A leading office supplies retailer sought to transform its demand forecasting and inventory management to enhance customer engagement and operational efficiency. Facing inaccurate demand predictions, complex customer behaviors, and a lack of AI-driven insights, the company needed a scalable, data-driven solution. We developed an AI-powered forecasting model using Microsoft Azure to refine demand predictions, optimize inventory, and drive strategic decision-making.

Key challenges

Inventory management in a dynamic retail environment is complex. The company struggled with:

·       Inaccurate demand forecasting: Traditional methods failed to predict spending behavior, leading to overstocking and stockouts.

·    Customer behavior complexity: Diverse purchasing patterns required a more granular understanding of demand drivers.

·    Limited AI utilization: The absence of AI-driven insights hindered the ability to forecast demand accurately across multiple categories.

·    Customer retention: Identifying high-value customers for targeted marketing was a challenge.

·    Holistic data integration: Internal data (historical transactions, website activity) needed to be combined with external market insights (economic trends, industry news) for more accurate forecasting.

·    Holistic data integration: Internal data (historical transactions, website activity) needed to be combined with external market insights (economic trends, industry news) for more accurate forecasting.

The solution

To address these challenges, we developed an AI-powered demand forecasting model on Microsoft Azure. The solution featured:

·       Retail-specific AI foundation model: A machine learning-driven forecasting model, fine-tuned with Azure AI services.

·    Classification and regression models: Predictive analytics to identify high-potential customers and estimate spending trends.

·    Feature engineering: Key customer attributes such as purchase recency, firmographics, and product preferences were incorporated.

·    MLOps for model deployment: Azure MLFlow and Databricks enabled seamless monitoring and optimization.

·    External data integration: Economic indicators, industry news, and seasonal demand factors were incorporated to enhance forecasting accuracy.

Figure 1: Solution architecture

Implementation process

1.       Data preparation and engineering:

·         Aggregated and cleaned historical transaction data, marketing insights, and external sources.

·         Identified key predictive features, filtering out non-relevant customer segments.

2.       Model development and training:

·         Developed and fine-tuned AI models using Azure AI services.

·         Tested multiple algorithms (LightGBM, XGBoost, Huber, KNN) for optimal performance.

3.       Optimization and deployment:

·         Hyperparameter tuning for improved accuracy.

·         Integrated with Azure MLOps for automated tracking and real-time feedback loops.

·         Enabled continuous learning to adapt to market fluctuations.

Business impact 

The AI-powered forecasting solution delivered measurable improvements:

·         Increased forecast accuracy: Achieved a 50% improvement in precision and 69% in recall for demand predictions.

·     Optimized inventory costs: Reduced overstocking and stockouts, leading to a 15% decrease in inventory costs.

·     Higher customer retention and sales: Targeted marketing campaigns resulted in increased revenue and customer engagement.

·     Enhanced operational efficiency: Streamlined inventory management, improving supply chain responsiveness by 20%.

Conclusion

By leveraging AI and Microsoft Azure, we enabled the retailer to revolutionize its demand forecasting and inventory planning. The solution not only optimized inventory but also enhanced customer engagement and sales growth. With integrated AI-driven insights, the company can now navigate market fluctuations with confidence, ensuring a competitive edge in the retail industry.

To learn how MAQ Software can help optimize your demand forecasting and inventory management, contact our team at CustomerSuccess@MAQSoftware.com.

Organizations today face a rapidly changing business landscape, where data is the key to staying competitive. As the demand for scalable, cost-effective solutions grows, many companies are making the shift from Tableau to Power BI. With AI-powered capabilities embedded in the Power BI workload within Microsoft Fabric, businesses can harness automation, self-service analytics, and advanced decision-making tools at a lower total cost of ownership.

The migration process from Tableau to Power BI can be overwhelming due to the complexities of planning, minimizing business disruptions, rebuilding reports and dashboards, adapting to a new interface, and managing costs—all while maintaining operational efficiency. MigrateFAST simplifies data and report migration through automation, reducing time-to-market and enabling a smooth transition with minimal disruptions.

6-step migration process with MigrateFAST

Transitioning from Tableau to Power BI can be smooth and efficient with the right approach. MigrateFAST’s highly automated process enables a seamless migration across six key stages, reducing costs and time-to-market.

1. Inventory Analysis: Laying the Foundation

Before the migration can take place, a complete inventory of Tableau workbooks, extracts, data models, and reports is essential to evaluate what needs to be migrated, what can be retired, and what may require re-engineering. Accurate and thorough inventory analysis ensures that you know exactly what you’re working with before diving into the migration process.

This step alone can take weeks without automation. MigrateFAST automatically analyzes Tableau data models and connections and accelerates the creation of metadata documents with a few clicks.

2. Estimation & Planning: Clarity from Day One

Successful migrations require a clear estimate of costs, risks, and unknowns. A well-laid-out plan can help prevent unexpected issues during migration and ensure that the project remains within scope and budget.

