by Team Azilen

April 24, 2025

How to Forecast Demand in Supply Chain: Get a Glimpse of Real-Time, Working Solution

A regional sales team missed their forecast. Again. Warehouses ran out of two fast-moving SKUs. The finance team had to adjust numbers mid-quarter. Sound familiar?

Demand forecasting in supply chain has always been tricky. Everyone agrees it’s important. But most teams still rely on spreadsheets, gut feel, and last year’s numbers. That’s where things fall apart.

This blog walks you through how we went from theory to practice. From explaining what demand forecasting really is, to showing how we built a working AI dashboard that tracks it in real-time.

What Is Demand Forecasting in Supply Chain?

Demand forecasting is just a way to predict how much of something your business will sell in the future. You look at past sales, maybe a trend or two, and make a call.

In the supply chain, it’s about more than just sales. You’re planning inventory. You’re lining up vendors. You’re deciding how much to produce, how much to store, and when to move it.

Good forecasts help you avoid running out of stock.

Great forecasts help you avoid overstock too.

And excellent forecasts? They turn the supply chain into a profit center, not a cost center.

Gaps in Traditional Demand Forecasting

Most forecasting setups are stuck in the past.

Teams use Excel files passed around in email threads. They forecast monthly, sometimes quarterly.

In addition, there’s no feedback loop when things change on the ground. And when sales spike or drop unexpectedly, they scramble.

Traditional forecasting doesn’t handle:

● Promotions and seasonality well.

● Regional differences.

● New products with no sales history.

● The reality is that no two SKUs behave the same.

The result? Teams end up reacting. Firefighting. Explaining misses after the fact.

Moving to AI-Driven Supply Chain Demand Forecasting

We decided to flip the approach.

Instead of relying only on what happened last year, we built a system that learns from what’s happening now. That’s where AI came in.

We pulled historical sales data. Cleaned it. Added context like:

● Inventory levels

● Lead times

● Holidays

● Channel-specific trends

Then we used time-series forecasting models. Some SKUs worked better with ARIMA. Others needed machine learning like XGBoost. The goal wasn’t the fanciest model. It was the most accurate one.

We didn’t automate everything from day one. We started small. Tested. Adjusted.

How We Built a Demand Forecasting Dashboard

We were solving a real problem — SKU-level visibility for a retail business spread across multiple cities. They needed to know, at any point in time:

● What demand looked like for the next 4–8 weeks.

● Which SKUs were likely to underperform or overperform.

● Where to act.

We pulled the data into a cloud data warehouse. Cleaned and structured it in a way the models could use. Forecasts ran in Python, and results were pushed to Power BI.

The dashboard became the front-end. No code. Just insight.

How to Forecast Demand in Supply Chain: A Dashboard Walkthrough

Let’s walk through the dashboard of demand forecasting in supply chain.

Step 1: Pick a product, pick a time range

You start at the top. Select the product you want to analyze. Choose the time period. The dashboard adjusts everything else based on that.

Select product and time period for analysis

Step 2: See your KPIs upfront

You get key numbers right away — forecast accuracy, actual sales, demand variance. All tied to your selections.

KPIs to track

Step 3: View the demand trend line

This chart lays it out — past sales, predicted demand, and where you’re likely headed. It updates in real-time as you feed in new data.

Historical and forecasted demand trends

Step 4: Break it down by channel

Want to know if retail is outperforming online? This sales distribution chart shows where demand is coming from. Click on any channel to drill deeper.

Sales distribution across different channels

Step 5: Ask the dashboard anything

There’s a chat widget built into the view. You can type questions like “Why is forecast off for Product X in March?” and get quick insights — powered by AI.

Chatbot

Step 6: Improve the model with a click

Got fresh sales data? Click a button and retrain the forecast model. The system learns, adapts, and updates the next projection automatically.

Train the AI model with new data

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Architecture of the Demand Forecasting System

The demand forecasting system is built with a modular, end-to-end pipeline that enables clean data flow, reliable model training, and intuitive user interaction via an integrated dashboard and chatbot.

Below is a typical architecture of it.

Demand Forecasting System Architecture

1. Simulated Data Generation

We used a Python script to regularly create synthetic time-series data. It mimicked a real manufacturing environment with variables like:

● Units sold

● Inventory levels

● Cost of goods sold (COGS)

● Forecasted demand

This gave us a controlled way to model seasonality, trends, and supply-demand gaps.

2. Data Cleaning

Once generated, we cleaned the data just enough to keep it useful — removing duplicate records and rows that didn’t make sense.

The goal was simple: keep it clean, but not over-engineered.

