (2016), Sarkar et al. (2014), Schultz (2012), Thekdi and Joshi (2016), Wang et al. (2015), Wang et al. (2017), Wybo et al. (2015), Yang et al. (2013), Zhu et al. (2016)

Variety

(1) Data variety, (2) Interoperability

Amaye et al. (2016), Chi et al. (2016), El-Askary et al. (2012), Gao et al. (2008), Houghton et al. (2012), Kulawiak et al. (2010), Lu et al. (2005), Lu et al. (2015), Ofli et al. (2016), Onorati and Diaz, (2016), Papadopoulos et al. (2017), Prasad et al. (2016), Puthal et al. (2016), Sarkar et al. (2014), Thekdi and Joshi (2016), Wang et al. (2015), Wang et al. (2017), Wybo et al. (2015), Yang et al. (2013), Zhu et al. (2016)

Velocity

(1) Speed of data generation, (2) Data processing time, (3) Data transmission time

Chi et al. (2016), Gao et al. (2008), Houghton et al. (2012), Kulawiak et al. (2010), Lu et al. (2015), Ofli et al. (2016), Onorati and Diaz (2016), Papadopoulos et al. (2017), Prasad et al. (2016), Puthal et al. (2016), Sarkar et al. (2014), Thekdi and Joshi (2016), Wang et al. (2015), Wybo et al. (2015)

Veracity

(1) Quality of information, (2) Accuracy of data, (3) Reliability, (4) Accessibility

Amaye et al. (2016), Fan et al. (2009), Gao et al. (2008), Houghton et al. (2012), Lu et al. (2005), Lu et al. (2015), Ofli et al. (2016), Onorati and Diaz (2016), Papadopoulos et al. (2017), Prasad et al. (2016), Puthal et al. (2016), Sarkar et al. (2014), Thekdi and Joshi (2016), Wang et al. (2015), Wybo et al. (2015)

Organizational mindfulness

(1) Preoocupation with failure, (2) Reluctance to simplify interpretations, (3) Senstivity to operations, (4) Commitment to resilience, (5) Deference to expertise

Amaye et al. (2016), El-Anwar et al. (2009), Ma and Zhang (2017), Moran et al. (2010), Papadopoulos et al. (2017), Prasad et al. (2016), Thekdi and Joshi (2016), Zhu et al. (2016)

Concerns for big data in humanitarian supply chain.

Concerns for big data in humanitarian supply chain

Measures

References

Humanitarian logistics

(1) Identification of logistics service providers, (2) Collaboration between agencies during humanitarian operations, (3) Response time of logistics agencies/organizations

Bealt e al. (2016), Gao et al. (2008), Papadopoulos et al. (2017), Prasad et al. (2016), Thekdi and Joshi (2016), Zhu et al. (2016)

Remote sensing

(1) Real time rendering of disaster location, (2) High-speed buffering mechanisms, (3) Large-scale 3D model visualization

Fan et al. (2009), Gao et al. (2008), Houghton et al. (2012), Kulawiak et al. (2010), Lu et al. (2015), Ofli et al. (2016), Wang et al. (2015)

Information security

(1) Privacy and confidentiality of data, (2) Encryption, (3) Accountability, (4) Maintenance

Gao et al. (2008), Houghton et al. (2012), Kulawiak et al. (2010), Lu et al. (2015), Moran et al. (2010), Onorati and Diaz (2016), Papadopoulos et al. (2017), Prasad et al. (2016), Puthal et al. (2016), Wang et al. (2015), Yang et al. (2013)

Social media (SM)

(1) Diffusion of information by SM, (2) Validation of information, (3) Support coordination and collaboration among agencies

Amaye et al. (2016); Ma and Zhang (2017), Ofli et al. (2016), Onorati and Diaz (2016), Papadopoulos et al. (2017), Sarkar et al. (2014), Wang et al. (2015), Wang et al. (2017), Wybo et al. (2015)

Appendix D

Papers Considered for Literature Review

  • Amaye, A., Neville, K., & Pope, A. (2016). Big Promises: Using organisational mindfulness to integrate big data in emergency management decision making. Journal of Decision Systems, 25, 76–84. https://doi.org/10.1080/12460125.2016.1187419.

  • Bealt, J., Fernández Barrera, J. C., & Mansouri, S. A. (2016). Collaborative relationships between logistics service providers and humanitarian organizations during disaster relief operations. Journal of Humanitarian Logistics and Supply Chain Management, 6(2), 118–144. https://doi.org/10.1108/JHLSCM-02-2015-0008.

  • Chi, M., Plaza, A., Benediktsson, J. A., Sun, Z., Shen, J., & Zhu, Y. (2016). Big data for remote sensing: Challenges and opportunities. Proceedings of the IEEE, 104(11), 2207–2219. https://doi.org/10.1109/JPROC.2016.2598228.

  • Dusse, F., Júnior, P. S., Alves, A. T., Novais, R., Vieira, V., & Mendonça, M. (2016). Information visualization for emergency management: A systematic mapping study. Expert Systems with Applications, 45, 424–437. https://doi.org/10.1016/j.eswa.2015.10.007.

  • El-Anwar, O., El-Rayes, K., & Elnashai, A. (2009). An automated system for optimizing post-disaster temporary housing allocation. Automation in Construction, 18(7), 983–993. https://doi.org/10.1016/j.autcon.2009.05.003.

  • El-Askary, H., Allali, M., Rakovski, C., Prasad, A., Kafatos, M., & Struppa, D. (2012). Computational methods for climate data. Wiley Interdisciplinary Reviews: Computational Statistics, 4(4), 359–374. https://doi.org/10.1002/wics.1213.

  • Fan, X., Du, X., Tan, J., & Zhu, J. (2009). Three-dimensional visualization simulation assessment system based on multi-source data fusion for the wenchuan earthquake. Journal of Applied Remote Sensing, 3(1), 1–9. https://doi.org/10.1117/1.3154425.

  • Gao, S., Mioc, D., Yi, X., Anton, F., Oldfield, E., & Coleman, D. J. (2008). The Canadian geospatial data infrastructure and health mapping. CyberGEO, pp. 434.

  • Houghton, A., Prudent, N., Scott, J. E., Wade, R., & Luber, G. (2012). Climate change-related vulnerabilities and local environmental public health tracking through GEMSS: A web-based visualization tool. Applied Geography, 33(1), 36–44. https://doi.org/10.1016/j.apgeog.2011.07.014.

  • Kulawiak, M., Prospathopoulos, A., Perivoliotis, L., Łuba, M., Kioroglou, S., & Stepnowski, A. (2010). Interactive visualization of marine pollution monitoring and forecasting data via a web-based GIS. Computers and Geosciences, 36(8), 1069–1080. https://doi.org/10.1016/j.cageo.2010.02.008.

  • Lu, C., Sripada, L. N., Shekhar, S., & Liu, R. (2005). Transportation data visualisation and mining for emergency management. International Journal of Critical Infrastructures, 1(2-3), 170–194. https://doi.org/10.1504/IJCIS.2005.006118.

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Gupta, S., Altay, N. & Luo, Z. Big data in humanitarian supply chain management: a review and further research directions. Ann Oper Res 283, 1153–1173 (2019). https://doi.org/10.1007/s10479-017-2671-4

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