MigrateFAST automates the estimation of licensing costs, infrastructure requirements, and the creation of a detailed migration timeline. Using historical data and pre-built migration pathways, MigrateFAST reduces uncertainty, providing a clear and actionable plan for each phase of the migration.

3. Semantic Model & Report Creation: Delivering Consistent Insights

Recreating semantic models and reports is the most visible part of the process for end users. It is essential that reports in Power BI look and behave the same way as in Tableau, if not better. Reports should be optimized for Power BI’s interface and features. Moreover, identifying and removing redundant reports helps improve the efficiency of the migration and performance of Power BI reports after deployment.

MigrateFAST reduces manual efforts by:

·       Migrating LOD expressions to DAX measures

·       Automatically creating Power BI semantic models

·       Recreating Tableau visualizations in Power BI

4. Review: Ensuring Data Accuracy and Integrity

Migrating large amounts of data and reports from one platform to another can lead to performance issues, incorrect data mappings, or errors in calculations. Without thorough review, there is a risk that your reports could deliver incorrect or suboptimal results.

MigrateFAST automates the validation of the migrated reports by running performance tests, optimizing queries, and ensuring data accuracy through validation and certification.

5. Governance: Maintaining Control and Integrity

Data governance is essential in migration to support data validation and certification processes while ensuring dataset integrity. A well-defined governance framework provides proactive alerts and real-time monitoring of capacity, usage, report availability, and platform issues.

Our approach automates performance optimization to ensure adherence to best practices and pre-defined SLAs.

6. Center of Excellence (CoE): Supporting Long-Term Success

Migration doesn’t end once the reports are deployed. Post-migration support is essential for monitoring system performance, optimizing costs, and ensuring that the organization can effectively adopt and use the new platform.

We provide ongoing support to optimize costs, monitor system performance, and improve change management. This approach drives higher adoption and long-term success—helping your organization move from reactive fixes to proactive innovation.

Customer success story

A leading retail client with 25,000+ users needed a scalable, secure, and resilient centralized data platform. Their existing infrastructure included 300+ workbooks, 350+ extracts, and a 200 TB Teradata database, supporting 200+ concurrent users. After migrating from Tableau to Power BI, the client achieved:

      •  60% reduction in overall maintenance and new development effort 

      •  35% cost savings on platform and maintenance 

      •  NSAT score improvement from 2 to 3.5 

      •  300% increase in adoption within a year through CoE setup 

With MigrateFAST, the client’s migration effort was automated by approximately 60%, dramatically reducing manual intervention and shortening the migration timeline.

Migration Steps	Automated using toolkit	Efforts without MigrateFAST (in hrs.)	Efforts with MigrateFAST (in hrs.)
Analyze Tableau data model and connections	✓	10	0
Create metadata document	✓	5	0
Create Power BI Semantic model	✓	16	0
Understand calculations and recreate them in DAX	✓	24	0
Validate and fix calculated columns and measure definitions	✗	16	20
Understand and create field parameters	✓	8	4
Create Power BI reports with base measures	✓	16	4
Implement field parameters in the report	✓	4	2
Identify alternate visuals for gaps	✗	4	4
Optimize performance (CertyFAST)	✓	8	4
Data validation	✗	8	8
Total		119 (~25 days)	46 (~6 days)

Figure 1: Migration effort with MigrateFAST per workbook

MigrateFAST ensures a seamless transition to Power BI, preserving Tableau’s look and feel while enhancing usability with AI-powered capabilities.

Figure 2: User interface of Tableau vs. Power BI

 

Expanding MigrateFAST Capabilities

Beyond Tableau, MigrateFAST streamlines migration from multiple platforms, including MicroStrategy, Cognos, SAP BOBJ, and more. Our customized approach aligns with your data architecture requirements, ensuring a seamless transition to help you harness Power BI’s full capabilities.

·             Tableau to Power BI Migration: 2-Hour Briefing

·       Tableau to Power BI Migration: 3-Week Implementation

·       Tableau to Power BI Migration: 6-Week Implementation

Accelerate your migration journey with our highly automated 6-step process. Contact CustomerSuccess@MAQSoftware.com to get started today.

 

Revolutionizing demand forecasting and planning with AI foundation model on Microsoft Azure

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.

Many technologies enable advanced supply chain analytics by offering unique capabilities in data integration, processing, and real-time insights. Among these, Snowflake stands out due to its powerful data-sharing capabilities, high-performance computing, and seamless integration with BI tools—making it valuable for optimizing and scaling supply chain operations.

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.

·    Manual and inefficient processes: Reliance on manual methods for updating and validating forecasts was time-consulting and prone to errors. This slowed decision-making.

·    Poor Inventory Alignment: Inventory levels often failed to match consumer demand, leading to high holding costs and lost sales opportunities.

·    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.

1.       Data integration and preprocessing:

·         Real-time data collection: Integrated sales, marketing, holiday, and promotion data using Azure Data Factory to enhance forecast accuracy.

