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Introduction: Discovering the Challenge in Demo Preparation
Enterprise planning software has become a cornerstone in how organizations manage and optimize their supply chain operations. As someone still relatively new to this expansive and intricate domain, working with such platforms has given me a strong foundation in understanding the complexities of supply chain networks and their orchestration. However, one persistent challenge remains, replicating a supply chain environment within these systems often proves to be a painstakingly manual and time-intensive process.
The Bottleneck: Manual Data Population
Having worked on several POCs and demonstration environments for different clients, one issue has consistently resurfaced: the considerable amount of time required to manually generate the data needed to simulate a supply chain network.
For the POC we reconstruct a network using client-provided diagrams or data for the POC, which is further refined to the software template. On the other hand, for a Demonstration, to build it from scratch based on a company's operational profile.
To faithfully replicate a supply chain environment in the software, users must populate some foundational worksheets, each critical for enabling key dashboards and functionalities such as demand planning, supply planning, and capacity analysis. Only after this foundation is laid can we move on to implementing the specific use cases requested by the client. Unfortunately, the most time-consuming part of the entire exercise is precisely this: preparing and populating these interdependent worksheets manually.
Firsthand Experience: Where Time Slips Away
As someone working closely with various such demos/POCs on one of the many Enterprises planning software there are, I’ve experienced firsthand how tedious and error-prone this process can be. Each demo/POCs requires the manual creation of numerous interconnected sheets that define elements like parts, sites, customers, suppliers, planning categories, and timelines.
With dozens of columns, thousands of rows, intricate dependencies, and contextual variables, errors are inevitable, ranging from spelling mistakes and date mismatches to misaligned quantities and incorrect mappings. These issues often result in multiple rounds of corrections and uploads, significantly delaying progress and diverting focus from the actual use cases we intend to work on and showcase.
Breaking Down the Numbers: The Time Cost of Manual Work
Consider this: on average, nearly 45% of the time spent on a POC goes into manual data population. Consequently, only about 40% of our time is available for tailoring the demo to client-specific scenarios. This imbalance hampers efficiency and adds unnecessary stress to project timelines.
The Turning Point: Bringing AI into the Picture
That led to the inevitable question: “How can we fix this?”
My colleague Ravi Kumar and I began to explore this recurring bottleneck and realized there was a simple yet powerful solution, AI. Having seen the transformative potential of AI in other domains, we thought, “Why not apply it here?”
The task of data generation is repetitive and rule-based, making it an ideal candidate for automation. Our idea was straightforward: develop a structured input format, feed it into an AI model, and let it generate the bulk of the dataset for us.
The Solution: Instruction List + Prompt = Smart Automation
We began by designing a master instruction sheet for one such Enterprise planning software. This sheet outlines the structure of the client's supply chain across 11 core tables, including Parts, Sites, Customers, and Suppliers, along with configuration options such as currency, lead times, planning categories (e.g., Forecast Planning, Demand Planning), and editable fields like dates and quantities.
Once the instruction list is complete, we upload it, along with a template Excel workbook, into our AI-powered tool, accompanied by a carefully curated prompt. Within minutes, we receive a fully populated Excel file containing thousands of clean, structured records, something that would otherwise take days to prepare manually.
The Results: Faster, Better, Smarter
- Drastic time reduction: Data preparation, which once consumed up to 45% of our time, now takes less than 10%.
- Better personalization: We now have more time to refine client-specific use cases, resulting in demos that are not only tailored but also more compelling.
- Scalability: With faster turnaround times, we can manage more demo requests in parallel, allowing us to scale without compromising quality.
- Business growth: Increased efficiency has directly contributed to higher demo volumes, better conversion rates, and ultimately, stronger revenue growth.
Looking Ahead: Scaling and Evolving the Process
We’re actively refining the instruction list to make it more intuitive and user-friendly. Additionally, we’re exploring opportunities to scale this AI-driven approach across other operating environments and use cases, aiming to bring similar efficiency to all parts of the solution delivery pipeline.
Conclusion: AI as an Enabler of Operational Excellence
This journey has underscored a vital lesson: AI is not just a tool for analysis; it’s a catalyst for operational excellence. By automating one of the most repetitive yet essential tasks in the software’s demo lifecycle, we’ve unlocked the time, clarity, and creative bandwidth needed to deliver more value to our clients.
AND THIS IS JUST THE BEGINNING.