A Proposition for Combining Rough Sets, Fuzzy Logic and FRAM to Address Methodological Challenges in Safety Management: A Discussion Paper
Abstract
:1. Introduction
2. Background and Motivation
2.1. The Evolution of Systems’ Analysis: An Argument for Adopting a Systemic Perspective
 There is always an underspecification of actual performance conditions, i.e., the difference between WAI and WAD.
 The principle of performance variability is the main reason why outcomes deviate from the norm or expectations.
 Retrospective Analyses are not sufficient and proactive assessments are needed to anticipate and be well prepared for adversity.
 The strive to maintain highly efficient and productive systems cannot be decoupled from safety, which should be incorporated into the business planning and core processes as a prerequisite for productivity.
2.2. Quantitative and Qualitative Methods: Why Fuzzy Logic?
2.3. Rough Sets: An Approach for Data Classification
3. Methods
3.1. Phase I: Basic FRAM as a Complex Systemic Assessment Tool
 The objective for the first application was to select an analysis scenario using a wellknown deicingrelated accident. The Scandinavian Airlines flight SK751 crash at Gottröra, Sweden, in 1991 was chosen [56]. The accident was investigated and well defined in the official accident report, which allowed for an easier characterization of the functions and their outputs. While the conditions and events leading to the accident were listed in a detailed manner in the report, the objective was to verify whether a FRAM application could add to the findings and present a different perspective. This perspective should be facilitated relying on the distinguishing four principles of FRAM: equivalence of success and failure, approximate adjustments, emergence of outcome and functional resonance [19] (Figure 2).
 The second step then was to characterize the working environment of aircraft deicing operations and create a functional representation of the context by identifying the functions that constitute the system in question. The functional characterization in FRAM describes how the various tasks are related and how the outcome’s variability can resonate and affect performance negatively or positively. Consequently, a list of representative functions was identified limiting the scope of the analysis to the functions needed to execute the deicing operations. The chosen analysis scenario would then specifically depict the deicing operation and takeoff process of SK751. Each function could possibly be characterized by six aspects: Input (I), Output (O), Preconditions (P), Resources (R), Time (T) and Control (C) [19]. However, it is not necessitated that all aspects are provided; rather, it depends on the function in question. The boundaries of the analysis are formed by the background functions, which only provide outputs and are invariable as they are not the focus of the analysis. The functions were described in the form of a table listing all their respective characteristics, which can be derived from the events and data provided in the official accident report published by the Board of Accident Investigation [56]. Additionally, three types of functions could be defined: organizational, technological and human functions.
 The third step was then to identify sources of performance variability within the designed setup. As mentioned above, variability is characterized in terms of two phenotypes: timing (early, on time, too late and omission) and precision (imprecise, acceptable and precise) [19].
 Finally, the influential relationships among functions were identified to construct a visual map of the system and illustrate how functional resonance can affect the outputs of the functions. This could explain how performance variability combined and resonated to eventually lead to the crash and what lessons or new findings could this analysis provide.
3.2. Phase II: A Predictive Assessment with Quantified Results
3.3. Phase III: Rough Sets to Classify Input Data
4. Discussion
5. Conclusions and Outlook for Future Studies
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Function  Common Performance Conditions  IVF  

CPC_{1}  CPC_{2}  .....  CPC_{n}  
Function 1  Adequate  Adequate  .....  Adequate  NonVariable 
Function 2  Inadequate  Adequate  .....  Adequate  NonVariable 
Function 3  Inadequate  Inadequate  .....  Adequate  Variable 
.....  ......  ......  .....  ......  ..... 
Function m  Inadequate  Inadequate  .....  Inadequate  Highly Variable 
Function  Aspects  Output  

IVF  Input  Time  Control  Preconditions  Resources  
Function 1  NonVariable  NonVariable  NonVariable  NonVariable  NonVariable  NonVariable  NonVariable 
Function 2  NonVariable  NonVariable  Variable  Variable  Variable  NonVariable  Variable 
Function 3  NonVariable  NonVariable  Highly Variable  NonVariable  NonVariable  NonVariable  Variable 
.....  ......  ......  .....  .....  .....  ......  ..... 
Function m  Highly Variable  Highly Variable  Highly Variable  Highly Variable  Highly Variable  Highly Variable  Highly Variable 
Model  Basic FRAM  Fuzzy FRAM  RoughFuzzy FRAM 

Advantages 



Limitations 



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Slim, H.; Nadeau, S. A Proposition for Combining Rough Sets, Fuzzy Logic and FRAM to Address Methodological Challenges in Safety Management: A Discussion Paper. Safety 2020, 6, 50. https://doi.org/10.3390/safety6040050
Slim H, Nadeau S. A Proposition for Combining Rough Sets, Fuzzy Logic and FRAM to Address Methodological Challenges in Safety Management: A Discussion Paper. Safety. 2020; 6(4):50. https://doi.org/10.3390/safety6040050
Chicago/Turabian StyleSlim, Hussein, and Sylvie Nadeau. 2020. "A Proposition for Combining Rough Sets, Fuzzy Logic and FRAM to Address Methodological Challenges in Safety Management: A Discussion Paper" Safety 6, no. 4: 50. https://doi.org/10.3390/safety6040050