SMART Criteria for Quality Assessment of Key Performance Indicators Used in the Oil and Gas Industry

doi:10.23940/ijpe.20.07.p2.9991007

Abstract

Abstract: SMART criteria, referring to the acronym for the terms 'specificity', 'measurability', 'achievability', 'relevancy', and 'time-based', are commonly used as a basis for the assessment of the quality of key performance indicators (KPIs). In this article, we discuss whether it is appropriate to use these criteria for this purpose. We conclude that all the SMART criteria should be satisfied if the KPI is to be regarded as of high quality and useful for business improvement, but, in addition, there is a particular need to include the criterion 'manageability' in the assessment. Without the inclusion of this criterion, the assessment of KPI quality could be misleading. An example is used for illustrative purposes. Although our starting point is KPIs for reliability and maintenance in the oil and gas industry, the discussions are also applicable to other industries.

Key words: key performance indicators, KPI, SMART, criteria, quality, management, reliability, maintenance, oil and gas

Jon Tømmerås Selvik, Ian Stanley, and Eirik Bjorheim Abrahamsen. SMART Criteria for Quality Assessment of Key Performance Indicators Used in the Oil and Gas Industry [J]. Int J Performability Eng, 2020, 16(7): 999-1007.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References 37

1.	ISO 41011, “Facility Management — Vocabulary,” 2017
2.	ISO 14224, “Petroleum, Petrochemical and Natural Gas Industries — Collection and Exchange of Reliability and Maintenance Data for Equipment,” 2016
3.	EN 15341, “Maintenance - Maintenance Key Performance Indicators,” 2007
4.	T. Kongsvik, S. Å. K.Johnsen, and S. Sklet, “Safety Climate and Hydrocarbon Leaks: An Empirical Contribution to the Leading-Lagging Indicator Discussion,”Journal of Loss Prevention in the Process Industries, Vol. 24, No. 4, pp. 405-411, 2011
5.	S. C. Payne, M. E. Bergman, J. M. Beus, J. M. Rodríguez,J. B. Henning, “Safety Climate: Leading or Lagging Indicator of Safety Outcomes?”Journal of Loss Prevention in the Process Industries, Vol. 22, No. 6, pp. 735-739, 2009
6.	N. Tamim, D. M. Laboureur, R. A. Mentzer, A. R. Hasan,M. S. Mannan, “A Framework for Developing Leading Indicators for Offshore Drillwell Blowout Incidents,”Process Safety and Environmental Protection, Vol. 106, pp. 256-262, 2017
7.	R. Smith and R. K. Mobley, “Chapter 6 - Key Performance Indicators,”Rules of Thumb for Maintenance and Reliability Engineers, pp. 89-106, Butterworth-Heinemann, 2008
8.	W. Peng, T. Sun, P. Rose,T. Li, “A Semi-Automatic System with an Iterative Learning Method for Discovering the Leading Indicators in Business Processes,” inProceedings of the 2007 International Workshop on Domain Driven Data Mining, ACM,. San Jose, CA, USA, 2007
9.	M. Badawy, A. A.Abd El-Aziz, A.M. Idress, H. Hefny, and S. Hossam, “A Survey on Exploring Key Performance Indicators,”Future Computing and Informatics Journal, Vol. 1, No. 1-2, pp. 47-52, 2016
10	R. S.Kaplan and D. P. Norton, “The Balanced Scorecard: Translating Strategy into Action,” Harvard Business Review Press, Boston, MA, USA, 1996
11	M. Vukomanovic and M. Radujkovic, “The Balanced Scorecard and EFQM Working Together in a Performance Management Framework in Construction Industry,”Journal of Civil Engineering and Management, Vol. 19, No. 5, pp. 683-695, 2013
12	A. Parida and U. Kumar, “Maintenance Performance Measurement (MPM): Issues and Challenges,”Journal of Quality in Maintenance Engineering, Vol. 12, No. 3, pp. 239-251, 2006
13	G. T. Doran, “There's a S.M.A.R.T. Way to Write Management's Goals and Objectives,”Management Review, Vol. 70, pp. 25-36, 1981
14	Better Regulation Task Force (BRTF), “Principles of Good Regulation,” Cabinet Office, London, 2000
15	S. Kaganski and S. Toompalu, “Development of Key Performance Selection Index Model,”Journal of Achievements in Materials and Manufacturing Engineering, Vol. 82, No. 1, pp. 33-40, 2017
