Chris McManaman

The power of energy supply chain optimization and prescriptive analytics

The ever-increasing complexity of the global energy marketplace is making optimization even more important. With easier access to powerful tools, more companies are turning to optimization to help make crucial decisions for tasks such as hedging strategies, storage management, inventory allocation, transportation route selection, product optionality, blending and optimizing portfolios, and more.

All functions in a company are usually aimed at a single goal—to make more money by selling more products at a higher margin. However, this goal often is forgotten when the operations are so myopic and siloed that each portion of the business (e.g., supply chain, storage and processing, procurement, etc.) has its own metrics and, too often, narrowly pursues them to the detriment of the company.

Unfortunately, much optimization work today is done in spreadsheets and is focused on solving a simple set of decision variables within a single functional silo. Models are not integrated despite the fact these critical planning decisions are most certainly interrelated in the real-world. Any high-level optimization effort requires a compilation of output from various spreadsheets and applications across the enterprise.

The growing need for advanced analytics in the energy supply chain

The science of business analytics will continue to evolve at exponential speeds due primarily to unprecedented data availability and the increasing strength and computing power of the various tools and capabilities. Other analytics drivers in this sector are intense competition, cost and revenue pressures, changing customer expectations, stringent regulatory requirements and geopolitical factors.

Analytics are used not only to report on past performance, but also to digest large quantities of information to predict the future and recommend actions to help analysts see the forest for the trees and avoid being locked into current dogma (see chart). The discipline of prescriptive analytics optimizes decisions to determine the best course of action based on changing conditions.

Use To

Descriptive analytics

Understand historical performance, be alerted to events, spot trends

Diagnostic analytics

Visualize and interact with data, discover relationships, explain outcomes, events and trends

Predictive analytics

Answer questions about the future or determine likelihood of unknown outcomes

Prescriptive analytics

Optimize decisions, perform integrated business planning or determine best course of action based on changing conditions

Improving performance with prescriptive analytics

Most businesses have a complex set of nonlinear relationships with constraints across demand, supply and financials. Senior management’s job is to gain clarity and determine the actions to be taken at all levels, such as where to allocate capital, which products to fund and cut, what policies to establish, and when to schedule operations. These actions all are designed to maximize the company’s primary goal.

Prescriptive analytics can be transformative for the trading and supply chain management function in realizing performance improvements by:

  • Providing forward-looking insights
  • Aligning the enterprise to the optimal course of action
  • Quantifying trade-offs fast and with a low cost of ownership
  • Increasing the ability to communicate and collaborate across functions

With the ability to apply optimization to these scenarios, trading executives can discover significant value.

Deciding “what’s best” with integrated business planning and analytics

Companies gain tremendous value when applying prescriptive analytics to make better decisions. First, users gain accuracy by modeling business processes and constraints in greater detail. Second, decisions improve as the software deals with complexity to find a better answer and support what-if analyses. Finally, the business gains agility by analyzing only the best scenarios and through deeper organizational learning. These themes—accuracy by modeling, software to handle business complexities, and business gains by analyzing best scenarios—are central to an organization’s finance function. Other departments often look to the trading function for its expertise in these areas.

Prescriptive analytics goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option. For example, energy supply chain strategic planning can benefit from using analytics to leverage operational and supply and consumption data combined with data of external factors such as market prices, supply and demand, congestion, weather, exchange rates and volatility.

Another example is energy and utilities. Natural gas prices fluctuate dramatically depending upon supply, demand, econometrics, geopolitics, and weather conditions. Gas producers, transmission (pipeline) companies and utility firms have a keen interest in more accurately predicting gas prices so that they can lock in favorable terms while hedging downside risk. Prescriptive analytics can accurately predict prices by modeling internal and external variables simultaneously and also providing decision options and showing the impact of each decision option.

There’s no question that advanced analytics will differentiate companies in the energy marketplace. Those who do not integrate advanced analytics into their everyday operations run the risk of falling behind.

I invite you to read more on this topic in our white paper, Using Prescriptive Analytics to Optimize Your Energy Supply Chain.

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