Organizations are increasingly focused on improving decision-making speed and quality. A growing number of them are turning to prescriptive analytics—a type of advanced analytics that not only analyzes data but also offers actionable recommendations. Let’s delve into what prescriptive analytics entails, examine some key use cases, and explore what’s fueling its rapid adoption.
What is Prescriptive Analytics?
Prescriptive analytics is often seen as the pinnacle of advanced analytics. While descriptive analytics reports on what has happened and predictive analytics forecasts future events, prescriptive analytics provides specific recommendations for optimizing future outcomes. It analyzes data, explores trade-offs, and suggests the best course of action to achieve business goals.
As explained by experts in the field, “Prescriptive analytics helps organizations balance trade-offs between conflicting objectives and determine the optimal path forward in complex decision-making scenarios” (Forbes, 2022). This means that businesses can move beyond simple data insights and take concrete, optimized actions based on their data.
Prescriptive Analytics, AI, and Mathematical Optimization
While prescriptive analytics often intersects with artificial intelligence (AI) and mathematical optimization, they are not exactly the same. AI encompasses a broad range of technologies designed to simulate human intelligence, including machine learning, natural language processing, and cognitive computing.
Mathematical optimization, on the other hand, involves framing business problems as mathematical equations that outline constraints and objectives. According to McKinsey, “Optimization models are central to prescriptive analytics because they provide a mathematical foundation for determining the best decision in complex environments” (McKinsey & Company, 2021). In essence, mathematical optimization is a type of prescriptive analytics, which helps businesses solve complex problems with precise, actionable solutions.
Prescriptive Analytics Use Cases
Initially used in industries such as energy extraction, prescriptive analytics is rapidly expanding across multiple sectors. As of 2018, the global prescriptive analytics market was valued at $1.9 billion and is expected to grow to $12.35 billion by 2026 (Allied Market Research, 2020).
A wide range of industries, including manufacturing, logistics, and education, are increasingly adopting prescriptive analytics solutions. For example, Volnei dos Santos, a technical director at a Brazilian consulting firm, highlights that “Optimization is now being used in industries like manufacturing, education, and even the beverage industry, beyond its original scope” (Unisoma, 2022).
In supply chain management, prescriptive analytics can be used to make decisions about where to establish new facilities, optimize inventory, or manage capacity planning. Other key applications include energy dispatch planning, materials blending, and replenishment planning.
Tools for Prescriptive Analytics
Prescriptive analytics tools have evolved significantly over the decades. AIMMS and similar platforms allow users to develop and deploy bespoke optimization applications much more efficiently. According to the Journal of Business Analytics, “Low-code platforms are enabling companies to build custom solutions for complex problems 10-20 times faster than traditional coding methods” (Journal of Business Analytics, 2023).
Historically, prescriptive analytics solutions were built by experts, and many companies still prefer custom applications tailored to their specific business needs. However, off-the-shelf solutions are also becoming more prevalent, as noted by Gartner, which states that “ready-made analytics solutions are growing in popularity as businesses seek faster implementation and scalability” (Gartner, 2021).
Why Is Prescriptive Analytics Gaining Traction?
Several factors are contributing to the increasing adoption of prescriptive analytics. Firstly, modern business environments are becoming more complex, and decision-makers often face millions of potential choices. “The growing complexity of supply chains requires advanced tools like prescriptive analytics to make optimized, data-driven decisions,” as highlighted by Supply Chain Dive (2022).
Secondly, the availability of large datasets and advancements in cloud computing have made deploying optimization models more accessible. Cloud computing allows companies to quickly operationalize prescriptive analytics models without heavy reliance on internal IT teams. This democratization of optimization has opened up new possibilities for businesses looking to scale their analytics capabilities.
Moreover, the return on investment (ROI) of prescriptive analytics can be substantial. For instance, logistics optimization models can deliver up to 30% savings, and in some cases, companies have identified millions of dollars in cost reductions within the first week of deploying these models (Harvard Business Review, 2023).
Is Your Organization Ready for Prescriptive Analytics? As prescriptive analytics continues to evolve, more companies are realizing its potential to deliver significant value. From demand forecasting to real-time decision-making, prescriptive analytics empowers organizations to leverage data-driven insights for smarter decision-making. Whether you’re looking to optimize your supply chain, improve manufacturing processes, or enhance customer service, prescriptive analytics offers the tools needed to stay competitive in today’s fast-paced business landscape.
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