Piyush Priyam

General trade comprises over 90% of the CPG industry in India. This involves thousands of on-ground sales executives taking orders from millions of retailers. Every CPG company (e.g., P&G, Nestle, Unilever) maintains their sales force, which is costly and a deterrent to entering new geographies. Retailers, on the other hand, have to manage hundreds of sales executives to place an order and get stock.

I designed a single app where retailers can order and manage products and get delivery from all CPG companies. This reduced the salary cost of companies by up to 30%, saved retailers’ time, and eased the distribution process.

As the company’s founder, I brought the product idea to life through market analysis and user research, defining product vision and strategy, and creating the product roadmap. I also hired and managed a team of 25 comprising development, UX, QA, and DevOps resources.

The app was hosted on AWS and developed using Python (Flask framework). Google voice assistant and AWS forecast and prediction AI engines were used for purchase list functionalities.

Food cost accounts for 30-55% of overall expense in the restaurant business. Bad planning can lead to food wastage or stock-outs. Wholesale food ordering is a complex task involving multiple employees and many considerations, and the weekly purchase list could include 150+ items.

I ideated and designed a ‘collaborative list-making app’ to simplify this. The app included features like smart lists, draft lists, approval of lists, voice-based search, volume-dependent rates, and historical rate trends. I hired and led a team of 35 to build mobile apps for customer and sales executives.

Using the app, restaurant managers could reduce time spent on order planning by 75% and save 5% on food-cost.

At EZkirana, we bought and sold food grains like rice and pulses. Rates of these products fluctuated every week. If we bought the stock at higher prices, we might have to hold it and block working capital, risk wastage, or sell at a loss. Profitability was closely linked to these rate fluctuations.

To solve this problem, I designed a product-level price engine to predict wholesale rates a week in advance. This enabled us to quote competitive rates on the EZkirana website. We also had the freedom to buy and hold stock when prices were low.

I created an Excel model initially to incorporate the algorithm. Later, I worked with the tech team to develop the pricing engine that fed rates to the user-facing website and mobile app, and designed an ordering system that guided our purchase timing and quantity based on inputs from the engine. This also reduced EZkirana’s procurement planning time by 90%.