From Dashboards to Decision Engines

In a compelling keynote at DELIVER America 2025, Niraj Jha, Senior Director of Logistics at Niagara Bottling, painted a vivid picture of where logistics and retail operations are headed. His message was clear: the future belongs to companies that evolve from simply monitoring performance to building decision engines that act on it.

He opened with an analogy. Imagine a GPS that, instead of providing directions, floods you with coordinates, temperatures, and traffic statistics. “After a few minutes, you’d go crazy,” Jha joked. “You don’t need more data — you need direction.”

It’s a problem that plagues operations teams everywhere: endless dashboards, data silos, and KPIs that describe what happened rather than prescribe what to do next.

The Problem with Dashboard Dependency

Jha introduced “Joe,” a fictional operations manager drowning in dashboards. Every Monday, Joe checks metrics, responds to emails, and tries to interpret what his data means. By Thursday, he’s still reacting to last week’s problems.

“Our dashboards show everything — but they don’t always translate into decisions,” Jha said. “We end up reacting to what’s already happened, not preparing for what’s next.”

For decades, dashboards have been invaluable. But Jha argues they’ve become a comfort zone — a reactive layer of management that traps supply chains in the past.

Decision Engines: From Reactive to Proactive

Jha envisions a shift toward optimization and decision engines — frameworks that synthesize data from planning, production, logistics, and finance to generate actionable recommendations in minutes, not days.

He gave an example of a “Monday morning rush order” scenario: a major retailer demands 50 truckloads after a weather event. Instead of scrambling through spreadsheets, a decision engine could instantly calculate inventory availability, transport options, costs, and customer SLAs — delivering a clear, profitable recommendation.

“The GPS gives you directions, but you’re still the driver,” Jha explained. “The system tells you the best path, but you decide when to accelerate, when to stop, and when to change lanes.”

These engines don’t replace human judgment; they amplify it.

Intelligent Forecasting and Proactive Supply Chains

A core element of this transformation is forecasting intelligence — integrating live data on weather, demand, and macroeconomic factors to anticipate disruption.

Traditional forecasting relies on historical data. Jha argued this model is no longer sufficient. Instead, companies must adopt causal intelligence — using dynamic data to sense, simulate, decide, act, and learn continuously.

“Good companies react,” he said. “Great companies prepare. The difference is foresight.”

This proactive mindset allows supply chains to diversify carriers before risk builds, optimize inventory levels dynamically, and respond instantly to production downtime or shifting consumer demand.

AI in the Factory: From IoT to Insight

Jha extended his argument beyond logistics, showing how AI and IoT are reshaping factory floors. He shared a relatable scenario: a $50,000 machine bearing nearing failure. The old-school response? Replace it early and accept costly downtime. The modern response? Use sensor data and predictive analytics to delay the repair until the exact moment it’s needed.

“The truth lies somewhere between winging it and replacing everything early,” Jha said. “AI helps us find that middle ground — precision instead of guesswork.”

By merging sensor intelligence with decision engines, companies can optimize maintenance, avoid waste, and align production efficiency with sustainability goals.

The Human Factor: From Repetition to Strategy

One of Jha’s strongest messages was cultural, not technical. AI and automation shouldn’t eliminate people — they should elevate them.

“The more you make people do repetitive work, the more their intellectual capital drops,” he said. “Smart people don’t want to press buttons all day. They want to solve problems.”

Automation can remove the “boring and mundane,” freeing teams to focus on higher-order strategy and innovation. This shift, however, demands change management — new incentive structures, training, and leadership support to build trust in technology.

A Cultural Shift Toward Digital Empowerment

Jha urged leaders to start small. Don’t wait for perfect IT systems or expensive vendors. Instead, experiment with the tools already available — even consumer-grade AI like ChatGPT.

“No vendor knows your business better than you,” he stressed. “Don’t ask someone else to make you smarter — start by digitizing your own tribal knowledge.”

The real challenge, he said, isn’t data or technology. It’s mindset. “Every major transformation in history — from mechanized farming to the internet — followed the same pattern: skepticism, collapse, and slow adoption. But eventually, the world changes around you.”

The Future of Operations: Augmented, Not Automated

Jha closed with optimism. Automation won’t erase the human role in logistics — it will redefine it. Success will come from those who blend machine intelligence with human creativity, turning static data into live decision ecosystems.

“Constraints don’t disappear — they migrate,” he concluded. “It’s up to us, as operators and managers, to decide whether we’re ready for that new world.”

At DELIVER America 2025, his message resonated: AI isn’t a moonshot — it’s a management shift. And those who master it now will define the logistics leaders of the decade ahead.