In a world driven by data, making the right decisions can be a complex task. Enter prescriptive analytics—a powerful tool that goes beyond merely analyzing data to actively recommending (“prescribing”) solutions.
From optimizing energy consumption to enhancing supply chain efficiency, reducing financial risks, and crafting personalized marketing strategies, mathematical optimization is the unsung hero behind many of today’s business successes. But how are companies harnessing this powerful tool, specifically? What are the real-world applications that are making waves across various sectors?
Although it is used across nearly all industries, let’s take a look at some specific prescriptive analytics example use cases.
Below are several real-world prescriptive analytics example use cases showing how prescriptive analytics can increase operational efficiency, maximize profits, and more.
Air France uses prescriptive analytics to optimize tail assignment for its fleet. By considering various constraints, such as maintenance requirements and regulations, the airline significantly reduces operational costs and increases efficiency, providing more reliable services to its passengers.
Energy trading company SESCO leverages mathematical optimization to enhance its energy trading portfolio. By considering complex variables like market volatility, regulatory constraints, and financial risks, SESCO creates an optimal trading strategy. This prescriptive analytics example not only maximizes profits but also provides a more resilient and adaptable trading system, ready to respond to ever-changing market conditions.
In the fast-paced world of retail, Blue Yonder uses mathematical optimization to determine optimal price points for entire product lines. By considering complex rules and product relations, the company makes real-time price adjustments, leading to a 5% increase in product sales and a 20% reduction in inventory. This innovative approach allows Blue Yonder to stay ahead of the competition and respond swiftly to market demands.
Snack giant Mondelēz International uses optimization to create shipping plans that maximize the use of vehicles at the lowest cost. By considering variables like capacity, lead time, and route availability, Mondelēz automates the shipping process, saving time and ensuring the most cost-effective solution.
Publicly-owned energy company Avista Utilities, uses mathematical optimization to produce more electricity with given resources. By developing the Avista Decision Support System (ADSS), powered by patented algorithms, Avista solves complex utility problems within seconds. This approach not only reduces manual decision-making but also helps in better utilization of resources, keeping maintenance costs low, and maintaining a balance of supply and demand.
Lyft, one of the largest shared mobility networks, uses mathematical optimization to influence positive market outcomes by providing driver incentives and showing drivers how they can increase their earnings. With prescriptive analytics, Lyft can quickly and easily provide incentives so drivers know where and when they will find riders.
With prescriptive analytics, ESUPS, a humanitarian logistics and preparedness project hosted by Welthungerhilfe, can optimally pre-position humanitarian aid to ensure that the appropriate relief items (such as blankets, tents, clothes, hygiene kits, sleeping mats, and kitchen items) are stored in the right places and in the right quantities, before a disaster hits.
For more real-world decision intelligence and prescriptive analytics examples, check out our full list of case studies.
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