Supply Chain & Inventory Analysis Dashboard

Overview

Every business generates data, but very few actually use it to make decisions. This dashboard was built for a six-store bakery chain that had years of ordering, delivery, and inventory records sitting unused, with no way to see which suppliers were reliable, why certain ingredients kept running out, or how much money was being lost to spoilage and missed sales. We took a full year of that raw data and turned it into a interactive visualization dashboard spanning nine report pages, covering stockouts, wastage, supplier performance, and seasonal demand, along with a written report summarizing the key findings and what to do about them.

This is exactly the kind of transformation we help our clients with. Most businesses already have the data they need to solve their biggest operational problems, it's just scattered across spreadsheets, invoices, and systems that don't talk to each other. Our job is to bring that data together, build a dashboard that actually gets used day to day, and back it up with a clear, plain-language report so decisions don't have to wait on someone digging through numbers manually.

The analysis surfaced patterns that weren't visible before the data was brought together in one place. Supplier deliveries were arriving on time only 55% of the time, and this single issue turned out to be the main driver behind most of the stockouts across the business. Those stockouts weren't spread evenly either, they were concentrated almost entirely in perishable ingredients with short shelf lives, while longer-lasting items rarely ran short at all. Two of the six stores were also found to be disproportionately responsible for the chain-wide numbers, giving a clear starting point for where to focus first. Altogether, stockouts and wastage were costing an estimated PKR 4.39 million a year, a specific, recoverable figure rather than a vague sense that something wasn't working.

Based on these findings, we put together a set of practical, prioritized changes rather than a long list of generic suggestions. This included renegotiating delivery schedules with suppliers that consistently ran late, shifting to smaller and more frequent orders for perishable ingredients to reduce both stockouts and spoilage, and building seasonal stock buffers ahead of predictable demand spikes. We also recommended starting any changes at the two highest-impact stores before rolling them out chain-wide, so improvements could be tested and confirmed before wider investment. This is the kind of output we aim for with every dashboard we build, not just numbers on a screen, but a clear set of next steps a business can act on immediately.

If your business is sitting on data you don't currently have the time or tools to make sense of, this is the kind of outcome we can build for you, a dashboard your team will actually open every week, and a set of recommendations you can act on immediately.

Note: For demonstration purposes, this dashboard is built on a synthetic dataset and does not represent real data.

Raw Data

The analysis was built on five linked datasets covering a full year of operations: a store list (6 stores), a product list (12 ingredients with category, unit cost, and shelf life), a supplier list (6 suppliers with lead time and reliability scores), a daily inventory table (26,280 rows covering opening stock, quantity received, quantity used, quantity wasted, closing stock, demand, and stockout flags per store and product), and a purchase order table (3,141 rows covering order date, quantities ordered and received, unit cost, expected and actual delivery dates, and delivery status).

Dashboard