Operational risk

Leaking fi om process flows may pose operational risks and cause environmental problems as well as economic losses. Two examples of tracer methods for testing, localising and quantifying leaks are given below. 

Notwithstanding the preceding notes, the fact is that whether large boilers or small boilers are involved, the starting point for the proactive reduction in operational risk is effective control of water chemistry. 

Layers-of-protection analysis (LOPA) is a semiquantitative methodology for analyzing and assessing risk. It is typically applied after a qualitative hazards analysis has been completed, which provides the LOPA team with a listing of hazard scenarios with associated safeguards for consideration. LOPA uses simplified methods to characterize the process risk based on the frequency of occurrence and consequence severity of potential hazard scenarios. The process risk is compared to the owner/operator risk criteria. When the process risk exceeds the risk criteria, protection layers are identified that reduce the process risk to the risk criteria. 

Checklist or Worksheet - A standardized listing which identifies common protection features required for typical facilities is compared against the facility design and operation. Risks are expressed by the omission of safety systems or system features. 

Beyond playing a significant role as a measurement tool, audits provide the opportunity to share a set of fresh perspectives on areas where requirements have yet to be codified (e.g., process control procedures, management information systems, and maintenance programs). Audits also serve to indicate ongoing efforts to reexamine and reevaluate operations to further reduce operational risks and consequent liabilities (including property damage and business interruption). 

Two changes in the SCWO system have had a significant impact on safety. The switch from kerosene to a mixture of kerosene and propylene glycol as a feed additive should reduce operational risks, while the change from air to oxygen will introduce a new hazard. Although the committee knows of no previous accidents that would raise safety questions unique to the transpiring-wall SCWO reactor, it has expressed gen-... 

Long-term maintenance therapy for patients with discrete aldosterone-producing adrenal adenomas who are poor operative risks, or who decline surgery. 

Chemical process systems are subject to uncertainties due to many random events such as raw material variations, demand fluctuations, equipment failures, and so on. In this chapter we will utilize stochastic programming (SP) methods to deal with these uncertainties that are typically employed in computational finance applications. These methods have been very useful in screening alternatives on the basis of the expected value of economic criteria as well as the economic and operational risks involved. Several approaches have been reported in the literature addressing the problem of production planning under uncertainty. Extensive reviews surveying various issues in this area can be found in Applequist et al. (1997), Shah (1998), Cheng, Subrahmanian, and Westerberg (2005) and Mendez et al. (2006). 

The first approach adopts the classical Markowitz s MV model to handle randomness in the objective function coefficients of prices, in which the expected profit is maximized while an appended term representing the magnitude of operational risk due to variability or dispersion in price, as measured by variance, is minimized (Eppen, Martin, and Schrage, 1989). The model can be formulated as minimizing risk (i.e., variance) subject to a lower bound constraint on the target profit (i.e., the mean return). 

As remarked in Approach 1, a potential complication with Expectation Model I lies in computing a suitable range of values for the operational risk factor 0j. Therefore, an alternative formulation of minimizing variance while adding a target profit constraint is employed for Expectation Model II ... 

The goal of Approach 3 is to append an operational risk term to the mean-risk model formulation in Approach 2 to account for the significance of both financial risk (as considered by Approach 1) and operational risk in decision-making. 

Note that the index s and the corresponding set S is used to denote scenarios for the evaluation of the inner expectation term to distinguish them from the original index s used to represent the scenarios. Vs is weighted by the operational risk factor 02 e (0, oo). The formulation of Risk Model II is ... 

Therefore, in this approach, we develop Risk Model III as a reformulation of Risk Model II by employing the mean-absolute deviation (MAD), in place of variance, as the measure of operational risk imposed by the recourse costs to handle the same three factors of uncertainty (prices, demands, and yields). To the best of our knowledge, this is the first such application of MAD, a widely-used metric in the area of system identification and process control, for risk management in refinery planning. 

Similar to Risk Model II, the adoption of MAD is weighted by the operational risk factor 03 (0 < 03 < oo) in Risk Model III, to give the following formulation ... 

