Executive Summary
Volatile market demand, rising commodity prices, fluctuating construction activity, and the pressure to maintain on-time deliveries have significantly increased operational complexity for large cement manufacturers and and the broader building materials industry. Traditional planning systems—ERP, APS, spreadsheets, and manual forecasting—are no longer sufficient to deliver the agility required by today’s industrial leaders.
This case study demonstrates how a leading global cement manufacturer used ThroughPut’s AI-powered supply chain decision intelligence platform to:
- Improve product mix profitability
- Increase on-time delivery performance
- Optimize capacity planning accuracy
- Reduce working capital requirements by $1.5M+
- Increase gross margins by $875,000
- Reduce inventory by $450,000
This transformation was achieved without adding new infrastructure, equipment, or labor—solely by unlocking the value hidden in the client’s existing enterprise data.
This blog outlines the full journey, solution, and measurable impact using a BOFU-focused, ROI-driven lens tailored for VP/Director/Head-level leaders in Supply, Sourcing, Operations, Maintenance, and Production.
Client Background
The client is a global construction material manufacturer with several decades of experience producing high-quality cement, aggregates, and complementary products used in major infrastructure and commercial construction projects.
Their product portfolio includes:
- Cement
- Sand
- Gravel
- Multiple aggregate granulometries
- Specialized blends for concrete and structural applications
Despite strong demand across regions, the client struggled with inconsistent on-time deliveries, fluctuating product mix profitability, and inefficient utilization of production capacity—all amplified by post-pandemic volatility.
Industry Context: Cement Manufacturers Face Rising Complexity
The global cement and construction materials industry is undergoing a structural shift driven by:
Post-COVID Volatility
Demand swings have become sharper and more unpredictable due to:
- Infrastructure stimulus spending
- Sudden slowdowns in commercial construction
- Labor shortages
- Disrupted raw material availability
Cost Pressure & Commodities Volatility
Fuel, aggregates, and raw material costs fluctuate rapidly, impacting margins.
Capacity Imbalances
Plants face alternating periods of under-utilization and overload, creating bottlenecks.
Growing Customer Expectations
Large infrastructure and construction firms expect:
- On-time delivery every time
- Stable product availability
- Price transparency
- Rapid service-level adjustments
The Need for Sustainability & Efficiency
Optimizing product mix, reducing waste, and improving throughput directly impact sustainability metrics.
Amid these pressures, cement manufacturers seek AI-driven supply chain decision intelligence that transforms raw operational data into better product mix decisions, capacity alignment, and actionable insights.
Business Challenges
The client’s leadership team—specifically the VP of Supply, Director of Operations, Head of Sourcing, and Regional Production Managers—faced four critical operational challenges:
Poor On-Time Delivery (OTD) Performance
Unpredictable demand patterns, sudden changes in construction project timelines, and limited early visibility made it challenging to maintain consistent OTD levels.
This resulted in:
- Lost orders
- Customer dissatisfaction
- Higher logistics costs
- Last-minute schedule changes
- Increased operational stress
Limited Visibility into Demand Sensing & Capacity Alignment
Manual forecasting and spreadsheet-heavy processes failed to provide real-time clarity on:
- What products specific customer segments needed
- When demand spikes or dips were likely to occur
- How to align production capacity accordingly
This often led to:
- Overproduction of low-margin SKUs
- Underproduction of high-demand variants
- Mismatches between plant capacity and market needs
Suboptimal Product & Customer Mix Decisions
The client lacked data-driven visibility into:
- Which SKUs generated the highest margins
- Which customer segments were profitable
- Which regions consistently underperformed
- Which products caused capacity overloads
As a result, the product mix did not align with:
- True demand
- Seasonality
- Capacity constraints
- Margin maximization opportunities
Pricing & Delivery Scheduling Not Connected to Real-Time Data
Pricing decisions and delivery scheduling were:
- Reactive
- Manual
- Based on limited historical data
This created difficulties in managing:
- Cost-to-serve
- Delivery feasibility
- Route efficiency
- Customer prioritization
Jobs to Be Done (JTBD) — What the Client Needed
For VP/Director/Head of Operations, the core jobs-to-be-done were:
Improve OTD while reducing delays and downtime
They needed AI that identifies bottlenecks before they happen.
Sense demand accurately and early
So production, sourcing, and logistics could be aligned.
Optimize product mix for profitability
Based on margins, demand behavior, seasonality, and capacity.
Free working capital without risking stockouts
Inventory needed to be optimized at SKU- and location-level.
Streamline capacity planning
So each plant’s resources match real market needs.
Improve pricing and scheduling decisions
Using real-time customer behavior data—not just spreadsheets.
Why Traditional Systems Failed
ERP + APS alone could not solve these issues because:
- They look backward, not forward
- They are not built for SKU-level margin optimization
- They lack real-time simulation capability
- They cannot integrate external signals like weather or market trends
- They do not reveal root causes of delays or performance drops
AI-powered supply chain decision intelligence was required.
ThroughPut’s AI-Powered Supply Chain Solution
ThroughPut deployed its AI-driven supply chain intelligence platform, which analyzes existing enterprise data—no new infrastructure required.
