FUEL MARKETING OPTIMIZATION: providing an advantage in an increasingly complex and competitive market

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    FUEL MARKETING OPTIMIZATION: providing an advantage in an increasingly complex and competitive market

    Fuel marketing companies are faced with a volatile commodity market and an increasingly stringent regulatory environment. Better decision support systems are required to provide insights to grow margins and effectively utilize assets. In this article, Pooja Malhotra, Rathin Gupta and Rajiv Gupta discuss how recent advances in computing make a strong case for fuel marketing companies to evaluate and invest in optimization tools. They also explain how users can leverage these tools to more efficiently and effectively evaluate multiple scenarios, uncover opportunities and make better business decisions.

    For decades, fuel marketing companies have been connecting customers to refiners, giving refiners demand predictability to plan refinery runs and customers a secure supply at competitive prices. Because the fuel marketing business is a low-margin business, companies have traditionally relied on cost savings through operational efficiencies and economies of scale to maintain and increase profitability. Although these methods have been effective in the past, companies are realizing that this approach has resulted in an underinvestment in people, tools and process standardization, which is making earning the next dollar more difficult.

    Compounding the problem, a typical organization with separate procurement, sales and marketing teams may be using different models, tools, data and assumptions to make decisions. This leads to suboptimal outcomes and a lack of transparency into the decision-making process. The underlying inconsistencies also make communication across groups more challenging.

    To survive in the current environment, companies need to have an increased focus on standardizing data, tools and processes, as well as providing appropriate information to the decision makers at the right frequency. The fuels supply chain is inherently complex with many moving parts running at different velocities, making it difficult to manage and adjust course on short notice. It is impossible to assess the impact of assumptions as well as decisions on supply chain and profitability without the use of strong analytical tools which can help users test hypotheses, evaluate the current state and optimize future decisions.


    Recent advances in computing technology, particularly for big data analytics, have made it easier and significantly less expensive to leverage technology in order to get more granular insights into the supply chain and make informed decisions.

    Portfolio optimization can help companies grow revenue, increase market share and improve profitability. It will also require them to treat all their assets, including terminals, contracts and logistic contracts, as an integrated portfolio that needs to be co-optimized for maximizing the overall portfolio profitability. All contractual obligations should be met while deriving the most value from the entire portfolio. With the concepts of advanced analytics applied to the fuel marketing business, models can be developed to enable outcomes that will maximize profit margins while staying within risk parameters and operational constraints.

    It is important to note that:

    • People at different levels use different types of data for decisions. The people on the ground typically drive operational decisions, while the leadership team drives more strategic decisions.
    • Many decisions are to be made in a relatively short span of time.
    • When making decisions, both controllable and noncontrollable factors are usually in play, resulting in extremely high levels of uncertainty.
    • Decisions made today have an impact on tomorrow’s decisions, creating the need for a more agile decision-making process that can rapidly adapt to changing conditions.

    As a consequence, the approach used needs to focus on optimizing near-term to longer-term operational decisions to operate the existing supply chain as well as making strategic decisions about evolving the business.

    Table 1: Optimizing operations versus evolving the business.

    To achieve incremental margin gains, all decision parameters across the value chain need to be evaluated, leveraging the optionality embedded in the portfolio assets/contracts and monetizing market conditions while adhering to the compliance limits and risk threshold. These recommendations also need to be revalued and adjusted to account for market volatility and any unforeseen events on a timely basis.

    There are multiple algorithmic tools available for managing strategic and operational plans and enterprise risk goals, such as linear programing or stochastic modelling tools. These decision support tools can be extended to manage the information and computational complexity of the supply chain assets, contracts, logistics and positions in recommending the optimal plans. The major benefit of such tools is to provide decision makers with the ability to run multiple variations of scenarios to evaluate execution feasibility. These tools also provide the ability to monitor the execution options available across the business units with the potential risk profile before the execution decisions are made. Tools can also better prepare the organization by stress testing existing plans and by simulating pricing/volume stress scenarios to evaluate the impact on portfolios and response strategies.

    For an optimization tool to provide relevant valid output and drive decision making, it is important that the business has insight into all the contracts as well as compliance, risk and operational constraints. These include the following:

    • All contracts for receiving and delivering commodities
    • All logistics contracts and operational constraints for moving/processing commodities
    • All demand and supply forecasts
    • Current prices and the future outlook
    Figure 1: Portfolio optimization workflow.


    An optimization tool provides clear advantages for fuel marketing businesses in terms of both monthly and annual planning.

    Improved annual planning: At the start of a planning year, fuel marketing businesses carry out planning activities in which product demand is matched with supply to balance the supply chain and determine annual profitability targets. Given the complex nature of the supply network and the variability in supplier and customer contracts, creating a plan that is both holistic and insightful becomes very difficult. Traditionally, companies plan based on historical numbers and “guesstimates” derived from market conditions and business knowledge.