16	K. B.Lawlor and M. J. Hornyak, “SMART Goals: How the Application of SMART Goals can Contribute to Achievement of Student Leaning Outcomes,”Developments in Business Simulation and Experiential Learning, Vol. 39, pp. 259-267, 2012
17	R. Kaufman, H. Oakley-Browne, R. Watkins,D. Leigh, “Strategic Planning for Success: Aligning People, Performance, and Payoffs,” John Wiley and Sons, Inc., San Francisco, CA, 2003
18	F. Galligan, T. Barry, D. Crawford, D. Howe, C. Maskery, A. Ruston, et al., “Advanced PE for Edexcel Student Book,” Heinemann Educational Publishers, Oxford, UK, 2000
19	19.RapidBI, “Writing SMARTer Objectives and Goals,” (https://rapidbi.com/writesmartobjectives/ [Uploaded 2 May 2016])
20	D. Kavanagh, “Advantage - A Roadmap for Entrepreneurs and Leaders in The Digital Age,” Kindle edition, Declan Kavanagh, (http://intelligentorg.com/wp-content/uploads/2013/11/@Note-10-SMART-squared.pdf, 2013
21	R. B.Bratvold and S. H. Begg, “Making Good Decisions,” Society of Petroleum Engineers, Richardson, TX, 2010
22	K. Øien, I. B. Utne,I. A. Herrera, “Building Safety Indicators: Part 1 - Theoretical Foundation,”Safety Science, Vol. 49, No. 2, pp. 148-161, 2011
23	N. Leveson, “A Systems Approach to Risk Management through Leading Safety Indicators,”Reliability Engineering and System Safety, Vol. 136, pp. 17-34, 2015; 136
24	P. Swuste, J. Theunissen, P. Schmitz, G. Reniers,P. Blokland, “Process Safety Indicators, A Review of Literature,”Journal of Loss Prevention in the Process Industries, Vol. 40, pp. 162-173, 2016
25	P. Hokstad, J. Vatn, T. Aven,M. Sørum, “Use of Risk Acceptance Criteria in Norwegian Offshore Industry: Dilemmas and challenges,”Risk Decision and Policy, Vol. 9, No. 3, pp. 193-206, July 2004
26	T. Aven and J. E. Vinnem, “On the Use of Risk Acceptance Criteria in the Offshore Oil and Gas ndustry,”Reliability Engineering and System Safety, Vol. 90, pp. 15-24, 2005
27	R. B. Bratvold, J. E. Bickel,H. P. Lohne, “Value of Information in the Oil and Gas Industry: Past, Present and Future,”SPE Reservoir Evaluation and Engineering, Vol. 12, No. 4, 2007
28	J. T. Selvik, H. P. Lohne,T. Aven, “On the Use of Value of Information Measure in Decision Making - A Drilling Jar Case,” inProceedings of the European Safety and Reliability Conference 2011(ESREL 2011), pp. 217-223, Troyes, France, September 18-22, 2011, Taylor and Francis Group, 2012
29	T. L. Saaty, “The Analytic Hierarchy Process,” McGraw-Hill Inc., 1980
30	R. M.Elhuni and M. M. Ahmad, “Key Performance Indicators for Sustainable Production Evaluation in Oil and Gas Sector,”Procedia Manufacturing, Vol. 11, pp. 718-724, 2017
31	A. Parida and G. Chattopadhyay, “Development of a Multi-Criteria Hierarchal Framework for Maintenance Performance Measurement (MPM),”Journal of Quality in Maintenance Engineering, Vol. 13, No. 3, pp. 241-258, 2007
32	K. Smart and K. Blakey, “Achieving Maintenance Excellence in Maersk Oil Qatar,” inProceedings of International Petroleum Technology Conference, 2014
33	M. Jansen, “Shell Maintenance Excellence,” inProceedings of Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, UAE, 9-12 November, 2015
34	D. A. Wood, “Asset Portfolio Multi-Objective Optimization Tools Provide Insight to Value, Risk and Strategy for Gas and Oil Decision Makers,”Journal of Natural Gas Science and Engineering, Vol. 33, pp. 196-216, 2016
35	A. E. B.Longhi, A. A. Pessoa, and P. A. de Almada Garcia, “Multiobjective Optimization of Strategies for Operation and Testing of Low-Demand Safety Instrumented Systems using a Generic Algorithm and Fault Trees,”Reliability Engineering and System Safety, Vol. 142, pp. 525-538, 2015
36	Solomon Associates, “Intellectual property,”(https://www.solomononline.com/intellectual-property, Accessed 14.08.2018)
37	ISO 20815, “Petroleum, Petrochemical and Natural Gas Industries — Production Assurance and Reliability Management,” 2018

