Prescriptive analytics helps energy companies to analyze trading of energy

Prescriptive analytics is no longer a financial approach as it is being used across diverse industries to gain insights to drive better decision making. Combining descriptive and predictive analytics, prescriptive analytics refers to the third and final phase of business analytics. As energy trading companies look to optimize the value of their assets and speculate on future commodity price movements, prescriptive analytics can be an effective solution to drive trading strategies.

Energy trading deals with a broad range of market rules and regulations that have diversely been established around the world. It involves products like crude oil, electricity, natural gas and wind power. Trading in energy is typically conducted by asset-trading companies that use production, demand and price forecasting to optimize the revenue created from energy production.

Emergence of Energy Trading

Energy trading has its roots back in 1978 when the first oil futures contract on the New York Mercantile Exchange (NYMEX) was placed. During the 1980s and 1990s, the International Petroleum Exchange (IPE) and NYMEX successfully launched futures contracts for oil and gas. These successful futures exchanges survived the Enron et al. energy-trading catastrophes of recent years and demonstrated their capable financial performance. In today’s fast-moving world, oil companies and financial institutes provide the necessary trading liquidity through market-making in consent with the established government-regulated futures exchanges and off-exchange energy derivatives markets.

Prescriptive Analytics in Energy Trading

The energy trading industry in recent years has emerged extremely as more and more energy organizations have made strategic positions and focused on creating their geographic market presence. However, many energy trading companies today are facing the legacy of past trade-offs where time to market and product innovation prevailed over considerations of cost and complexity. While energy traders make decisions regarding how much energy they need to buy or sell, at what prices, in which markets, etc., they require complex tools and techniques with a very high degree of sophistication that can provide the optimal solution.

Prescriptive analytics offers energy trading companies a competitive edge. It has the ability to optimize decision-making from multiple perspectives such as demand shaping, financial planning, capacity planning, supply planning, inventory planning, product mix and profitability, customer service policy and much more. Prescriptive analytics also provides insights into how to lower costs and considers other possibilities with new environments and extraction techniques. This analytics approach can be incorporated into integrated business planning to make better decisions, faster and with better execution from production to finance.

In energy supply chain optimization, which is a crucial part of energy trading, prescriptive analytics solves the basic problem of finding the best solution to meet objectives. It uses metaheuristic optimization solver algorithms to minimize or maximize an objective, such as refine margins, increase revenue 5 percent, etc., while meeting global business constraints like contractual, credit, risk and regulatory. 

Moreover, prescriptive analytics synthesizes big data, disciplines of mathematical and computational sciences, and business rules to make predictions and suggest actions to take advantage of the predictions.