As energy merchants face tighter profitability, they need to look beyond reduced cost to efficiency and automation and explore innovative ways to identify new revenue opportunities around their current portfolio. Here´s a look at emerging approaches for harnessing emerging information services and technology capabilities to more systematically detect new revenue opportunities.
Over the last decade, it has become harder for many energy merchant organisations to continue growing their bottomline. The economic collapse of 2008, followed by the energy renaissance in the US that caused tectonic shifts in supply, demand and transportation, has made it more difficult for firms to discover new arbitrage opportunities using legacy methods. The problem is compounded by the fact that arbitrage signals tend to be shorter lived, with more constraints to consider in terms of markets and logistics, and potentially have smaller margins.
As such, to maintain a competitive edge, energy companies that are engaging in trading, whether they are producers, consumers, integrated or merchants, will need to look at more information at a deeper granularity and with faster turnaround to capture the value from potential opportunities. We have labelled this new approach to growing the bottom line: Energy Intelligence.
An example of how Energy Intelligence helps a global petroleum merchant (GPM Co.) is depicted in Figure 1. GPM Co. has title for a vessel of gasoline in the US Gulf Coast and a commitment to deliver it at New York Harbor (solid blue line in Figure 1), ignoring complexities of some physical constraints and specification differences across regions. The company hears about an event-a refinery on the West Coast has shut down due to a fire. GPM believes that this will create a temporary price hike in California and would like to take advantage of that by diverting its vessel to the West Coast (see Figure 1). But before it can make this call, it wants to be sure that there will likely be a price hike in California, and that it would still be able to cover its obligation at New York Harbor without losing all the gains from the West Coast. To rapidly evaluate these considerations, GPM looks at its intelligence map which shows who has ships where, what´s on board these ships and when they will reach their destination.
This information can be used to very quickly build a model for the specific date and price ranges that would work to monetise the situation. It can also be used to shortlist the companies or groups to contact externally with the relevant requests. This information allows GPM to gain a better qualitative and quantitative understanding of the near-term markets and how it should respond. Without the rapid and relevant compiling of information, GPM would not have been able to capitalise on the event because it was short lived and required immediate action.The assumption has long been that most events are highly visible, such as in this example. But as companies are pulling together their Energy Intelligence information, they are discovering that events, both large and small, are happening daily. The main concern is whether firms can see all of them, identify the impacts and decide whether to take advantage of them. Only companies with a robust Energy Intelligence capability have this visibility and insight.
To build an Energy Intelligence capability, firms must bring the fundamental analysis approach to the immediate and near-future using specific intelligence factors and associated data and models, because accuracy of long-term fundamental analysis of these forecasts drops dramatically the further out in time they are. The shorter time range increases the confidence level on accuracy sufficiently for the company to act on the results. The overall business process for this approach includes four key steps, as shown in Figure 2.
The first step is to gather all the data that is of interest and that relates to the relevant intelligence factors. Gathering includes finding the data sources, acquiring data through data service agreements, performing any validations and formatting necessary and storing the data.
The next step is to collate all the information that was gathered and prepare it for analysis. This includes data cleansing, correlation, map¡p¡ing and aggregation. Data collation and aggregations have to address the key problems of volume, velocity and variety- the 3 Vs of BigData.
Once the information is ready, quants and analysts are able to commercially analyse the data. Generally, this will start with some research and formulating hypotheses of where opportunities may lie-or simply mining the information gathered to explore opportunities. Then, a relevant model may be built that will show the ¨signal¨ from any large or small event.
The final step is for users to monitor for signals based on the previous analysis. This may be as rudimentary as preparing tabular reports or more sophisticated by using visualisations, such as a graph with data overlays allowing users to more quickly spot an opportunity. Adding complex event processing, as used in trade surveillance and other areas, could further enhance usability and speed.
Over time, not only will more companies have visualisation and complex event processing, but Energy Intelligence will evolve towards Artificial Intelligence. In fact, some companies are already exploring approaches such as these as a possible trigger for better signal detection which analysts then validate.
A number of recent changes are encouraging energy companies to consider Energy Intelligence:
To successfully build an Energy Intelligence capability, firms must:
Co-authored by Abhishek Bhattacharya
(Vice President at Sapient Global Markets based in India), Rashed Haq (Vice President of Energy Commerce at Sapient Global Markets based in Houston), and Charles Ford (Director at Sapient Global Markets based in Houston).
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