Operational risk factor 02 Optimal objective value Expected variation in profit V(z0)(E + 8) Expected total unmet demand/ production shortfall Expected total excess production/ production surplus Expected recourse penalty costs Es Expected variation in recourse penalty costs Vs p = E[z ] - Es c a P... 

Financial and Operational Risk (Measured hy Standard Deviation)... 

Operational Risk Trade-off Parameter <9 for Recourse Penalty Costs... 

Figure 6.3 Risk Model II plot of expected profit for different levels of risk as represented by the economic risk factor 0n and the operational risk factor 02.

In the following section we illustrate how the mean-absolute deviation (MAD) is employed as the measure of operational risk. 

New measures and procedures to manage operational risk with applications to the planning of gas commercialization in Asia. Computers el Chemical Engineering,... 

Barbara, A. and Bagajewicz, M.J. (2004) Managing operational risk in planning under uncertainty. AIChE Journal, 50, 963. 

In this study, operational risk was accounted for in terms of variance in both projected benefits, represented by first stage variables, and forecasted demand, represented by the recourse variables. The variability in the projected benefit represents the solution robustness where the model solution will remain close to optimal for all scenarios. On the other hand, variability of the recourse term represents the model robustness where the model solution will almost be feasible for all scenarios. This approach gives rise to a multiobjective analysis in which scaling factors are used to evaluate the sensitivity due to variations in each term. The projected benefits variation was scaled by 0i, and deviation from forecasted demand was scaled by 02, where different values of 0i and 02 were used in order to observe the sensitivity of each term on the final petrochemical complex. The objective function with risk consideration can be written as follows ... 

A health risk assessment (HRA) must also be prepared but has less impact on operational risk and safety. Figure 6-1 is a diagram showing how these analyses and assessments interact. 

Different approaches towards structuring the various kinds of political risks can be found in literature (e.g., Meldrum 2000, pp. 34-35 Desta 1986, pp. 50-53). As shown in Figure 57, typically, based on the impact of the respective type of political risk, one can distinguish between asset protection risks, transfer risks and operational risks (cf. Root 1972, p. 357). Additionally, a separation into macro risks affecting all foreign companies operating in a country and micro risks that are specific to the industry and/or the individual company should be made (cf. Robock 1971, pp. 9-11). 

Consequently, these models often require model tuning through semiempirical correlations and data integration. Such tasks are time-consuming and problem specific as they often require information from additional experiments and pilot plant trials, with missing information leading to start-up and operational risks. The incorporation of more accurate multi-scale phenomena (at device-, meso- and even molecular scales) captured by reduced-order models (ROMs) will overcome these limitations (Lang et al., 2009, 2011). 

Which six factors characterize the operational risk associated with information technology and automation in pharmacies ... 

Operational risk generally is defined as risk of loss resulting from inadequate or failed internal processes, people and systems, or external events (APRA, 2007 Erickson, 2001 McCuaig, 2005 Vaughn, 2005 Walker, 2001 Wikipedia, 2007). Information technology operational risks in todays digitized pharmacy are characterized by six event factors ... 

Operational risk Psychosocial risk Data theft/loss patient injury Productivity loss increased health benefits Loss of customers/ trading partners Data theft ... 

APRA. 2007. Prudential Practice Guide LPG 230—Operation risk. Available at www.apra.gov.au/Life/upload/ Prudential-Practice-Guide-LPG-230-Operational-Risk. pdf. 

Erickson J. 2001. IT trends Operational risk. Available at www.forrester.com/Research/LegacyIT/Excerpt/0,7208, 29917,00.html. 

McCuaig B. 2005. The Case of Operational Risk Management. Availableatwww.cgservices.com/compliance/ library/Case for OperationaLRisk Management.pdf. 

Walker S. 2001. Operational Risk Management Controlling Opportunities and Threats. Available at www. connleywalker.com.au/ORM.pdf. 

Wikipedia. 2007. Operational risk. Available at http //en. wikipedia.org/wiki/Operational risk. 

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