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A $2M Overstock That Started With a Forecasting Tool Doing Its Job Correctly
A demand planning tool recommended 44,000 units on a SKU. Actual need was under 6,000.
The tool wasn't broken. It was doing exactly what it was built to do, project forward off transaction history. The problem was upstream: pre-order transactions were feeding into that history as if they were confirmed demand, and nobody had gone back to check that assumption since onboarding, months earlier.
Software doesn't know the difference between a pre-order and a sale unless someone tells it. Nobody had. The result was a single buy that put roughly $2M into overstock on one SKU.
This gets filed under "forecasting problem" a lot. I'd call it a data governance problem. The software ran a clean calculation on dirty inputs, and the only fix is someone who understands the business well enough to sit inside the configuration before the number becomes a purchase order.
This is the layer I work in with 7-8 figure Shopify DTC brands not building another forecasting tool, but making sure the assumptions feeding it are actually true before they turn into inventory sitting in a warehouse.
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"We have more data than ever before, yet we're making decisions with less confidence."
A CEO made this observation during a strategy meeting after realizing that every department was producing reports, but none of them told a consistent story.
Finance had one version of the numbers.
Operations had another.
Sales had its own spreadsheets.
By the time leadership consolidated the information, the opportunity to act had already passed.
This is not an isolated challenge.
Across industries and around the world, organizations generate vast amounts of data every day—from finance, sales, operations, procurement, supply chains, HR, and customer interactions. Yet many continue to rely on fragmented spreadsheets, manual reporting, and disconnected systems that slow decision-making and obscure critical business insights.
The issue is rarely a lack of data.
It is the absence of systems that transform data into timely, reliable, and actionable intelligence.
At Intersect Analytics, we help organizations bridge that gap.
We don't simply build dashboards—we redesign reporting processes, modernize financial and operational workflows, automate repetitive tasks, and develop business intelligence solutions that empower leaders to make faster, more informed decisions.
Our expertise includes:
📊 Executive Power BI Dashboards
📈 Financial Analytics & Forecasting
⚙️ Business Process Improvement & Automation
📉 KPI & Performance Management
🗄️ SQL Data Warehousing & Data Modeling
📑 Advanced Excel & Google Sheets Solutions
🤖 AI-Powered Business Intelligence
The outcomes extend far beyond better reports.
Organizations gain:
✔ Real-time visibility into business performance
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In today's business environment, competitive advantage is no longer determined by who has the most data.
It belongs to organizations that can transform data into insight—and insight into action.
If your business is still dependent on disconnected spreadsheets, manual reporting, or outdated processes, it may be time to rethink how information flows through your organization.
At Intersect Analytics, we help businesses build intelligent reporting and analytics solutions that improve visibility, strengthen decision-making, and drive sustainable growth.
📩 Let's discuss how we can help transform your reporting and business processes.
📱 WhatsApp: +254 723 632 207
Intersect Analytics
Decisions Backed by Data. Growth Driven by Insight.
Retail Sales Analytics Dashboard (Power BI)
Interactive Power BI dashboard analysing two years of retail sales — revenue, profit, margins, top customers, and monthly trends. Built with data modelling and DAX measures.
Link: github.com/AYOOLUWA1996/retail-sales-analysis
This dashboard takes raw workforce and inventory data and automatically surfaces the metrics that matter no manual pivot tables or chart configuration needed.
What it includes:
KPI summary cards (Total Salary, Average Salary, Record Count) with period-over-period change indicators
A plain-English insights panel that surfaces the standout patterns in the data peak values, leading categories, and distribution shifts instead of leaving the reader to interpret raw numbers
A Reorder Level vs. Stock scatter plot to visualize the relationship between two variables at a glance
Time-series and categorical comparison charts (metric trends over time, comparison by employee ID) that respond to clicks for on-the-fly filtering
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