3. Data Transformation

Next came feature engineering. This step added depth to the raw numbers. We included:

● Lag features (1, 7, 14, 30 days) to spot short- and long-term trends

● Rolling averages and exponential moving averages

● Time-based features like day of week, month, etc.

● Metrics like inventory coverage days and supply-demand ratios

These features helped the model understand patterns beyond just sales volume.

4. Model Training

We tried several forecasting models — ARIMA, Prophet, and Temporal Convolutional Networks (TCNs). After testing, XGBoost gave us the best results.

It handled complex patterns well and could easily use extra variables like pricing or marketing inputs. Accuracy hit around 80% on test data.

5. Forecast Output

The model produced demand forecasts for the next 7, 14, and 30 days. These predictions were saved back into our main database, ready for access.

6. Centralized Storage with PostgreSQL

All the data — raw, engineered, and forecasted — lives in PostgreSQL. This is our single source of truth, and both the model and the dashboard pull from here.

7. Visualization Layer

We connected this data to a custom dashboard built with tools like Power BI and Chart.js.
Users can:

● Track key KPIs

● Drill into SKU-level trends

● Compare forecasts vs actuals

● See confidence intervals in real-time

The dashboard turns raw forecasts into something actionable for business users.

8. Chatbot Assistant (via Langchain)

To make things easier for non-technical users, we added a chatbot interface.
Using Langchain, the bot can answer questions like:

● “What’s the forecast for Product A next week?”

● “Which products are trending above average in Region X?”

It pulls answers directly from the database, making data more accessible to teams without needing to log into a BI tool.

Results and Business Impact of AI-Powered Demand Forecasting in Supply Chain

By implementing AI-driven supply chain demand forecasting, you can expect the following gains:

Operational Benefits

✔️ Forecast accuracy hit ~80%. This helps align production and inventory planning.

✔️ Overstock dropped by 15–20%. Better alignment cuts storage costs and reduces slow-moving items.

✔️ Stockouts fell by 10–15%. Teams can see demand spikes early and adjust fast.

Financial Wins

✔️ Freed-up working capital by cutting inventory buffers.

✔️ More stable COGS through smoother production runs.

✔️ Revenue uplift by reducing missed sales due to stockouts.

Team-Level Impact

✔️ Sales can commit to delivery timelines.

✔️ Supply chain can plan replenishment with fewer surprises.

✔️ Finance and ops can budget and plan capacity with more predictability.

Want Smarter Forecasting Decisions without Manual Effort?

We create AI Agents that do the heavy lifting.

AI Agents Development Services

Key Takeaways from Implementing Supply Chain Demand Forecasting

Not everything worked on day one. Some lessons we learned:

● Data cleanup took longer than expected. Legacy systems had holes.

● One model doesn’t fit all SKUs. We grouped SKUs by behavior and trained separate models.

● Stakeholder buy-in mattered more than model accuracy. If the dashboard wasn’t used, it didn’t matter how smart it was.

We also learned to involve users early. Planners helped shape the visuals. That’s why they adopted it fast.

The Future of Demand Forecasting in Supply Chain

Here’s what’s coming next — and fast:

AI That Explains the “Why,” Not Just the “What”

Forecasts today tell you what will happen. Next-gen AI will tell you why.

Why did SKU-104 suddenly jump 40%? Because regional searches for it went up after a local campaign.

The AI will link it. Show it. Recommend action.

From Reactive to Autonomous

Today, planners react. In the near future, AI agents will act.

They’ll flag issues. Reallocate stock between locations. Nudge suppliers when lead times slip.

Forecasting Gets Context-Aware

Models will start pulling in signals beyond ERP data — weather, event schedules, fuel prices, and market trends.

They won’t forecast in a vacuum anymore. They’ll think like humans, just at scale.

Hyper-Personalization at SKU + Channel Level

Instead of one broad forecast per region or category, we’ll move toward micro-forecasts.

Each SKU. Each channel. Each warehouse. Optimized independently. But still part of the bigger picture.

Close the Forecasting Gap with Azilen

Demand forecasting in supply chain isn’t just a spreadsheet function anymore. It’s how businesses stay lean, agile, and competitive.

If your current setup involves guessing or outdated reports, it’s time to rethink it.

You don’t need to build a huge system to start. Begin with one product line. Use what data you have. Focus on accuracy, not complexity. And bring the team along.

We’ve done it. You can too. And we’re happy to share the playbook!

Azilen’s been doing this with enterprises for over 16 years. We know the mess. We know the pressure. And we know how to make AI useful where it matters.

Let’s connect!

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