    For such scenarios, an optimization tool can help business managers make much more astute decisions in their planning by evaluating all the permutations possible and drawing out new insights. The optimization model can be used to run simulations by changing different variables and evaluating for maximum profitability.

    The model can provide results that consider circumstances of operational constraints at the lift and delivery locations, and the variability in supply and delivery volumes and costs. The real advantage of an optimization tool is the fact that what-if analysis can be done by simply changing the inputs to the model. These results, when compared with the previous model run results, can give powerful insights into the business such as:

    • What should be the allocation of products to the different sales channels in order to maximize profitability given the supply contract constraints which in turn will drive the sales effort?
    • How should the supply contracts be utilized given a mix of demand to various sales channels to maximize specific contract incentives?
    • What will be the impact to supply chain profitability if a new storage terminal is introduced at a specific location?
    • Will it be valuable to introduce a blending facility to sell a product spec versus buying from a third party? Under what price and volume ranges will it be profitable?
    • What will be the impact to the existing supply chain if a new supply chain network is acquired?

    Improved monthly planning: At a monthly level, fuel marketing businesses plan the nominations for next month’s deliveries, which will translate into weekly, biweekly dispatch plans for logistics operators. Traditionally, these plans have been driven by historical precedence rather than true economic value. Given the uncertainties, complexity and lack of tools for evaluation, people have simplified assumptions to develop a feasible and easy-to-use solution. Unfortunately, these shortcuts prevent all the options from being considered and, often, money is left on the table. An optimization model can consider various opportunities to identify the cheapest source for procurement as well as specific customer delivery, contract and logistics constraints. This helps businesses reach various goals such as maximum profitability or maximum contract utilization. The model can answer such questions as:

    • In case of limited supply, what contracts can be reduced within contract constraints while maximizing profitability?
    • What will be the optimal way to schedule distribution throughout the month if the market is in contango versus normal backwardation?
    • How can I meet the maximum demand in case of logistics constraints due to outages?
    • In which scenarios is it better to meet the requirements by doing spot contracts versus using long-term negotiated contracts without affecting or minimizing the impact to contract performance benchmarks?


    As an organization moves toward the adoption of better optimization tools, it is important to have a concrete user adoption plan in place. Four significant areas need to be covered as part of the change management process. These include:

    • Overcoming historical bias: The natural tendency is to go to the “safe” route and do what was done in the past. This will require challenging tribal constraints and assumptions that have built up over a period of time. To address this, the decision-making process needs to be actively assessed to check that data-driven insights, rather than historical decision bias, are driving decisions.
    • Overcoming silos: The organization will need to rethink the collaboration strategy along with the performance metrics between different groups to ensure that the individual metrics are aligned to the same goal. It must also ensure that when the model recommendations are executed, the impact is measurable. Modeling data from multiple groups (for example, distribution and supply) in an optimization tool can provide powerful metrics to ensure that strategic goals are met. Changing input data, running the model multiple times and then comparing the results can help identify areas of collaboration and ensure maximum profit for the organization as a whole.
    • User buy-in: New tools can generate uncertainties with employees who are threatened by them and feel their jobs may be in jeopardy. Leadership support is required to ensure they appreciate the fact that the more powerful decision support tools will help improve their decision making. Additionally, sufficient user training will be needed.
    • Data governance: The quality of output is dependent upon the quality of input data. Given the complexity of the supply chain and multiple systems in existing enterprise architectures, it is important to ensure the data is systematically captured, validated and automated to flow between systems. This helps to ensure that the quality of data is not compromised as it flows throughout the enterprise. Business users must be educated on the importance of good quality data—and should be instructed that exceptions need to be corrected.


    Fuel marketing companies will need to evolve to maintain or grow their market share in the increasingly competitive and volatile commodity market. The systematic incorporation of advanced analytics and optimization tools in the decisionmaking process will allow companies to swiftly respond to and capitalize on changing market conditions and gain a competitive edge.

    The Authors
    Pooja Malhotra

    Pooja Malhotra
    is a Manager at Sapient Global Markets with over 11 years of experience working primarily with energy firms in the oil and gas space. She combines a strong industry background with process and system knowledge to provide creative solutions for improving business operations. Her current work involves modeling and implementing mathematical optimization solutions for commodity trading, especially in the areas of oil supply chain optimization and natural gas supply chain optimization including gathering, processing, storage and distribution networks.

    Rathin Gupta

    Rathin Gupta
    is a Manager of Business Consulting at Sapient Global Markets with nine years of experience in business/ management consulting and investment banking. Most of his career has been in the oil and gas industry, particularly in the energy trading and risk-management space as well as supply and logistics.

    Rajiv Gupta

    Rajiv Gupta
    is a Director of Business Consulting at Sapient Global Markets based in Houston. He works in an advisory role with clients and provides project leadership in the commodities domain. Rajiv has over 15 years of full project lifecycle experience working with start-ups, utilities, investment banks and integrated oil majors across front, middle and back offices.

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