Product Flexibility Strategy Under Supply and Demand Risk

Problem definition: With heightened global uncertainty, supply chain managers are under increasing pressure to craft strategies that accommodate both supply and demand risks. Although product flexibility is a well-understood strategy to accommodate risk, there is no clear guidance on the optimal flexibility configuration of a supply network that comprises both unreliable primary suppliers and reliable backup suppliers. Academic/practical relevance: Existing literature examines the value of flexibility with primary and backup suppliers independently. For a risk-neutral firm, research shows that (a) incorporating flexibility in a primary supplier by replacing two dedicated ones (in absence of backup supply) is always beneficial and that (b) adding flexibility to a reliable backup supplier (in absence of product flexibility in primary suppliers) is always valuable. It is unclear, however, how flexibility should be incorporated into a supply network with both unreliable primary suppliers and reliable backup suppliers. This research studies whether flexibility should be incorporated in a primary supplier, a backup supplier, or both. Methodology: We develop a normative model to analyze when flexibility benefits and when it hurts. Results: Compared with a base case of no flexibility, we prove that incorporating flexibility in either primary or backup suppliers is always beneficial. However, incorporating flexibility in both primary and backup suppliers can be counterproductive because the supply chain performance can decline with saturated flexibility, even if flexibility is costless. A key reason is that the risk-aggregation effect of consolidating flexibility in an unreliable supplier becomes more salient when flexibility is already embedded in a backup supplier. Managerial implications: This research refines the existing understanding of flexibility by illustrating that flexibility is not always beneficial. When there is a choice, a firm should prioritize incorporating flexibility in a reliable backup supplier.

Multihazard Risk Aggregation Approach for Quantitative Risk Assessment of Upstream Oil and Gas Facilities

For the design and operation of Oil and Gas (O&G) facilities, a Quantitative Risk Assessment (QRA) should be performed to quantify the risk of major accidents due to multiple hazards and sources at the plant level, thus allowing the effective identification and allocation of safety barriers. In this work, a novel approach for the multi-hazard and multi-source aggregation of risks is proposed, accounting for the uncertainties typically unexpressed in a conventional QRA (both on the frequency and severity of the accidental scenarios). The multi-hazard risk assessment framework proposed is applied to assess the Location-Specific Individual Risk (LSIR) for a representative Upstream O&G plant (case study), using a model based on multistate Bayesian Networks (BNs) for different functional units, each one undergoing an initiating event of Loss Of Primary Containment (LOPC). Estimates of frequency and severity for each possible accident scenario are aggregated to eventually calculate the overall LSIR. Moreover, LSIR's confidence intervals are provided to describe the uncertainty associated to the estimates, and the frequency and severity contributions to risk are derived for targeted prioritization of the safety barriers in view of the risk reduction.

Aggregated Risk Assessment and Survey for Risk Reduction in Oil Terminals

One of the goals of any oil terminal is to make a business while avoiding hazardous events and harmful effects for both humans and the environment. This can be achieved by creating a safe working place as well as by performing safe and acceptable activities regarding the impact on surrounding objects, including residential and industrial areas. The aim of the hazard analysis of the oil terminal is to assess the risks related to hazardous events or phenomena and to evaluate whether the assessed risks are acceptable. The hazard analysis and assessment of risk are also used for risk reduction while examining and limiting hazardous scenarios that, for instance, involve the loss-of-containment of flammable or combustible material. In this paper, the authors aim to contribute to risk research by providing a comprehensive methodology of risk assessment for oil terminals with case study results and discussion on features of the methodology, risk aggregation, its applicability for risk reduction, and industrial interests. The performed study considered the “Klaipedos Nafta AB” (an operator of the Klaipeda Oil Terminal, Lithuania) case study regarding hazardous materials that might be released from various tanks, devices, and associated pipelines. The performed quantitative risk assessment has enabled the determination of the probability regarding whether releases would ignite and, for instance, cause explosion. In the case study, the estimate of probability, i.e., the frequency, and the possible consequences of the hazardous events were evaluated, and both mitigation and risk reduction measures were also considered.

