Risk Management and Knowledge Management as Critical Success Factors of Sustainability Projects
- Advanced risk management of sustainability development projects.
- Utilization of knowledge management by employing post-project phases in the project life cycle.
2. Literature Review
3. Materials and Methods
- Is it possible that implementation of the principles of knowledge management through post-project phases contribute to improving the management of sustainability projects?
- What are the reasons for ignoring post-project phases?
- Is it possible that fuzzy approach application improves the risk evaluation process of sustainability projects?
- Fuzzification: translate input into truth values. Input variables are assigned degrees of membership in various classes.
- Fuzzy inference: compute output truth values. Inputs are applied to a set of “if–then” control rules.
- Defuzzification: transfer truth values into output. Fuzzy outputs are combined into discrete values needed to drive the control mechanism.
4.1. Knowledge Management: Post-Project Phases
4.2. Risk Management: Fuzzy Risk Quantification
Conflicts of Interest
- Nilsson, M.; Griggs, D.; Visbeck, M. Policy: Map the interactions between Sustainable Development Goals. Nat. News 2016, 534, 320. [Google Scholar] [CrossRef] [PubMed]
- Sustainability Projects for United States | Sustainable Measures. Available online: http://www.sustainablemeasures.com/projects/Sus/Sustainability/5 (accessed on 20 March 2018).
- Yu, M.; Zhu, F.; Yang, X.; Wang, L.; Sun, X. Integrating Sustainability into Construction Engineering Projects: Perspective of Sustainable Project Planning. Sustainability 2018, 10, 784. [Google Scholar] [CrossRef]
- Hardy-Vallee, B. The Cost of Bad Project Management. Business Journals, 7 February 2012. [Google Scholar]
- Lacko, B. Evaluation of Software Projects with Mta; Vsb-Tech University Ostrava: Ostrava, Czech Republic, 2012; ISBN 978-80-248-2669-1. [Google Scholar]
- Doležal, J. Prediction in Project Using Markov Chains. Available online: https://www.vutbr.cz/studenti/zav-prace?zp_id=34323 (accessed on 15 March 2018).
- Šviráková, E. Methods for Project Tracking in Creative Environment. Acta Inform. Pragensia 2017, 6, 32–59. [Google Scholar] [CrossRef]
- Rasheed, S.; Wang, C.; Lucena, B. Risk Leveling in Program Environments—A Structured Approach for Program Risk Management. Sustainability 2015, 7, 5896–5919. [Google Scholar] [CrossRef]
- Nguyen, L.H.; Watanabe, T. The Impact of Project Organizational Culture on the Performance of Construction Projects. Sustainability 2017, 9, 781. [Google Scholar] [CrossRef]
- Relich, M. A computational intelligence approach to predicting new product success. In Proceedings of the 11th International Conference on Strategic Management and Its Support by Information Systems, Uherske Hradiste, Czech Republic, 21–22 May 2015; pp. 142–150. [Google Scholar]
- Naeni, L.M.; Shadrokh, S.; Salehipour, A. A fuzzy approach for the earned value management. Int. J. Proj. Manag. 2011, 29, 764–772. [Google Scholar] [CrossRef]
- Schwalbe, K. Řízení projektů v IT: Kompletní průvodce; Vyd. 1; Computer Press: Brno, Czech Republic, 2011; ISBN 978-80-251-2882-4. [Google Scholar]
- McManus, J. Risk Management in Software Development Projects; Routledge: Abingdon, UK, 2012; ISBN 978-1-136-36791-5. [Google Scholar]
- Boehm, B.W. Software Risk Management: Principles and Practices. Nasirzadeh 1991, 8, 32–41. [Google Scholar] [CrossRef]
- Rudnik, K.; Deptula, A.M. System with probabilistic fuzzy knowledge base and parametric inference operators in risk assessment of innovative projects. Expert Syst. Appl. 2015, 42, 6365–6379. [Google Scholar] [CrossRef]
- Nasirzadeh, F.; Khanzadi, M.; Rezaie, M. Dynamic modeling of the quantitative risk allocation in construction projects. Int. J. Proj. Manag. 2014, 32, 442–451. [Google Scholar] [CrossRef]
- Liu, Z.-C.; Ye, Y. Models for comprehensive evaluating modeling of investment project risk with trapezoid fuzzy linguistic information. J. Intell. Fuzzy Syst. 2015, 28, 151–156. [Google Scholar] [CrossRef]
- Rodriguez, A.; Ortega, F.; Concepcion, R. A method for the evaluation of risk in IT projects. Expert Syst. Appl. 2016, 45, 273–285. [Google Scholar] [CrossRef]
- Zwikael, O.; Pathak, R.D.; Singh, G.; Ahmed, S. The moderating effect of risk on the relationship between planning and success. Int. J. Proj. Manag. 2014, 32, 435–441. [Google Scholar] [CrossRef]
- Doskocil, R.; Skapa, S.; Olsova, P. Success Evaluation Model for Project Management. E M Ekon. Manag. 2016, 19, 167–185. [Google Scholar] [CrossRef]
- RIPRAN—Metoda pro analýzu projektových rizik. Available online: http://ripran.eu/ (accessed on 30 January 2018).