[1]	Kavita Pandey, and Dhiraj Pandey. Real-Time Crop Disease Detection and Remedial Suggestion through Deep Learning-based Smartphone Application [J]. Int J Performability Eng, 2023, 19(8): 491-498.
[2]	Yassine Eddouh, Abdelmajid Daya, Rabie Elotmani, and Abdelhamid Touache. Optimization Approaches for Cost Reduction in Preventive Maintenance Strategies: A Comparative Study [J]. Int J Performability Eng, 2023, 19(6): 359-367.
[3]	Yassine Eddouh, Abdelmajid Daya, Rabie Elotmani, and Abdelhamid Touache. Imperfect Maintenance Model for Optimizing Air Compressor Availability [J]. Int J Performability Eng, 2023, 19(4): 263-272.
[4]	Sidali Bacha and Ahmed Bellaouar. Assessment of the Effectiveness of Maintenance Actions and the Influence of Covariates on the Reliability of Gas Turbines using the Extended Generalized Proportional Intensity Model [J]. Int J Performability Eng, 2023, 19(4): 283-290.
[5]	Amit Kumar Singh and P. C. Tewari. An Overview of Reliability, Availability, Maintainability, and Safety Strategies for Complex Systems in Various Process Industries [J]. Int J Performability Eng, 2023, 19(12): 788-796.
[6]	Om Prakash Bohrey and A. S. Chatpalliwar. Optimization of Preventive Maintenance to Maximize the Availability of Aircraft under Resource Constraints [J]. Int J Performability Eng, 2023, 19(11): 762-770.
[7]	Yonghua Li, Shujian Liu, Xiaoning Bai, and Yufeng Wang. An Interval-Probability Hybrid Structural Reliability Calculation Method Based on CSSA-BR-BP [J]. Int J Performability Eng, 2023, 19(10): 687-699.
[8]	Jinxin Wang, Zhiping Zhai, Yuezheng Lan, Xiaoyi Zhai, and Lixiang Zhao. Reliability Analysis and Optimization of Forage Crushers Based on Bayesian Network [J]. Int J Performability Eng, 2023, 19(10): 700-709.
[9]	Darius Muyizere, Lawrence K. Letting, and Bernard B. Munyazikwiye. Effect on Transient Stability and Analyses Resulting from a Cyber-Attack on Frequency Relay Device [J]. Int J Performability Eng, 2023, 19(1): 20-32.
[10]	Linda Bouyaya and Rachid Chaib. Maintenance and Reliability of a Motor Pump: A Case Study [J]. Int J Performability Eng, 2022, 18(8): 559-569.
[11]	Kashif Alia, Sajjad Mubina, and Ekatrina Gavrishyk. Evaluating Quality Management System of Construction Projects [J]. Int J Performability Eng, 2022, 18(7): 492-501.
[12]	Divya Singhal, Laxmi Ahuja, and Ashish Seth. An Insight into Combating Security Attacks for Smart Grid [J]. Int J Performability Eng, 2022, 18(7): 512-520.
[13]	Hongjing Deng, Xuan Zhang, Jiahao Jiang, Jie Wang, and Hexiang Huang. Privacy Protection of Personal Education Information on Blockchain [J]. Int J Performability Eng, 2022, 18(5): 317-328.
[14]	Yu-Lun Chao, Yi-Kuei Lin, and Cheng-Ta Yeh. AMC-Based Algorithm for Network Reliability Evaluation of a Manufacturing System with Scrapping and Rework [J]. Int J Performability Eng, 2022, 18(4): 231-240.
[15]	Zhihong Liang, Na Du, Yuxiang Huang, Kai Liu, and Zhichang Guo. Performance Improvement Scheme of Blockchain Consensus for Supply Chain [J]. Int J Performability Eng, 2022, 18(3): 158-166.