Effect of Choice Bracketing on Risk Aggregation in Repeated-Play Gambles With no Feedback

Aggregating the risk of a series of decisions reduces the overall risk compared to when each decision is considered individually—the logic behind diversified investment strategies. Most experimental research on a series of risky decisions provides participants with immediate feedback for each individual choice before presenting the subsequent gamble—a task-structure that inhibits the possibility of risk aggregation. In real-life business decisions, feedback is usually not seen until a significant delay with many more business decisions made in the interim. This decision-making sequence has the potential for systematic risk aggregation. However, it is unclear how people determine what decisions cluster together such that those decisions’ risks become aggregated. In the current work, we presented experimental participants a series of scenarios describing potential investments and investigated multiple ways to support clustering or bracketing choices together. We found that showing a distribution of outcome probabilities without inter-trial feedback reduced risk aversion. Further, we found mixed evidence for an effect of similarity of projects, and found only minimal evidence that viewing projects together and awareness of the number of projects encourages aggregation. These results suggest that risk aggregation is hard to facilitate, at least in laypeople, without first aggregating the options for them.

Landscape Ecological Risk Assessment Based on LUCC—A Case Study of Chaoyang County, China

The ecological environment is suffering from great human disturbance. Scientific assessment of landscape ecological risks can provide scientific guidance for land use management. This study focused on Chaoyang County in China, used ecological risk assessment methods to characterize the impact of land use/land cover (LUCC) change, and revealed the risk aggregation pattern with the help of spatial autocorrelation analysis. The results showed that ecological risk was increased from 2000 to 2010 but decreased from 2010 to 2018. The ecological risk of the Daling River and Xiaoling River basin was at a relatively high level, and low in the northwest and southeast of the study which covered by forest land. Occupying cultivated land for built-up and large-scale deforestation were two of the main factors to contribute to the increase of ecological risk. The distribution of High-High (HH) and Low-Low (LL) risk agglomeration areas was basically the same as risk levels, but the scope is smaller and more precise. Thus, HH and LH risk agglomeration area should be paid more attention to prevent the adverse impact of adjacent areas. Our study gave a novel perspective to investigate the pattern of ecological risk in order for government managers to identify key risk areas.

Risk allocation with Shapley value in the risk aggregation framework

The topic of risk aggregation arises from the need to incorporate in a single measure the overall exposure to the different risk types. In general, the methodologies adopted for the purposes of risk integration are based on the principle that the overall economic capital is lower than the simple algebraic sum of economic capital measures related to individual risks. This phenomenon, due to the existence of an imperfect correlation between the risks, determines, in line with portfolio theory, a "diversification benefit". The issue of risk allocation subsequently arises when the risk value of the diversified aggregated loss needs to be reassigned to the different risk classes. A similar issue has been solved in the framework of cooperative Game Theory, where the Shapley value provides a player-specific contribution of the total surplus generated by the coalition. The paper proposes a novel application of the Shapley formula in the ICAAP context (Pillar II - economic view). In particular, we show that the Shapley value is the unique solution to the allocation problem of an overall risk value, granting the fundamental requested properties, including the efficiency one. An exemplificative model application is reported, as well as a comparison with a benchmark methodology. The experimental part shows the advantages of the novel approach in terms of precision and reliability of the estimates. Finally, it is important to mention that the presented framework can be applied also in other contexts such as, for instance, in the risk class attribution of the operational risk.

Rearrangement Algorithm in Risk Aggregation

Sometimes there’s no closed-form analytical solutions for the risk measure of aggregate losses representing, say, a company’s losses in each country or city it operates in, a portfolio of losses subdivided by investment, or claims made by clients to an insurance company. Assuming there’s enough data to assign a distribution to those losses, we examine the Rearrangement Algorithm’s ability to numerically compute the Expected Shortfall and Exponential Premium Principle/Entropic Risk Measure of aggregate losses. A more efficient discretization scheme is introduced and the algorithm is extended to the Entropic Risk Measure which turns out to have a smaller uncertainty spread than the Expected Shortfall at least for the cases that we examined.