- Ginevičius, T.; Kaklauskas, A.; Kazokaitis, P. Knowledge model for integrated construction project management. Bus. Theory Pract. 2011, 12, 162–174. [Google Scholar] [CrossRef]
- Matthies, B.; Coners, A. Double-loop learning in project environments: An implementation approach. Expert Syst. Appl. 2018, 96, 330–346. [Google Scholar] [CrossRef]
- Zhang, J.; Li, H.; Wang, S.H.-M. Analysis and Potential Application of the Maturity of Growth Management in the Developing Construction Industry of a Province of China: A Case Study. Sustainability 2017, 9, 143. [Google Scholar] [CrossRef]
- Janicek, P. Systems Conception of Problem-Solving. In Engineering Mechanics 2017; Acad Sci Czech Republic, Inst Thermomechanics: Prague, Czech Republic, 2017; pp. 402–405. ISBN 978-80-214-5497-2. [Google Scholar]
- Zadeh, L.A. Fuzzy sets. Inf. Control 1965, 8, 338–353. [Google Scholar] [CrossRef]
- Chen, S.-H.; Kaboudan, M.; Du, Y.-R. The Oxford Handbook of Computational Economics and Finance; Oxford University Press: Oxford, UK, 2018; ISBN 978-0-19-984437-1. [Google Scholar]
- Meyer, A.; Zimmermann, H.-J. Applications of Fuzzy Technology in Business Intelligence. Int. J. Comput. Commun. Control 2011, 6, 428–441. [Google Scholar] [CrossRef]
- Tripathy, B.K.; Sooraj, T.R.; Mohanty, R.K. A New Approach to Fuzzy Soft Set Theory and Its Application in Decision Making. In Computational Intelligence in Data Mining, Cidm, Vol 2; Behera, H.S., Mohapatra, D.P., Eds.; Springer-Verlag Berlin: Berlin, Germany, 2016; Volume 411, pp. 305–313. ISBN 978-81-322-2731-1. [Google Scholar]
- Zadeh, L.A. Fuzzy logic as the logic of natural languages. In Analysis and Design of Intelligent Systems Using Soft Computing Techniques; Melin, P., Castillo, O., Ramirez, E.G., Kacprzyk, J., Pedrycz, W., Eds.; Springer-Verlag Berlin: Berlin, Germany, 2007; Volume 41, pp. 1–2. ISBN 978-3-540-72431-5. [Google Scholar]
- Dostál, P. Advanced Decision Making in Business and Public Services, 1st ed.; Akademické nakladatelství CERM: Brno, Czech Republic, 2011; ISBN 978-80-7204-747-5. [Google Scholar]
- Doskočil, R. An evaluation of total project risk based on fuzzy logic. Bus. Theory Pract. 2016, 15, 23–31. [Google Scholar] [CrossRef]
- Fassinger, R.E.; Shullman, S.L.; Buki, L.P. Future Shock: Counseling Psychology in a VUCA World. Couns. Psychol. 2017, 45, 1048–1058. [Google Scholar] [CrossRef]
- Zandhuis, A. ISO21500: Guidance on Project Management—A Pocket Guide; Van Haren Publishing: Zaltbommel, The Netherlands, 2013; p. 51. [Google Scholar]
- Cooper, D.; Bosnich, P.; Grey, S.; Purdy, G.; Raymond, G.; Walker, P.; Wood, M. Project Risk Management Guidelines: Managing Risk with ISO 31000 and IEC 62198; Wiley: Hoboken, NJ, USA, 2014. [Google Scholar]
- ISO 10006:2017—Quality Management—Guidelines for Quality Management in Projects. Available online: https://www.iso.org/standard/70376.html (accessed on 20 April 2018).
