ReviewDownstream oil supply chain management: A critical review and future directions
Introduction
In the literature, there is no agreement about the supply chain definition, but the core of all definitions addresses the integration among entities and activities throughout the business processes which are developed over the network (Sahebi et al., 2014). Here, supply chain is understood as a complex and dynamic system, within collaborative or competitive environment, whose entities may or not cooperate to fulfill customer requests for products or services, where information, product and financial flows occur among different echelons over the time horizon. Therefore, the supply chain management arises to cope with this complex environment in order to plan, manage, coordinate and integrate all entities and activities by pursuing to accomplish customer requests, while striving for high revenues and low costs across the chain. In this way, Simchi-Levi et al. (2003) emphasize supply chain management as being a set of approaches to efficiently integrate the entities to organize all activities involved in fulfilling orders in the right quantities, right locations and on time so as to minimize the overall costs, while satisfying the service level requirement.
One special type of supply chain comes from the petroleum industry due to its size, its complexity, besides its economic and social importance. From 2010 onwards, the global oil production and consumption along with the global trade of crude oil and refined products have increased again in accordance with the data released by the British Petroleum (2015). However, in the petroleum industry, the revenues are as large as the overall costs, such as costs of exploring, producing and supplying crude oil, as well as costs of refining, distributing and marketing the refined products, as argued by the American Petroleum Institute (API, 2015). Additionally, the oil supply chain is inserted in an unstable context, influenced by geopolitical unrest, global competition and price volatility, where the business focuses on margins and the savings are carried out through improved forecasts and schedules with shorter planning horizon. As a result, the seek for designing and implementing new tools aims to establish an integrated and adaptive supply chain in order to improve the decision making process, reduce costs, decrease inventories and enhance margins (Capgemini, 2008). Moreover, the integration is a powerful way to lead the companies to the optimization of their value chain, while enabling them to balance their upstream and downstream activities, besides mitigating risk and reducing volatility (Ernst and Young, 2012).
According to Rice and Caniato (2003), a secure and resilient supply chain is a system enabled with security processes and procedures, while it is capable to proactively respond to diverse disruptions and restore its original operations. Christopher and Peck (2004) go further and add the capability of moving to a new and more desirable state after disturbances. In turn, resilient supply chains with higher capacity of mitigating risks are sought by the oil companies, where the resilience could be improved by different ways, e.g., through flexibility and redundancy. However, there exists a trade-off between the cost of developing it and its benefits to the network (Deloitte, 2010). Thus, the concept of resilience must be taken into account when designing and planning the supply chain to improve the capability to respond rapidly and cost-effectively to unpredictable events (Cardoso et al., 2015). Within this context, the optimization of the supply chain is a need to guarantee profitable, resilient and efficient systems. Such need has been addressed by the academic community and optimization tools have been developed and applied to better support the decision making process across such chain, by allowing and improving its management (Oliveira and Hamacher, 2012b).
It has been a long discussion whether the oil supply chain is divided into two or three segments, being the allocation of refinery operations the center of the discussion. As described by Sahebi (2013), the oil supply chain can be classified into three different classification schemes. The first considers the oil supply chain divided between upstream and downstream segments, incorporating the refinery and petrochemical plants within downstream segment. However, the second divides the network into upstream, midstream and downstream segments, where the midstream part comprises the refinery and petrochemical operations. Lastly, the third also considers the oil supply chain divided into three segments, but the midstream part refers to crude oil transportation to terminal and storage facilities. For the objectives of this literature review, the second classification scheme is more adequate, where the oil supply chain encompasses a set of functions which can be divided into three main segments, namely: upstream; midstream; and downstream – Fig. 1.
Then, the upstream segment comprises all functions from petroleum exploration, production and transportation until the refineries. The midstream concerns about the conversion of the petroleum into refined products at refineries and petrochemicals. Lastly, the downstream segment includes storage, primary and secondary distributions and marketing of refined products. In each segment, there are petroleum companies which rely on physical infrastructures across the network to develop these functions (Fernandes et al., 2014). Thus, Sahebi et al. (2014) list and classify the major facilities that compose the infrastructure into each segment as follows: in upstream, wellhead, well platform, production platform and crude oil terminal; in midstream, refinery and petrochemical industries; and then, in downstream, primary and secondary transport, storage depot and wholesale & retail market.
