Reducing Latency in Your Supply Chain Decisions with AI
By Sai Mali Ananthanarayanan, PhD, Co-Founder & Chief AI Officer, Teragonia
In the high-pressure world of private equity-backed operations, we often confuse “data availability” with “decision speed.” We assume that because we have real-time dashboards showing us that a shipment is late or a margin is eroding, we are somehow “data-driven.”
But there is a silent killer in most supply chains that no dashboard can display: Decision Latency.
This is the gap between knowing something has changed and doing something about it. In traditional operating models, that gap is measured in days or weeks—time spent in meetings, waiting for approvals, or debating which spreadsheet to believe.
For PE operators and supply chain executives, shrinking this latency is no longer just an efficiency play; it is the difference between hitting your EBITDA targets on schedule or explaining why you missed them.
For the last decade, the industry response to volatility has been to invest in Decision Intelligence, which is used to describe using AI to build better predictive models. The logic goes: “If I can predict demand 5% more accurately, I can solve my inventory problem.”
This logic is only partly true. Decision Intelligence is excellent for simulating scenarios like pricing elasticity or budget modeling. But it often stops short of the finish line. It models the future with a probability without closing the loop to execution. And even when the signal is right, manyteams spend time debating whether to trust it. Without verified, auditable AI recommendations, speed is irrelevant; the organization stalls at the moment of decision anyway.
Most supply chains don’t fail because they lacked a forecast; they fail because the organization couldn’t coordinate a response fast enough. Decision Intelligence tells you what might happen. It doesn’t tell you what to do now.
This is where Value Orchestration changes the game. Unlike traditional analytics that report on the past, or Decision Intelligence that models the future, Value Orchestration focuses entirely on prioritized action, right now.
It operates at the apex of your data stack, linking operational signals directly to execution levers across finance, commercial, and operations. The metric that matters is the cycle time from signal, to decision, to execution, to measured outcome.
When we apply this to the supply chain, the question shifts from “What will demand be?” to “Given this demand signal, what is the single highest-value action we can take today to protect margin?”
“Unlike traditional analytics that report on the past, or Decision Intelligence that models the future, Value Orchestration focuses entirely on prioritized action, right now.”
By moving from passive reporting to active orchestration, supply chain leaders can compress decision cycles from weeks to hours. Here is what that looks like in practice:
In a standard Business Intelligence setup, you might get a red alert on a dashboard when inventory drops below a safety threshold. You then email a planner, who downloads a report, checks with finance, and maybe places an order three days later.
In a Value Orchestration system, the AI detects the forecast error risk early and triggers a fully-formed recommendation:
- Quantifies the risk: “Potential $50k revenue loss in Region B.”
- Recommends the move: “Transfer stock from Region A (overstocked) to Region B.”
- Routes the action: Pushes the recommendation directly to the Logistics Manager to action with a one-click approval.
The decision latency drops from days to minutes.
When a supplier misses an OTIF (On-Time In-Full) target, it often takes a quarter for the vendor management team to notice the trend and renegotiate.
Value Orchestration systems monitor variances in real-time. When a “supplier risk” signal crosses a defined threshold, the system triggers a BizOps play. It might automatically flag incoming invoices for review or prompt the procurement lead to activate a backup supplier for the next cycle. The system orchestrates the response across finance and operations, ensuring the “miss” doesn’t bleed into margin erosion.
One of the most common leaks in PE portfolios is the disconnect between logistics costs and product pricing. If tariffs or shipping rates spike, Finance sees it in the GL (General Ledger) at the end of the month, but Sales is still quoting old prices.
Value Orchestration bridges this silo. It detects the “landed cost variance” immediately and triggers a pricing guardrail. It alerts the Commercial team: “Product X margin has dropped 4%. Update the floor price or seek approval.” This cross-functional alignment protects EBITDA before the deal is signed, not after.
The technology to do this, cloud-native data stacks, accessible AI, and orchestration layers, is finally mature. The barrier is organizational much more than technical.
For private equity-backed companies, the imperative is speed. Traditional planning cycles are too slow for the current market pace. You cannot afford to wait for the Monthly (or Quarterly) Business Review to make a decision that should have happened on Tuesday.
By adopting a Value Orchestration approach, you shift your supply chain from a reactive cost center to a proactive value engine. You stop analyzing “what happened” and start executing on “what’s next.”
“By adopting a Value Orchestration approach, you shift your supply chain from a reactive cost center to a proactive value engine. You stop analyzing “what happened” and start executing on “what’s next.””
If you are being asked to move faster with leaner teams, the answer is rarely more headcount. It’s tighter loops between the signal and the person who can act on it, with less friction at every handoff.
The question worth sitting with: how many decisions in your organization last week were made on Wednesday that should have happened the previous Friday? And what did that lag cost you?
An accomplished applied mathematician, Mali specializes in machine learning, statistics, and optimization. With a PhD from Columbia University, his career highlights prior to joining Teragonia include optimizing portfolio strategies for Barclays, developing large-scale airline disruption recovery algorithms for GE Research, and improving elevator queue efficiency in NYC’s largest government office building.
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MS Analytics | Georgia Institute of Technology
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Former analytics engineer at Cyderes and ConocoPhillips with a Master’s in Analytics from Georgia Institute of Technology and a Bachelor’s in Management Information Systems from Oklahoma State University
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Grace Sun
Bachelor’s in Finance & Accounting | Georgetown University
Former analytics engineer at Houlihan Lokey and financial analytics at JP Morgan Chase with a Bachelor’s in Finance & Accounting at Georgetown University
Grace is a seasoned analytics engineer with specialized expertise in crafting and implementing analytics solutions that drive agile, informed executive decisions in M&A and value creation for private equity-backed companies.
Before joining Teragonia, Grace was a part of the data science and business analytics team at Houlihan Lokey. She has excelled in harmonizing, enriching, and analyzing data from diverse sources, providing key insights that enabled private equity investors and portfolio company executives to make rapid, data-driven decisions across the investment lifecycle. She has developed novel analytics solutions, including deal sourcing and evaluation tools for platform investments that employ a buy-and-build or de novo growth strategy, as well as post-close value creation and KPI reporting tools for operators and management teams.
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