- Kerzner, H.R. Project Management: A Systems Approach to Planning, Scheduling, and Controlling; John Wiley & Sons: Hoboken, NJ, USA, 2017; ISBN 978-1-119-16535-4. [Google Scholar]
- Ariely, D. The Upside of Irrationality: The Unexpected Benefits of Defying Logic at Work and at Home; HarperCollins: London, UK, 2010; ISBN 978-0-00-735479-5. [Google Scholar]
- McCrindle, M.; Wolfinger, E. The ABC of XYZ: Understanding the Global Generations; University of New South Wales Press: Sydney, Australia; London, UK, 2010; ISBN 978-1-74223-035-1. [Google Scholar]
- Simonin, B.L. The Importance of Collaborative Know-How: An Empirical Test of the Learning Organization. Acad. Manag. J. 1997, 40, 1150–1174. [Google Scholar] [CrossRef]
|Rudnik, Deptula ||The probabilistic fuzzy risk assessment model for the innovative project. The linguistic risk variables are the inputs to the model. The shapes of fuzzy sets for linguistic values are identified using expert knowledge. Fuzzy rules (if–then), probability measures of fuzzy events, and conclusion of rules are the knowledge.|
|Nasirzadeh et al. ||The integrated dynamic fuzzy model for quantitative allocation of construction risks between owners and contractors. Fuzzy logic and system dynamics approach was used for modelling of all the factors affecting the risk allocation process. The values of key uncertainty factors were described using fuzzy numbers. The project cost is simulated at different percentages of risk allocation, thanks to the model.|
|Liu, Ye ||The procedure for multiple attribute decision making based on the trapezoid fuzzy linguistic weighted Bonferroni mean (TFLWBM) operator was developed. The procedure was verified for evaluating the investment project risk of the case study.|
|Rodriguez et al. ||The multicriteria risk assessment model based in the fuzzy inference system (FIS) and fuzzy analytic hierarchy process (FAHP). FIS was used for the integration of the areas of risk factors. FAHP was used for evaluation of the risk factors. The fuzzy model includes the different levels of uncertainty and the relationship among risk factor areas.|
|Zwikael et al. ||The research of the relationship between a project planning process and project success. The level of project success is measured in the form of project plan risks. They suggest the careful planning for high-risk projects.|
|Doskočil et al. ||The expert fuzzy model is used for project success evaluation. It is a hierarchical fuzzy model that evaluates project success in terms of project status, project risk, and project quality assessment. They recommend applying the model in particular in the implementation stage and then repeatedly after the completion of each project stage. Thanks to the model, the project manager and the project team members have a tool to support their decision making, which also enables them to implement the respective measures relatively in time, which contributes to more efficient project management.|
|Ginevičius et al. ||The project management knowledge model is used to analyse the economic, legal, technical, technological, organizational, social, cultural, political, ethical, and psychological factors in a comprehensive way. According to the authors, the above factors affect the project as such and their application contributes to increased competitiveness.|
|Matthies, Coners ||The semiautomated implementation approach for double-loop learning in project environments. A combined application of two complementary methods is suggested for this purpose: latent semantic analysis (LSA) and analytic network process (ANP). This way, the approach addresses two problems of the project management practice: Firstly, the information overload in project environments, where the LSA is used for the semiautomated extraction of lessons learned from large collections of textual project documentation. Secondly, the lack of procedures and methods for the practical implementation of available project knowledge, where the ANP is used for the systematic modelling of extracted lessons learned and their integration into the evaluation of project concepts and current project management routines|
|Zhang, Wang ||The study investigates the level of construction industry maturity in Shaanxi. The authors state that the maturity is in its early stage (level two of four) and faces several critical factors. As the main issue, they mention the low level of transformation and insufficient knowledge management within construction projects. Their recommendations include, for example, the need to increase the stress on company vision target realization in the development strategies of their enterprises, increased education, research and development budget, and implementation of standardized project management.|