As in any typical supply chain, the decisions can be classified within strategic, tactical or operational levels, depending on its frequency and the time that it affects the network. The strategic planning is related to decisions with long-lasting effects that define the structure of the supply chain, what restrains the tactical planning, where the medium-term decisions concern to identify the best flow of material across the chain, besides establishing the operating policies in the operational level, that copes with short-term decisions related to scheduling of the activities (Chopra and Meindl, 2007), such as multiproduct pipeline scheduling and vehicle routing in the downstream segment, as well as any operational activities in the upstream and midstream segments (Barbosa-Póvoa, 2014). Each one of these decision levels must consider uncertainty over the decision horizon. Furthermore, the level of uncertainty changes over these planning stages, where it begins to decrease according to the decreasing of the decision horizon across the supply-chain planning. In short, there is a natural hierarchy among these stages, where the strategic planning imposes limits to the tactical planning, which in turn is implemented via operational level, while their mutual analysis and optimization are needed as well as their integration to improve the supply chain performance, as outlined by Al-Qahtani and Elkamel (2010a).
According to Grossmann (2005), the enterprise-wide optimization (EWO) aims to integrate the information and the decision-making along the supply chain through developing analytical information technology (IT) tools, based on mathematical programming, while seeking to improve the optimal economic performance. Thereby, EWO focuses on major operations and activities in the process industries (e.g., oil industry) in such way to reduce costs and inventories. However, the lack of optimization models and IT tools are the major barriers faced by EWO area, as highlighted by Grossmann, 2005, Grossmann, 2014. Similarly, Shah et al. (2011) and Barbosa-Póvoa (2014) emphasize the need and role of those decision-support tools concerning the decision-making on petroleum refining operations and across the supply chain, respectively, whereas Garcia and You (2015) see the technical development of these tools combined with sustainability trends as an opportunity to enhance the supply chain design in coming years. Moreover, the need to further develop analytical optimization tools concerning many sources of uncertainty, different types of risk, resilience, responsiveness, sustainability and collaborative perspective is also discussed by Barbosa-Póvoa (2014) in the strategic, tactical and operational levels of the supply-chain planning.
In this context, the downstream oil supply chain has been researched by an increasing number of authors and several analytical methodologies have been proposed, where the main features of the sector have been studied and the quantity of works has increased significantly in recent years, as can be observed in the following sections. For this reason, a literature review is required, focused specifically on optimization methods within the downstream segment context. Thus, the objective of this current work is twofold. Firstly, it explores the existing body of knowledge in the optimization models applied to the distribution of refined products. Secondly, it intends to identify the opportunities for the future research investigations in this field. To achieve these goals, this paper is organized as follows: Section 2 provides an overview of downstream oil supply chain; Section 3 explains the review methodology used in this survey; Section 4 actually presents the literature review; and Section 5 concludes through a discussion about the findings and gaps in the current literature, besides stressing the challenges in the future research directions.
Section snippets
The downstream oil supply chain network
The downstream segment is the third part of the oil supply chain and comprises the distribution and marketing of oil products, such as diesel, fuel oil, gasoline and jet fuel, as well as motor oils and lubricants. The distribution requires a well-established infrastructure (logistics assets), which englobes storage facilities, appropriate resources, transportation modes and routes, where oil companies develop different activities in such way to move the oil products from refineries to the end
Review methodology
According to Ridley (2012), the literature review can be understood as a quest for the identification of theories and previous researches in a certain scientific area in order to support a research investigation, besides aiming the future research directions from the detection of gaps in the known literature. In this work, a literature review is performed which aims to investigate the relevant works around the usage of optimization-based decision-support tools applied to the downstream oil
From strategic to operational planning
Optimization techniques applied to strategic, tactical and operational problems within the downstream oil supply chain will be the focus of the current literature review. In addition, the selected papers will be mainly investigated under the perspectives of uncertainty, risk, resilience, sustainability, integration and inventory management. The framework behind this section will be properly defined from the selected papers in order to extract the major subjects that have been treated in this
General contributions and challenges in the future research directions
In this literature review, studies focused on mathematical programming models applied to the downstream oil supply chain have been selected and analyzed in order to picture the scientific coverage of these works, besides identifying the most promising research directions in this field. Some findings can be withdrawn about major subjects addressed by these reviewed articles, as well as issues not treated by them, which may guide future researches to fulfill the existing voids. The identified
References (81)
- et al.
