A Q&A with Vikram Srinivasan, Head of Product Strategy, Optilogic

The Case for Faster, Smarter Supply Chain Decisions 

Across industries, supply chain leaders face growing pressure to deliver faster, smarter decisions that balance cost, risk, service, and sustainability. The latest Gartner CSCO Priorities Report underscores this shift: 72% of chief supply chain officers say they are responsible for driving enterprise growth—not just functional performance.  

Of course, adapting supply chains at the pace that business demands is easier said than done. 

The challenge is not the lack of analytics, solvers, or software. It’s the data — or more precisely, how difficult it remains to access, prepare, and use data to inform strategic and tactical decisions. 

For Vikram Srinivasan, Head of Product Strategy at Optilogic, this is the defining challenge — and opportunity — for supply chain leaders today. 

In this conversation, Vik unpacks how we arrived at this bottleneck, how it’s affecting supply chain decision-making at scale, and what it takes to move from reactive, one-off projects to fast, repeatable decision cycles. 

Q: Supply chain teams are being asked to answer more questions, for more people, more often. Vik, how has this demand evolved over the past few years? 

Vik: “What used to be a few large strategic questions per year is now a constant stream of tactical, localized decisions. Leaders are asking: ‘What if a route fails due to flooding? What if tariffs change next quarter? What if we shift production across regions?’ These questions aren’t hypothetical anymore—they’re urgent.” 

According to the 2025 Gartner Hype Cycle for Supply Chain Strategy, the enterprise is moving from episodic modeling to continuous design, where models and scenarios inform decision-making across the entire supply chain lifecycle.  

“We’re seeing that reality on the ground,” Vikram adds. “Planners, procurement teams, and regional leaders all want input — not just strategy teams.” 

This new cadence requires workflows — and those workflows shouldn’t need to be rebuilt every time.  

“Speed doesn’t just mean faster processingby a single team member once,” Vik says. “It means repeatability. A model or workflow built by one team should be usable by others—instantly and confidently.”

Q: From your perspective, why is data — not algorithms or solvers — becoming the biggest bottleneck in supply chain design today? 

Vik: “Data is the bottleneck because the systems we use weren’t built for supply chain-specific design or decision-making. ERP, TMS, WMS — these tools are built for transaction execution. They don’t contain the context, history, or structure needed for supply chain decisions.  

Data lakes and data warehouses — while good for storage and making data more accessible across the enterprise — lack the semantics and end-to-end supply chain context to support repeatable, collaborative decision-making.” 

Even when centralized, data remains fragmented, misaligned, and difficult to translate into decision-ready inputs. 

“Business users don’t just want clean data—they want contextual data they can action on. Data that understands what a supply chain is, how lead times affect service, or how cost interacts with risk.”— Vikram Srinivasan

Q: You often say modelers spend 80% of their time on data. Why hasn’t that changed? 

Vik: “We often see multiple teams solving the same problems — from scratch and sometimes manually — which is unacceptable in the age of cloud platforms and AI! A new data pull. A new cleanup step. A new join. And they do it without visibility into what other teams have already done.” 

Modelers, analysts, planners, and decision-makers must often gather fragmented, incomplete, or inconsistent data from across the enterprise just to begin their work. They do the hard, often heroic work of transforming chaos into clean, usable inputs for decision-making — usually without visibility into what others have already done. 

And when it’s done, they rarely have an easy way to share that work. So the cycle repeats. 

“There’s a lot of time being spent—redundant time—on doing similar data connections, trying to collate the same data, because it’s so siloed.” Vik says. “You’ve got people in different geographies or business units writing near-identical transformations — when they could be reusing and adapting each other’s work.” 

“When one team sets up a connection, everyone should be able to use it. When one user builds a workflow, others should adapt it. That’s what unlocks true agility.”— Vikram Srinivasan

The result? Valuable time is lost, institutional knowledge isn’t captured, and agility suffers. Too often, decades of expertise live in one person’s spreadsheet — impossible for others to use or scale. “The solution isn’t just faster tools,” Vik says. “It’s making every workflow reusable, every connection shareable, every insight portable.” 

Q: What role does team structure and skillset play in this? 

Vik: “The gap isn’t just technical. It’s organizational. The typical path from data to decision goes through four or five different people — IT, data engineering, data science, modeling — and each handoff adds delay and risk.” 

This structure made sense when design was infrequent. But in today’s environment, where decision cycles must match business cycles, it’s too slow. “That’s why we’re seeing the rise of hybrid teams—modelers, analysts, planners—working side by side in a shared environment.” 

Reusable workflows are the foundation for these new ways of working. “You might have a lead modeler or designer build the workflow, but now the business analyst can tweak the filters, the planner can run the scenario, and no one has to start over.”

Q: How is Optilogic’s approach with DataStar different? 

Vik: “DataStar is built for scale, collaboration, and speed. It’s not just another ETL — it’s an AI-powered environment that combines data prep, analytics, machine learning, and optimization in one seamless workflow.  

Teams connect to enterprise systems once — Snowflake, SAP, flat files, and more — and then build, reuse, and publish everything in the same space. And with the growing library of supply chain-specific templates in DataStar, users can jumpstart everything — from sourcing strategy to inventory policy — in minutes.” 

The power of the platform lies in team-wide enablement. “With our Teams functionality, workflows are visible, reusable, and improvable by everyone on the account,” Vik explains. “You can go from data ingest to decision-ready in one space, and you don’t need to be technical to participate.”  

Features like AI-assisted workflow building, machine learning for pattern recognition, and low-code app deployment make this especially impactful.  

“Even business users can now cleanse data, create decision workflows, and deploy apps to answer questions without waiting for IT or team members with specific skillsets.” 

Q: And what’s the broader vision of DataStar — for executives and their teams? 

Vik: “Executives want decision velocity… But if your modeling pipeline takes three months, you’ve already missed your window. That’s not agility. That’s lag.” 

This vision hinges on more than better tooling — it requires a cultural shift. “We’re moving from project-based decision-making to platform-enabled decision-making,” Vik says. “It’s not about one person solving one problem — it’s about building capabilities that the entire organization can leverage.” 

The outcome? More decisions made in less time, with less rework, and better business impact. 

From One-Time Projects to Scalable, Shared Decision Workflows 

For modern supply chains, adaptability is no longer optional — it’s the foundation of competitiveness. Yet most decision workflow efforts remain stalled by repetitive data prep, siloed workflows, and a lack of reuse across teams.  

As Gartner points out, companies that adopt continuous design and decision agility will outperform their peers by more than 30% in responsiveness and scenario readiness by 2027. 

DataStar redefines the decision workflow as a shared capability — enabling speed, repeatability, and team-wide engagement. It combines AI with deep supply chain context as well as machine learning and analytics to help users transform data into decisions faster.  

Teams move quicker, business users can self-serve, and decision-makers act with confidence. The result isn’t just better answers — it’s a better way to get there. 

Because in supply chain today, speed isn’t just an advantage — it’s a requirement. And reusable, collaborative decision workflows are how to get there. 

See how DataStar can help your team transform fragmented data into decision ready insights—faster and with less rework. Be among the first to experience the future of supply chain decision-making.