The Black Swan – Knowing the Unknown in Projects

Hajikazemi, Sara; Ekambaram, Anandasivakumar; Andersen, Bjørn; Zidane, Youcef J-T.

doi:10.1016/j.sbspro.2016.06.178

Cited by 33 publications

(30 citation statements)

References 9 publications

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“…Some authors consider [119] that when unexpected deviations arise due to causes that are difficult to predict because of their randomness, this can be classified as a special risk [120,121]. From the point of view of the methodology (SRC), these causes that can lead to unexpected risk situations are called potential causes, which are summarized in Figure 8.…”

Section: Statistical Risk Control (Src) Methodologymentioning

confidence: 99%

New Risk Methodology Based on Control Charts to Assess Occupational Risks in Manufacturing Processes

2019

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The accident rate in the EU-28 region of the European Union showed a value of 2 fatal accidents per 100,000 people in 2019 that mainly affect construction (24%), manufacturing (19%) and logistics (19%). To manage situations that affect occupational risk at work, a review of existing tools is first carried out taking into account three prevention, simultaneity and immediacy characteristics. As a result, a new dynamic methodology called Statistical Risk Control (SRC) based on Bayesian inference, control charts and analysis of the hidden Markov chain is presented. The objective is to detect a situation outside the limits early enough to allow corrective actions to reduce the risk before an accident occurs. A case is developed in a medium-density fiberboard (MDF) manufacturing plant, in which five inference models based on Poisson, exponential and Weibull distributions and risk parameters following gamma and normal distributions have been tested. The results show that the methodology offers all three characteristics, together with a better understanding of the evolution of the operators in the plant and the safety barriers in the scenario under study.

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Section: Statistical Risk Control (Src) Methodologymentioning

confidence: 99%

New Risk Methodology Based on Control Charts to Assess Occupational Risks in Manufacturing Processes

2019

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“… Hajikazemi et al (2016) state that not all severe incidents are black swans, rather that a black swan is specifically a “game-changer” event for those who are impacted by it e.g. the Lehman Brothers bankruptcy in 2008 ( Hajikazemi et al, 2016 ), or COVID-19 in 2020 ( Ker & Cardwell, 2020 ). Others suggest that COVID-19 or rather a coronavirus outbreak on a global scale was actually predictable as coronaviruses are a known risk and thus this event is not a black swan ( Inayatullah, 2020 ).…”

Section: Black Grey and White Swans And Black And White Elephantsmentioning

confidence: 99%

“…The concept of unknown unknowns is widely discussed in contemporary academic literature associated with risk ( Taleb, 2007 ; Aven, 2013 , 2015 ; Aven & Krohn, 2014 ; Dufva & Ahlqvist, 2015 ; Flage & Aven, 2015 ; Hajikazemi, Ekambaram, Andersen, & Zidane, 2016 ; Wardman & Mythen, 2016 ). However, more recently it has been associated with food related policy ( Manning & Soon, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Swans and elephants: A typology to capture the challenges of food supply chain risk assessment

Manning

Birchmore

Morris

2020

Trends in Food Science & Technology

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“…This is because both share the condition of having a major impact, extreme infrequency, and the widespread insistence. It is described by some as an outlier because it lies outside the realm of regular expectation, since nothing in the past can predict its possibility (Hajikazemi, Ekambaram, Andersen, Zidane, 2016).…”

Section: Unknown Unknown Risksmentioning

confidence: 99%

“…Unknown risk has warning signs and if we can identify them and act against minor events, then perhaps we can avoid losses. It is important to obtain an early indication of negative risk development that will become noticeable in the future (Hajikazemi, Ekambaram, Andersen, Zidane, 2016). AI can be used to identify abnormalities in certain areas of data storage.…”

Section: Early Predictionmentioning

confidence: 99%

The Role of Artificial Intelligence in Mitigating Unknown-Unknown Risks

Beshwari¹,

Beshwari²,

Beshwari³

2020

IJRP

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Artificial intelligence (AI) is a potentially powerful tool to mitigate the unknown unknown risk. T his research's objective is investigating the impact of AI on mitigating unknown unknown risk. For all six cases analyzed, it was possible to extract that unknown risk is hard to be reported in advance. T herefore, since the outbreak of the Coronavirus COVID-19, organizations have been eager to use AI in response to the consequences caused by this pandemic. As a result, AI played a significant role in rapid response, decision-making, early prediction, automated processes, and detection and tracking. This means AI has the potential to be used as a solution to mitigate the post event impacts and study the patterns for future prediction.

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The Black Swan – Knowing the Unknown in Projects

Cited by 33 publications

References 9 publications

New Risk Methodology Based on Control Charts to Assess Occupational Risks in Manufacturing Processes

New Risk Methodology Based on Control Charts to Assess Occupational Risks in Manufacturing Processes

Swans and elephants: A typology to capture the challenges of food supply chain risk assessment

The Role of Artificial Intelligence in Mitigating Unknown-Unknown Risks

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