OPERATIONS RESEARCH HAS EVOLVED INTO DATA ANALYTICS

OPERATIONS RESEARCH HAS EVOLVED INTO DATA ANALYTICS

It is just my own speculation—but perhaps not a wild one—that what used to be known as Operations Research (OR) has now evolved into the modern science of Data Analytics. And more than that, Artificial Intelligence (AI) has raised OR to a higher, stronger, and faster discipline.

I am not an economist, but I do know that economics is generally about managing scarce resources, while operations research is about maximizing those same resources. Economics defines scarcity; OR defies it.

If economics asks, “How do we divide limited resources?” then OR replies, “How do we make the most out of what we have left?” In a world drowning in data and limited in time, that distinction has never been more relevant.

From Equations to Insights

Operations Research was born in the 1940s, during World War II, when mathematicians were tasked to optimize everything from radar deployment to convoy routes. It was, in essence, the science of efficiency. It used mathematical models—linear programming, simulation, queuing theory—to make the most of scarce resources under pressure.

Fast forward to today, and those same mathematical foundations have found new expression in data analytics. Where OR once optimized military supply chains, data analytics now optimizes everything from hospital staffing to urban traffic flow, from agriculture yield to disaster response.

It’s not that OR disappeared—it evolved. The models became smarter, the data became richer, and the goals became broader. We are no longer solving just equations; we are generating insights.

Descriptive analytics tells us what happened. Predictive analytics tells us what might happen. And prescriptive analytics—OR’s spiritual heir—tells us what we should do next.

When Artificial Intelligence Joins the Equation

The real leap came when AI entered the picture. Artificial Intelligence didn’t replace OR—it turbocharged it.

Machine learning can now generate parameters automatically, simulate thousands of scenarios, and refine models based on real-time feedback. In traditional OR, analysts might spend days tuning a model; now, AI does it in seconds.

For example, reinforcement learning (a branch of AI) can find the best decisions in complex environments—just like OR did—but dynamically and continuously. That’s why hybrid systems combining AI and OR are now used in logistics, energy grids, and even public policy.

Think of it this way: AI makes predictions, OR makes decisions. Together, they make smarter systems.

Maximizing National Resources Through OR + AI

Here’s an idea worth exploring: what if we could apply OR and AI to the General Appropriations Act (GAA)? Imagine a data-driven system that allocates government budgets based not just on political priorities but on mathematical optimization—maximizing social benefit per peso spent.

We could simulate different scenarios: how much more health coverage could be achieved if funds were reallocated? How can road repairs be scheduled to reduce traffic and costs simultaneously? OR can model that; AI can predict outcomes; data analytics can measure success.

If corruption is the sin of commission, then inefficiency is the sin of omission—and both rob the people of progress. OR and AI, used together, could make the government more transparent, accountable, and effective.

Scarcity vs. Maximization

Economics, as defined by Lionel Robbins, is the science of human behavior as a relationship between ends and scarce means. But OR is the science of maximization—it starts where economics ends.

Economics may analyze how scarce resources affect the economy. OR will tell you how to use those same resources optimally.

For example:

  • Economics studies how burial land scarcity affects urban planning.

  • OR models the optimal use of cemetery space through columbaria or digital memorial systems.

  • Economics looks at incentives for aquaculture transitions.

  • OR simulates feed ratios and harvest schedules to maximize yield.

In short, economics helps us understand why systems fail or succeed. OR helps us design how to fix them. And AI ensures that the system keeps learning as it goes.

The Human Side of Numbers

Still, we must not forget that behind every algorithm are people. The danger in today’s data-driven world is that we might focus so much on optimization that we forget about compassion.

AI and OR should never be tools for exclusion or control—they should be instruments for service. Data must serve the people, not the system. If we use analytics to predict who gets aid, let it be to reach the vulnerable faster, not to deny them. If we optimize budgets, let it be for equitable distribution, not mere efficiency.

As systems thinkers often remind us: optimization without ethics is just automation of injustice.

A Final Thought

If economics is about managing scarcity, and OR is about maximizing resources, then perhaps AI and data analytics are about illuminating potential. They take the invisible patterns of society and make them visible, measurable, and actionable.

Imagine a Philippines where every barangay uses AI-assisted dashboards to allocate resources, manage waste, plan transport, and even predict flood risks. That is not science fiction anymore—it’s within reach.

So yes, Operations Research has evolved into Data Analytics—but only in the hands of those who dare to use it wisely. Because ultimately, the goal is not just maximization of numbers, but maximization of human dignity.

Ramon Ike V. Seneres, www.facebook.com/ike.seneres

iseneres@yahoo.com, senseneres.blogspot.com 03-09-2026

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