|1||Excited by the success of a completed project, the workers start to feel there is no need to analyse or improve anything.|
|2||Devastated by the project failure, the project participants and all the stakeholders try to forget the project as fast as possible.|
|3||Under the load of more and more new projects and everyday issues, there is no time for such an analysis.|
|4||Since any possible “easy and possible financial savings” are made in the already tight project budgets, the post-implementation analysis is usually one of them, so it is not even planned.|
|5||Such a thing is considered unnecessary pondering and obstruction to proper work.|
|6||There is a worry among the project team members that even well-intended, (self-) critical conclusions may turn against them (e.g., reduction of project remuneration).|
|7||People do not know how to perform it practically, so they prefer not to do it.|
|8||The analysis was done once but the recommendations were put aside ad acta, so the whole thing inevitably seemed to have been a waste of time and considerable efforts, and so nobody wants to risk needless work.|
|9||Unlike the project execution, it is often not explicitly required; so it is not done!|
|10||The project team does not want to point out mistakes they have made (why should they?), and pointing out success, on the other hand, is considered boasting.|
|11||Since the workers do it wrong, the results are not satisfactory, so after some time, the activity is discontinued due to “inefficiency”.|
|12||The analytical teams are repeatedly comprised of incompetent staff members, so the results do not correspond to the expended resources or time and the analysis is cancelled.|
|13||Because its need and existence are essentially denied or ignored. (This belongs to “quality”, not to “projects”.)|
|14||Most companies lack a system of company experience accumulation, so it is not required for projects either. (Must be required by company top management.)|
|15||In the Czech Republic, many people consider themselves to be very smart and believe they do everything right and do not need to learn anything anymore.|
|16||There are still many people who remember a document titled “Lessons Learnt from Critical Development...” which did not bring success to its authors! (Generation-specific and Czech-specific reason.)|
|17||In the chaos and hurry of everyday work on the project, it simply gets forgotten.|
|18||A lot of people often refuse to look back, they only want to look ahead—a common attitude of many young people. (There is not so much time in their past, but a relatively long time in their future.)|
|19||There is no project documentation, and sometimes there are no project participants anymore, so the question is what in particular should be responsibly analysed.|
|20||A number of project management methodology materials still do not mention these phases, as well as pre-project phases, and focus solely on immediate project management, from start to completion.|
|21||The current time is VUCA (volatile, uncertain, complex, ambiguous). Therefore, it makes no sense to prepare for anything by analysing the past. Everything will be different and nothing can be predicted and no past experience can be used.|
|Impact on the Project (IP)|
|Probability (P)||VH||RV = VH||RV = VH||RV = VH||RV = H||RV = M|
|H||RV = VH||RV = VH||RV = H||RV = M||RV = L|
|M||RV = VH||RV = H||RV = M||RV = L||RV = VL|
|L||RV = H||RV = M||RV = L||RV = VL||RV = VL|
|VL||RV = M||RV = L||RV = VL||RV = VL||RV = VL|
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Doskočil, R.; Lacko, B. Risk Management and Knowledge Management as Critical Success Factors of Sustainability Projects. Sustainability 2018, 10, 1438. https://doi.org/10.3390/su10051438
Doskočil R, Lacko B. Risk Management and Knowledge Management as Critical Success Factors of Sustainability Projects. Sustainability. 2018; 10(5):1438. https://doi.org/10.3390/su10051438Chicago/Turabian Style
Doskočil, Radek, and Branislav Lacko. 2018. "Risk Management and Knowledge Management as Critical Success Factors of Sustainability Projects" Sustainability 10, no. 5: 1438. https://doi.org/10.3390/su10051438