improvement in demand forecasting accuracy
reduction in excess stock
reduction in planning time (from 2 weeks to 1 week)
A $17 billion global leader in steel manufacturing powers the construction industry with high-quality products. But as its operations expanded across multiple divisions and teams, complexity mounted making it increasingly difficult to unify planning, forecast mid-term demand, and balance inventory. To overcome these challenges, the company turned to TADA’s Demand Navigator, an AI-Enabled Digital Twin application designed to deliver accurate forecasts, streamline collaboration, and optimize inventory across the enterprise.
The company encountered several critical challenges in its demand planning process:
Volatile Demand Patterns:
Forecast accuracy hovered around 84%, making it difficult to align production with demand. Seasonal trends and macroeconomic shifts amplified this volatility.
Lack of Midterm Planning:
The absence of a structured approach to midterm planning hindered the ability to align long-term strategies with short-term operational goals.
Cross-Functional Collaboration and Alignment:
Minimal communication and misalignment among sales, production, and supply chain teams led to conflicting priorities and a lack of consensus on planning.
Long Lead Times:
Extended procurement and production lead times created a mismatch between demand signals and supply availability.
Inventory Inefficiencies:
Both overstocking and understocking resulted in high carrying costs accounting for 25% of inventory value and frequent stockouts, negatively impacting customer satisfaction and operational expenses leading to lost sales worth $59M annually.
To address these challenges, the company implemented TADA’s Demand Navigator, an advanced demand planning application leveraging TADA’s patented AI-Enabled Digital Twin platform to enhance forecasting accuracy. The key features of the solution included:
AI-Driven Demand Forecasting Engine:
Utilizing advanced AI algorithms, the application analyzed historical sales data, market trends, and external factors such as commodity prices, enabling more accurate demand predictions, the solution improved forecast precision from 84% to 96%.
Cross-Functional Collaboration Tools:
The solution facilitated improved collaboration across departments, offering real-time updates and shared insights into market conditions and customer demand.
Inventory Optimization Engine:
The application provided recommendations for optimal inventory levels by considering lead times, safety stock, and reorder points. This approach helped reduce carrying costs while ensuring inventory levels remained aligned with forecasted demand.
Scenario Planning and Sensitivity Analysis:
Features for running various scenarios and conducting sensitivity analyses allowed the company to plan proactively for demand fluctuations.
Integrated End-to-End Planning System:
The demand planning application was fully integrated with other planning tools, ensuring seamless data exchange between demand forecasts, inventory management, and production/raw material planning.
Improved Forecast Accuracy:
The adoption of AI and machine learning led to a 12% improvement in demand forecasting accuracy, enabling more precise planning and reducing both stockouts and excess inventory.
Reduced Inventory Costs:
With more accurate forecasts, the company optimized inventory levels, cut excess stock by 15%, and lowered warehousing and storage expenses.
Faster Planning Cycles:
Monthly planning meetings were shortened from 2 weeks to 4 days, enabling quicker decision-making.
Better Collaboration Across Teams:
Enhanced communication and collaboration among sales, procurement, and production teams resulted in smoother operations and fewer bottlenecks in the supply chain. Forecast debates eliminated by a single version of the truth, resulting in reducing planning friction by 18%.
Increased Customer Satisfaction:
Improved inventory management and faster production lead times enabled the company to fulfill customer demand more reliably, boosting customer satisfaction and retention by more than 15%.
The implementation of TADA’s Demand Navigator Application enabled the steel manufacturer to significantly enhance demand forecasting accuracy, reduce inventory costs, and improve customer experience. The solution fostered better collaboration between teams and streamlined the overall supply chain process. By leveraging AI-Enabled digital twin technology, the company became more agile in responding to volatile market conditions, thereby improving operational efficiency and driving business success.
