CASE STUDY

Demand Forecasting & Inventory Optimization for Specialty Chemicals

Industry

Chemical Manufacturing

Work Done

AI Consulting Services,

A global specialty-chemicals manufacturer faced several inventory issues, leading to losses and disrupted operations.

Some products accumulated in warehouses, while others ran out precisely when production needed them. Demand from export customers was highly unpredictable. Supplier lead times were long and inconsistent. The planning team had no reliable way to figure out what to order or when.

To solve these problems, they hired an AI consulting team to develop a forecasting and inventory-optimizing system.

The Challenge

The manufacturer handled a wide range of specialty chemicals, including performance chemicals, intermediates, textile chemicals, home-care formulations, and more. However, they struggled with:

Unpredictable demand patterns arising from both seasonality and exports abroad, which made planning very challenging
Data was fragmented across sales, procurement, and production systems, with numbers that often conflicted or failed to reconcile.
Long supplier lead times extend from weeks to months and cause production disruptions
Frequent stockouts that resulted in emergency purchases at inflated prices and the consequent loss of loyal customers
Excess inventory tied up working capital and warehouse capacity, while critical materials were unavailable during production runs.

They required a predictive model that would combine all planning inputs and provide accurate demand forecasts along with the optimal inventory levels for each product line.

The Solution

The AI team divided the work into four parts.

Unified Data Warehousing for Planning

First, the AI team brought all the historical data into one place. It consisted of historical sales records, dispatch logs, production schedules, procurement cycles, and supplier lead times consolidated into a structured data warehouse.

The silos were removed. Now, all people have complete access to the information they need.

Machine-Learning Demand Forecasting Models

Next, the team built forecasting models using machine learning. The models analyzed seasonality patterns, export trends, lead-time differences, and how customers behaved over time.

This forecasting layer used advanced capabilities from Inventory Forecasting Solution.

The accuracy of predictions improved as additional information entered the system.

Inventory Optimization Engine

The third piece was an optimization engine. It calculated when to reorder materials, how much safety stock to maintain, what buffer stock made sense, and what procurement teams should prioritize.

The objective was simple: aligning inventory with market demand. Stop guessing and start planning based on what the data is showing.

Planning & BI Dashboards

Finally, they set up dashboards so teams can access insights in real time. Procurement, sales, and production all got their own views.

The dashboards were powered by Conversational Business Intelligence.

As a direct result, a “single source of truth” emerged. Everyone worked with the same numbers. Decisions were faster because people no longer had to wait for reports or dig through spreadsheets.

Why This Approach Worked for Specialty Chemicals?

Specialty chemicals are not commodity goods. Demand does not trend in a straight fashion. Some products sell steadily. Others spike during certain months or when export orders come through. A few barely move at all.

This variability makes planning difficult. Traditional methods rely on averages or simple projections. Those methods fall apart when demand behaves unpredictably.

Machine learning handles this better. It can detect patterns humans might miss and adjusts predictions as new data comes in. It further accounts for multiple variables at once.

The inventory engine adds another layer. Forecasting tells you what demand will look like. The engine tells you how to respond, how much to order, when to order it, and how much cushion to keep in case something went wrong.

Taken together, these tools offered a level of planning that better reflected how this business operated.

Business Impact

The results showed up across multiple areas.

Excess inventory declined by 18 to 25 percent. Warehouses were no longer packed with products that were not selling. Capital that was tied up in stock got freed up for other uses.
Accuracy is increased by a further 30 to 40 percent. Predictions started matching reality. Teams could trust the numbers they were seeing.
Stockouts became less frequent. Emergency purchases dropped. Production lines ran without interruptions because materials were available when needed.
Procurement cycles moved 15 to 20 percent faster. Buyers knew what to order and when suppliers received clearer instructions. The whole process became more efficient.
Working capital improved. Money was not sitting idle in inventory. It could be deployed where the business needed it most.
On-time deliveries got better, especially for export customers. When inventory planning works, everything downstream benefits. Orders shipped on schedule.

Conclusion

The manufacturer had been experiencing the same problems year after year. There were warehouses full of slow-moving stock while production was waiting for materials that had not been ordered for weeks.

The forecasting system provided visibility that was previously unavailable. Machine learning identified patterns in their demand data that traditional methods overlooked. The optimization engine translated those insights into actionable steps.

Consequently, procurement is not catching up with crises but planning ahead now. Production gets what it needs without fuss. And their cash is no longer stuck in warehouses.

Rather than incremental fixes, the company achieved results through a focused, system-level transformation.

Demand Forecasting & Inventory Optimization for Specialty Chemicals

The Challenge

The Solution

Unified Data Warehousing for Planning

Machine-Learning Demand Forecasting Models

Inventory Optimization Engine

Planning & BI Dashboards

Why This Approach Worked for Specialty Chemicals?

Business Impact

Conclusion

Technologies used

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