Supply chain optimization of petroleum organization under uncertainty in market demands and prices
Eur. J. Oper Res.
(2008) - et al.
Multisite facility network integration design and coordination: an application to the refining industry
Comput. Chem. Eng.
(2008) - et al.
Robust planning of multisite refinery networks: optimization under uncertainty
Comput. Chem. Eng.
(2010) - et al.
Dantzig–Wolfe and block coordinate-descent decomposition in large-scale integrated refinery-planning
Comput. Oper. Res.
(2009) Supply chain design and analysis: models and methods
Int. J. Prod. Econ.
(1998)- et al.
Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty
Omega
(2015) - et al.
Integrating financial risk measures into the design and planning of closed-loop supply chains
Comput. Chem. Eng.
(2016) - et al.
A heuristic for the multi-period petrol station replenishment problem
Eur. J. Oper. Res.
(2008) - et al.
Heuristics for the multi-depot petrol station replenishment problem with time windows
Eur. J. Oper. Res.
(2012) - et al.
Quantitative models for managing supply chain risks: a review
Eur. J. Oper. Res.
(2015)
Strategic network design of downstream petroleum supply chains: single versus multi-entity participation
Chem. Eng. Res. Des.
Supply chain design and optimization: challenges and opportunities
Comput. Chem. Eng.
Optimal network design and storage management in petroleum distribution network under uncertainty
Eng. Appl. Artif. Intell.
Speciality oils supply chain optimization: from a decoupled to an integrated planning approach
Eur. J. Oper. Res.
A critical review on supply chain risk −Definition, measure and modeling
Omega
Modeling downstream petroleum supply chain: the importance of multi-mode transportation to strategic planning
Transp. Res. Part E Logist. Transp. Rev.
An integrated model of supply network and production planning for multiple fuel products of multi-site refineries
Comput. Chem. Eng.
Optimal planning strategy for the supply chains of light aromatic compounds in petrochemical industries
Comput. Chem. Eng.
Implications of capacity expansion under uncertainty in oil industry
J. Petrol. Sci. Eng.
An operational planning model for petroleum products logistics under uncertainty
Appl. Math. Comput.
A general modeling framework for the operational planning of petroleum supply chains
Comput. Chem. Eng.
A Lagrangean decomposition approach for oil supply chain investment planning under uncertainty with risk considerations
Comput. Chem. Eng.
Accelerating Benders stochastic decomposition for the optimization under uncertainty of the petroleum product supply chain
Comput. Oper. Res.
Supply chain optimisation for the process industries: advances and opportunities
Comput. Chem. Eng.
Planning and scheduling models for refinery operations
Comput. Chem. Eng.
Heuristic batch sequencing on a multiproduct oil distribution system
Comput. Chem. Eng.
Strategic and tactical mathematical programming models within the crude oil supply chain context – a review
Comput. Chem. Eng.
Optimization under uncertainty: state-of-the-art and opportunities
Comput. Chem. Eng.
Optimal design of advanced drop-in hydrocarbon biofuel supply chain integrating with existing petroleum refineries under uncertainty
Biomass Bioenergy
Robust design and operations of hydrocarbon biofuel supply chain integrating with existing petroleum refineries considering unit cost objective
Comput. Chem. Eng.
The UK oil and gas supply chains: an empirical analysis of adoption of sustainable measures and performance outcomes
Int. J. Prod. Econ.
Multisite refinery and petrochemical network design: optimal integration and coordination
Ind. Eng. Chem. Res.
Planning and Integration of Refinery and Petrochemical Operations
Downstream Petroleum 2013
Process supply chains management – where are we? Where to go next?
Process Energy Syst. Eng.
Point of View: Creating the Integrated Value Chain for Downstream Oil
Risk management in the oil supply chain: a CVaR approach
Ind. Eng. Chem. Res.
Cited by (69)
Evaluation and optimization of pipeline pricing strategies on oil product logistics in China
2024, Journal of Pipeline Science and EngineeringA trio of resiliency, reliability, and uncertainty to design and plan the downstream oil supply chain
2023, Computers and Chemical EngineeringIntegration optimization of production and transportation of refined oil: A case study from China
2022, Chemical Engineering Research and DesignInnovations of carbon-neutral petroleum pipeline: A review
2022, Energy Reports