Development of Fuzzy Data Envelopment Risk Analysis Applied on Auditory Ergonomics for Call Center Agents in the Philippines

  • Erika Mae Go
  • Karl Benedict Ong
  • Jayne Lois San JuanEmail author
  • Wendy Gail Sia
  • Rendell Heindrick Tiu
  • Richard Li
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 827)


Most contact center employees experience work related injuries, leading to decreased productivity and performance. The increased risk is due to poor workstation design, such as indecent noise level and duration exposure experienced by call center agents daily, while receiving and making calls on a headset or telephone. Some illnesses caused by prolonged exposure to unfavorable acoustics are headaches, increased anxiety levels, tinnitus, and noise-induced hearing loss. Mathematical modelling has only been applied in optimizing systems considering musculoskeletal disorders and only in other industries. Thus, it is important to consider the auditory ergonomic risks faced by call center agents mathematically. This study proposes Fuzzy Data Envelopment Risk Analysis (FDERA), a DEA-based risk analysis tool that considers the presence of imprecise data. The validity of the model is demonstrated using a case example. The results of the proposed tool are relative risk efficiency scores for each agent. Guidelines for interventions to improve risk efficiencies are presented using a matrix that provides possible preventive and corrective measures to address risks.


Fuzzy Data Envelopment Risk Analysis Auditory ergonomics Call center agents 


  1. 1.
    Errighi, Khatiwada, Bodwell (2016) Business process outsourcing in the Philippines: Challenges for decent work. ILO Asia- Pacific Working Paper SeriesGoogle Scholar
  2. 2.
    Norman: Call centre work – characteristics, physical, and psychosocial exposure, and health related outcomes (2005)Google Scholar
  3. 3.
    OSHA (2000) Ergonomics: The Study of Work.
  4. 4.
    Lan L, Wargocki P, Lian Z (2011) Quantitative measurement of productivity loss due to thermal discomfort. Energy Build 43(5):1057–1062CrossRefGoogle Scholar
  5. 5.
    Kurakata K, Mizunami T, Matsushita K (2007) Accessible design in auditory ergonomics, p 1–7Google Scholar
  6. 6.
    Tiesler G, Oberdoerster M (2008) Noise - a stress factor? acoustic ergonomics at schools. J Acoustical Soc Am 123(5):3918CrossRefGoogle Scholar
  7. 7.
    Beyan A, Demiral Y, Cimrin A, Ergor A (2016) Call centers and noise-induced hearing loss. Noise Health 18(81):113CrossRefGoogle Scholar
  8. 8.
    Choi G (2009) A goal programming mixed-model line balancing for processing time and physical workload. Comput Ind Eng 57(1):395–400CrossRefGoogle Scholar
  9. 9.
    Xu Z (2010) Design of Assembly Lines with the Concurrent Consideration of Productivity and Upper Extremity Musculoskeletal Disorders using Linear Models (Master’s thesis)Google Scholar
  10. 10.
    Mummolo G, Mossa G, Boenzi F, Digiesi S (2015) Productivity and ergonomic risk in human based production systems: a job-rotation scheduling model. Int J Prod Econ 171:471–477Google Scholar
  11. 11.
    Chang S, Chen T (2006) Discriminating relative workload level by data envelopment analysis. Int J Ind Ergon 36(9):773–778CrossRefGoogle Scholar
  12. 12.
    Azadeh A, Motevali Haghighi S, Gaeini Z, Shabanpour N (2016) Optimization of healthcare supply chain in context of macro-ergonomics factors by a unique mathematical programming approach. Appl. Ergon 55:46–55CrossRefGoogle Scholar
  13. 13.
    Ramanathan R (2001) Comparative risk assessment of energy supply technologies: a data envelopment analysis approach. Energy 26(2):197–203CrossRefGoogle Scholar
  14. 14.
    Rougier J, Rougier J, Hill LJ, Sparks RS (2013) Risk and uncertainty assessment for natural hazards. Cambridge University Press, New YorkCrossRefGoogle Scholar
  15. 15.
    Azadeh A, Alem S (2010) A flexible deterministic, stochastic and fuzzy Data Envelopment Analysis approach for supply chain risk and vendor selection problem: Simulation analysis. Expert Syst Appl 37(12):7438–7448CrossRefGoogle Scholar
  16. 16.
    Project Management Institute Inc.: A guide to the project management body of knowledge (PMBOK® guide) (2000)Google Scholar
  17. 17.
    Chapman C, Ward S (2004) Why risk efficiency is a key aspect of best practice projects. Int J Project Manag 22(8):619–632CrossRefGoogle Scholar
  18. 18.
    Yang H, Pollitt M (2009) Incorporating both undesirable outputs and uncontrollable variables into DEA: the performance of Chinese coal-fired power plants. Eur J Oper Res 197(3):1095–1105CrossRefGoogle Scholar
  19. 19.
    Puri J, Yadav SP (2014) A fuzzy DEA model with undesirable fuzzy outputs and its application to the banking sector in India. Expert Syst Appl 41(14):6419–6432CrossRefGoogle Scholar
  20. 20.
    Golmohammadi R, Eshaghi M, Khoram MR (2011) Fuzzy Logic Method for Assessment of Noise Exposure Risk in an Industrial Workplace 3(2):49–55Google Scholar
  21. 21.
    Pawlaczyk-Łuszczyńska M, Dudarewicz A, Zamojska-Daniszewska M, Rutkowska-Kaczmarek P, Zaborowski K (2017) Noise exposure and hearing threshold levels in call center operators. ICBENGoogle Scholar
  22. 22.
    DisMark Tinnitus (2017) Tinnitus hearing-test - Sound therapy helps against tinnitus, ringing in the ears. Accessed 2017/12/11

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Industrial Engineering DepartmentDe La Salle UniversityManilaPhilippines

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