The deployment enabled:
AI-Based On-Time Delivery Improvement
The system identified scheduling inefficiencies, bottlenecks, and high-risk delivery routes.
AI automatically:
- Flagged potential delys
- Optimized dispatch timing
- Recommended production sequence changes
- Reduced unplanned downtime
Result: Consistent improvement in OTD across customer segments
Demand Sensing for Accurate Capacity Utilization
AI analyzed:
- Sales data
- Seasonal patterns
- Weather flows
- Customer ordering behavior
- Commodity price movements
This enabled:
- SKU-level demand projection
- Location-level capacity alignment
- Leaner production cycles
Result: Increased throughput and reduced operational waste.
Product & Customer Mix Optimization
Using AI algorithms, the platform identified:
- High-margin vs low-margin SKUs
- Profitability by customer type
- Regional performance outliers
- Product lines causing bottlenecks
The system automatically recommended:
- Which products to prioritize
- Which segments to serve more aggressively
- Which SKUs could be reduced or discontinued
Result: Improved gross margins by $875,000.
Intelligent Capacity Planning
ThroughPut AI created digital twins of production and distribution flows.
This enabled the client to:
- Simulate capacity scenarios
- Predict demand surges
- Align upstream material requirements
- Reduce overproduction
- Eliminate underutilization
Result: Major gains in resource utilization and cost savings.
Pricing & Delivery Scheduling Optimization
AI simulated:
- Customer behavior
- Delivery windows
- Seasonal order patterns
- Cost-to-serve variability
The platform recommended pricing adjustments and scheduling changes that helped maximize:
- Revenue
- Margin
- Delivery feasibility
- Resource allocation efficiency
Data-Driven Insights Unlocked
ThroughPut AI uncovered critical insights that were not visible through traditional systems:
- 22% of SKUs were consuming 41% of capacity but generating only 8% margin
- A specific customer segment accounted for 30% of delays
- Seasonal rainfall patterns influenced demand for certain aggregate sizes
- Plant-level capacity utilization fluctuated 18–27% month-to-month
- A few key SKUs were responsible for inventory overload
- A hidden bottleneck in dispatch sequencing was costing $350,000 annually
These insights revealed actionable opportunities for improvement.
Transformation Before vs After AI Deployment
| Category | Before AI | After AI |
| OTD | Inconsistent, reactive | Predictable, optimized |
| Capacity Planning | Manual, error-prone | Data-driven, automated |
| Product Mix | Margin blind | Margin-optimized |
| Working Capital | High inventory costs | $450K reduction |
| Throughput | Capacity mismatches | Balanced flows |
| Profit Margins | Volatile | $875K increase |
| Decision-Making | Delayed | Real-time |
The Impact
The client achieved:
$1.5M+ Annual Working Capital & Operational Savings
- $875,000 in gross margin improvement
- $450,000 inventory cost reduction
- Significant throughput improvement
- Supply chain waste elimination
Stronger Data-Driven Decision-Making
Leaders gained:
- Predictive visibility
- Daily AI recommendations
- Rapid scenario simulation
- Clear prioritization logic
Improved Takt Time and Delivery Reliability
- Better resource utilization
- Higher customer satisfaction
- Improved competitive positioning
What This Means for Cement, Aggregates & Construction Materials Manufacturers
This case study proves that AI-driven supply chain decision intelligence is no longer optional.
- Manufacturers who adopt AI now gain:
- Margin protection during commodity volatility
- Demand clarity during uncertainty
- Faster OTD improvements
- Reduced inventory risk
- Visibility into profitable customer segments
- More resilient capacity planning
This is especially critical for leaders managing complex product portfolios and geographically distributed customer bases.
FAQ
Question: How does AI help cement manufacturers improve OTD performance?
Answer: AI detects bottlenecks early, optimizes scheduling, and aligns production with real-time demand signals—reducing downtime and delivery delays.
Question: How does AI improve product mix profitability?
Answer: It analyzes SKU profitability, demand trends, seasonality, and capacity constraints to recommend the most profitable product mix.
Question: Can AI reduce working capital for cement companies?
Answer: Yes. AI optimizes inventory levels and identifies slow-moving SKUs, reducing unnecessary working capital tied in stock.
Question: What data is needed to deploy AI-driven supply chain optimization?
Answer: Existing ERP, sales, production, logistics, and weather or market data are enough. No new infrastructure is required.
Question: How quickly can cement companies see results?
Answer: Most companies begin seeing measurable improvements in 6–12 weeks.
Question: How does AI help with capacity planning?
Answer: It forecasts demand and automatically matches it with plant capacity, providing optimization recommendations.
Question: Can AI improve customer service levels for construction clients?
Answer: Yes. AI improves delivery reliability, ensures correct product availability, and aligns production schedules with customer needs.
Question: Does AI work with fluctuating or seasonal demand?
Answer: AI models incorporate seasonality, weather patterns, and market behavior to predict demand with high accuracy.
Question: Is AI deployment risky for cement manufacturers?
Answer: No. ThroughPut uses a super-connector model, sitting on top of your existing data architecture without disrupting operations.
