Risk Management in the Oil Supply Chain: A CVaR Approach

Explaining of the experts' points of view about the types and importance of man-made hazards in Iran's process industries: A qualitative study

Introduction

Process industries may have different hazards that can cause serious injury to humans, the environment, and the economy. Considering the importance of man-made risks in process industries, Experts' Points of View should be considered to apply risk reduction strategies Hence, the present study was performed to explain the views of experts regarding the types and importance of man-made hazards in process industries.

Materials and methods

This study was conducted using a qualitative method of directed content analysis with a deductive approach. The participants included 22 experts in the field of process industries. The selection of samples started purposeful and continued until data saturation. Data collection was done through semi-structured interviews.

Results

Based on experts' points of view, 5 man-made hazards in process industries were classified into 14 subcategories. The man category was divided into three subcategories: human error, technical knowledge error, and management error, the Material category was divided into 3 sub-categories of leakage and rupture, chemical properties, physical properties, Medium category was divided into two sub-categories of incorrect location selection and placement and harmful environmental factors, Machines category was divided into three sub-categories of failure in design, failure in Preventive Maintenance (PM), failure in Safety Instrumented System (SIS), and the Methods category was classified into three sub-categories: defects in inspection, defects in information, defects in executive instructions.

Conclusion

Technical training to reduce personnel errors, conducting risk-based inspections to control leaks and possible ruptures, careful design and site selection in the initial phase of the project, is recommended. The use of engineering methods and artificial intelligence to obtain the risk number and control methods to reduce the harmful effects of risks can be helpful.

A mathematical tri-level programming model for designing an integrated dynamic petroleum product supply chain

Purpose

The purpose of this paper is to design petroleum products’ supply chain management, which includes efficient integration of suppliers, manufacturers, storehouses and retailers.

Design/methodology/approach

This paper proposes that a three-level supply chain will be turned into a bi-level supply chain of petroleum products by simultaneous integration of the middle level with the upstream and downstream levels. Also, it is integrally optimized by considering the multiple managerial flows' mutual results at various supply chain levels. Also, it is integrally optimized by considering the multiple managerial flows' mutual results at various supply chain levels.

Findings

The concepts of the design, structure and outputs are led by the model's solution. The model also responds to the variations in the market via coordination in the related decisions to the distribution, production and inventory issues, and also coordinating between the demands and production.

Research limitations/implications

This paper has limited its analysis to definite values due to the over-expansion of calculations and analysis. Future works can study other aspects of the proposed model for a multi-level petroleum product supply chain in different states of certain parameters and time zones.

Practical implications

The designed model can directly and transparently help the oil managers and decision-makers lower the costs of manufacturing, distribution and sales with respect to the determined criteria.

Originality/value

This paper establishes that effectiveness of the dynamic petroleum materials supply chain design will increase by considering maintained and increased production costs and coordinate management flows at all levels by supply chain creation’s integration.

A Convex Dynamic Approach for Globally Optimal Profit in Supply Chains

Supply chain finance aims to coordinate multiple stakeholders to maximize the flow of cash and internal and external funding along the supply chain, as shown in prior research. From a regulatory standpoint, the goal of this paper is to maximize the profitability of an entire supply chain. As a result, a constrained finite time Linear Quadratic Regulation (LQR) approach is provided for determining an entity’s optimal profit state in a supply chain. The framework is represented by discrete-time linear dynamical equations for each entity in the supply chain network, taking state and input variables into account. The problem is formulated in terms of a convex quadratic programming optimization for which several numerically efficient algorithms are readily available. In order to validate the approach, it was tested on two topologies. The first topology is a fully connected supply chain with six nodes; the second is a simple topology based on the Iranian pharmaceutical supply chain. The results indicate that the proposed approach successfully planned production and financing decisions within the simulated supply chain and obtained globally optimal profit for all supply chain stakeholders.

Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks

The oil industry operates in a very uncertain marketplace, where uncertain conditions can engender oil production fluctuations, order cancellation, transportation delays, etc. Uncertainty may arise from several sources and inexorably affect its management by interfering in the associated decision-making, increasing costs and decreasing margins. In this context, companies often must make fast and precise decisions based on inaccurate information about their operations. The development of mathematical programming techniques in order to manage oil networks under uncertainty is thus a very relevant and timely issue. This paper proposes an adjustable robust optimization approach for the optimization of the refined products distribution in a downstream oil network under uncertainty in market demands. Alternative optimization techniques are studied and employed to tackle this planning problem under uncertainty, which is also cast as a non-adjustable robust optimization problem and a stochastic programing problem. The proposed models are then employed to solve a real case study based on the Portuguese oil industry. The results show minor discrepancies in terms of network profitability and material flows between the three approaches, while the major differences are related to problem sizes and computational effort. Also, the adjustable model shows to be the most adequate one to handle the uncertain distribution problem, because it balances more satisfactorily solution quality, feasibility and computational performance.

Beyond Value-at-Risk: GlueVaR Distortion Risk Measures

We propose a new family of risk measures, called GlueVaR, within the class of distortion risk measures. Analytical closed-form expressions are shown for the most frequently used distribution functions in financial and insurance applications. The relationship between GlueVaR, value-at-risk, and tail value-at-risk is explained. Tail subadditivity is investigated and it is shown that some GlueVaR risk measures satisfy this property. An interpretation in terms of risk attitudes is provided and a discussion is given on the applicability in nonfinancial problems such as health, safety, environmental, or catastrophic risk management.