For manufacturers considering digital transformation, understanding the financial returns can be as important as understanding the technology itself. During a year-long industry project at the Amrut Mody School of Management, I, Pranjal Soni, developed a return on investment (ROI) calculator for a cloud-based Smart ERP System, translating shop-floor data into financial estimates to help businesses assess the potential value of their investment. The project taught me how to identify an unmet business need, turn operational data into financial insights, and build a tool to support business investment decisions.
Identifying an Opportunity Beyond the Obvious
When I joined the MBA programme, I expected case studies, business frameworks, and a fair share of PowerPoint presentations. What I didn't anticipate was that an Excel spreadsheet, developed over several months, would change how I approached business problems.
The project involved working with a company that positions itself as an accelerator of digital transformation for small and medium enterprises. Its Smart ERP System is a cloud-based platform that collects real-time shop-floor data through tablets, machines, and sensors, giving manufacturers greater visibility into their operations.
While evaluating competitors, I noticed that many offered ROI calculators, a tool the company lacked. This raised a practical question: how could we demonstrate the financial value of investing in the platform to prospective clients before they committed?
Rather than focusing only on marketing deliverables, I began exploring how a financial tool could help potential customers assess the costs and benefits of adopting the system.
Building an ROI Calculator from Scratch
Having never built an ROI calculator before, I started by studying existing tools. I examined the variables they used, how their calculations were structured, and what made their outputs credible and useful rather than merely impressive.
Next, I translated manufacturing operations into a workable financial model. I mapped key variables such as production days, shifts, machine counts, cycle times, scrap and rework rates, downtime, labour costs, and energy consumption.
I built the calculation logic around Overall Equipment Effectiveness (OEE), a widely used manufacturing metric based on three components: Availability, Performance, and Quality. The model explored how improvements in OEE could translate into additional production, potential revenue gains, operational savings, and a shorter investment payback period following the adoption of Smart ERP.
To account for differences between businesses, I incorporated three scenarios: conservative, realistic, and aggressive. A prospective customer could enter relevant operational figures, compare assumptions, and estimate the system's potential financial benefits and the time required to recover the investment.
The spreadsheet gradually evolved into a structured decision-support tool, with a focus on presenting the assumptions and results in a way that businesses could examine and use.
Learning to Think Beyond the Deliverable
The most significant learning from the project was how I came to define a good solution.
Initially, I approached the task as a student working towards a deliverable. Over time, I began thinking more like a consultant: What decision does the client need to make, and what information would genuinely help them make it?
Every variable I considered raised another question. Would including it help the client better understand their business, or would it add unnecessary complexity? Was the assumption defensible? Could another person follow the calculation and question its logic?
The project also taught me to work with ambiguity. There was no single textbook answer for selecting an appropriate scrap-rate benchmark for a mid-sized manufacturer or estimating the savings attributable to predictive maintenance and process optimisation. These questions required research, reasoned assumptions, and a model flexible enough to be reviewed and refined.
I began to see that calculations alone were not enough. Their value depended on interpreting the results, explaining the assumptions behind them, and communicating what they might mean for a business.
Carrying the Learning Into Consulting
Today, I work in strategy and business analysis, where I continue to apply the approach I developed during this project.
When building a business case, I start by identifying the decision the client needs to make. I then determine the variables that matter most, examine the assumptions driving the analysis, and build a model that the client can understand and scrutinise.
The ROI calculator also became a valuable part of my professional portfolio during interviews. What made it meaningful was not just the technical complexity of the spreadsheet, but the work behind it: identifying a gap, structuring an open-ended problem, and developing a practical solution with considerable independence.
Looking back, the project changed how I approach business analysis. I learned to look beyond the immediate task and consider what the work is meant to help someone decide. That question continues to guide my approach to consulting.