A leading global food and beverage company faced significant challenges in accurately predicting demand and optimizing inventory across its diverse product portfolio. As a result, the organization faced higher costs, missed sales, and an inability to adapt quickly to market fluctuations. To address these challenges, we developed an AI-powered demand forecasting and planning solution leveraging hyperparameter tuning at scale and MLOps on Microsoft Azure. This improved forecasting accuracy, reduced costs, and enhanced business outcomes.
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Business challenges
Prior to adopting the AI solution, the company faced several key challenges:
· Inaccurate demand forecasts: Complex data, outdated forecasting methods, and the need for detailed predictions at various levels (location, segment, customer, and package) led to stock shortages and excess inventory. This resulted in lower customer satisfaction and profitability.
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Manual and inefficient processes: Reliance on manual methods for updating and validating forecasts was time-consulting and prone to errors. This slowed decision-making.
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Poor Inventory Alignment: Inventory levels often failed to match consumer demand, leading to high holding costs and lost sales opportunities.
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Lack of Planning Flexibility: Rigid forecasting processes struggled to adapt to a diverse product portfolio and rapidly changing market conditions.
The ask
To solve these challenges, the company needed a solution that could integrate real-time data, use AI for better forecasting, automate processes, and optimize inventory levels. It also had to be scalable for growth and continuously improve through MLOps. To achieve this, they sought an experienced AI and MLOps partner to build a custom solution on a reliable cloud platform.
Solution overview and implementation
We developed a custom AI-powered demand forecasting and planning solution using Microsoft Azure, AI Services, hyperparameter tuning at scale, and MLOps practices. The structured implementation followed a phased approach, ensuring seamless integration, optimization, and deployment.
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Data integration and preprocessing:
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Real-time data collection: Integrated sales, marketing, holiday, and promotion data using Azure Data Factory to enhance forecast accuracy.
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Storage and preprocessing: Stored data in Azure Data Lake Storage (ADLS), handling missing values, outliers, and inconsistencies for a robust data pipeline.
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AI model development and optimization:
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Model Selection: Developed an industry-specific AI model using Random Forest Regressor and XGBoost, trained on historical data.
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Feature engineering: Created key features such as datetime attributes, holiday indicators, promotion effects, and rolling averages to improve predictive accuracy.
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Hyperparameter tuning at scale: Leveraged Pandas UDF and HyperOpt for parallel tuning across customer segments, refining the model for optimal performance.
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Deployment and automation with MLOps:
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Model validation and monitoring: Implemented pre- and post-check frameworks with MLFlow experiments for continuous tracking of model health and accuracy.
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Automated ML pipeline: Automated data preparation, model training, validation, deployment, and monitoring to achieve high MLOps maturity.
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Azure deployment: Deployed models on Azure Machine Learning (AML) for scalability and real-time demand forecasting.
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Custom web application for forecasting:
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User interface: Built an intuitive web application using Azure Web Services for visualizing forecasts and manual adjustments.
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Data management: Integrated with Azure SQL Database for seamless storage and retrieval of forecast results.
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Figure 1: Architecture
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Figure 2: Forecasting flow from data sources to data push
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Solution highlights
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Interactive web application: Provided granular volume and price forecasts, enabling managers to plan strategically and respond proactively to market shifts.
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Forecasting models: Developed over 1 million models to generate highly detailed forecasts across location, segment, customer, and package levels, fine-tuned for optimal performance.
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Automated processes: Automated data validation, model training, hyperparameter tuning, and monitoring, significantly reducing manual effort and enhancing decision-making.
Key results and business outcomes
Our AI-powered demand forecasting solution addressed the client’s challenges to streamline demand forecasting and inventory management. Key outcomes included:
· Improved Forecast Accuracy: The solution achieved over 90% accuracy for key product categories and high forecast accuracy across various segments, with rates above 80% for the top 35 locations. This level of precision in forecasting is crucial for making informed inventory and sales decisions.
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Reduced Manual Effort: By automating the forecasting process, we reduced the turnaround time for updating forecasts from two weeks to just 30 minutes. This significant reduction in manual effort allows their team to focus on strategic initiatives rather than routine tasks.
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Scalability: The solution demonstrated its ability to handle a large volume of data and models, providing scalable demand forecasting and planning capabilities that can grow with their business.
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Model Performance: Through hyperparameter tuning and continuous monitoring, we improved overall error rates and accuracy metrics, ensuring that the forecasting models perform optimally over time.
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Enhanced Planning Capabilities: The solution enabled integrated business planning by offering a unified view of demand and supply and supported dynamic market planning by incorporating real-time data and external events into forecasts.
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Optimized Inventory Management: AI-driven demand predictions led to a 20% reduction in stockouts and more accurate inventory levels, which in turn improved customer satisfaction by ensuring timely product availability.
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Reduced Inventory Holding Costs: More accurate demand predictions resulted in a 15% reduction in inventory holding costs, directly impacting the company’s bottom line.
Conclusion
The AI-powered demand forecasting solution on Microsoft Azure transformed the company’s inventory management, planning, and forecasting capabilities. The solution facilitated faster development and deployment of new models, ensuring that the business can adapt and remain competitive in the food and beverage industry.
To learn how MAQ Software can help optimize your demand forecasting and inventory management, contact us at CustomerSuccess@MAQSoftware.com.