·         Storage and preprocessing: Stored data in Azure Data Lake Storage (ADLS), handling missing values, outliers, and inconsistencies for a robust data pipeline.

2.       AI model development and optimization:

·         Model Selection: Developed an industry-specific AI model using Random Forest Regressor and XGBoost, trained on historical data.

·         Feature engineering: Created key features such as datetime attributes, holiday indicators, promotion effects, and rolling averages to improve predictive accuracy.

·         Hyperparameter tuning at scale: Leveraged Pandas UDF and HyperOpt for parallel tuning across customer segments, refining the model for optimal performance.

3.       Deployment and automation with MLOps:

·         Model validation and monitoring: Implemented pre- and post-check frameworks with MLFlow experiments for continuous tracking of model health and accuracy.

·         Automated ML pipeline: Automated data preparation, model training, validation, deployment, and monitoring to achieve high MLOps maturity.

·         Azure deployment: Deployed models on Azure Machine Learning (AML) for scalability and real-time demand forecasting.

4.       Custom web application for forecasting:

·         User interface: Built an intuitive web application using Azure Web Services for visualizing forecasts and manual adjustments.

·         Data management: Integrated with Azure SQL Database for seamless storage and retrieval of forecast results.

Figure 1: Architecture

Figure 2: Forecasting flow from data sources to data push

Solution highlights 

·         Interactive web application: Provided granular volume and price forecasts, enabling managers to plan strategically and respond proactively to market shifts.

·     Forecasting models: Developed over 1 million models to generate highly detailed forecasts across location, segment, customer, and package levels, fine-tuned for optimal performance.

·     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.

·     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.

·     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.

·     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.

·     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.

·     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.

·     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.

Enabling self-service for supply chain analytics with Microsoft Fabric and Direct Lake

  

Effective supply chain analytics is critical for success. Especially for retail companies, an efficient supply chain helps meet customer demand, optimize inventory, and ensure timely product delivery. It directly impacts cost management, operational efficiency, and customer satisfaction too. A well-managed supply chain also enables faster response to market trends, helping companies stay competitive and adaptable in a dynamic retail landscape. 

Many technologies enable advanced supply chain analytics offering unique capabilities in data integration, processing, and real-time insights. Among these, Snowflake is one of the key platforms. This is due to its powerful data-sharing capabilities, high-performance computing, and seamless integration with BI tools—making it valuable for optimizing and scaling supply chain operations.

 

About the scenario

A grocery retailer undergoing digital transformation aimed to empower its supply chain team with data-driven decision-making. The company selected Microsoft Fabric, a robust data platform designed to deliver efficient self-service analytics. Using Microsoft Fabric’s capabilities and AI (artificial intelligence) tools like Microsoft Copilot, the retailer gained faster data access and additional scalability. This approach fosters operational efficiency, providing a low-maintenance solution that delivers reliable insights and supports seamless data processing.

Key business requirements

The migration process included the following aspects:

·              Self-service analytics: Empowers non-technical users to generate insights.

·        Comprehensive data coverage: Includes data from the last three years at highly granular levels (~5 TB).

·        User support: Accommodates 250+ users with tailored data access and permissions for different roles.

·        Generative AI integration (Copilot): Simplifies insight discovery with tools like Microsoft Copilot.

·        Scalability and performance: Scales to meet increasing demands without compromising performance.

·        Security and data access control: Enforces role-based access to protect sensitive data.

·        Integration with multiple data sources: Unifies data from over 20 sources for comprehensive analysis.

Unfolding the solution

Choosing Direct Lake

Selecting the right storage mode was essential for self-service analytics. Direct Lake was selected as it provided high-speed, real-time data access from Delta tables in Microsoft OneLake. This choice eliminated data imports and frequent refreshes, allowing analysts instant access to large datasets. Direct Lake’s unrestricted dataset size in Microsoft Fabric SKUs (stock keeping unit) supported anticipated data growth. Integration with Power BI, Excel, and other tools was another benefit, offering immediate insights.

Conducting proof of concepts (POCs)

A proof of concept (POC) evaluated the feasibility of migrating the client to Microsoft Fabric’s Direct Lake model. Essential elements like calculated columns, static tables, row-level security (RLS), and time intelligence were replicated. This phase involved mapping approximately 180 relationships and defining around 400 measures. Azure Databricks (ADB) notebooks handled calculated tables and columns, while Azure Data Lake Storage (ADLS) shortcuts created a Lakehouse. Direct Lake semantic models supported Power BI reports. As part of this implementation, we also evaluated aspects relating to security, time intelligence, and refresh latency. A 50+ concurrent user load test was also conducted.

The POC successfully met all requirements and passed all tests.

Figure 1: Architecture

Solution design: A lake-centric approach

Following the POC’s success, a solution was built using a lake-centric architecture, centered on Microsoft Fabric and Direct Lake. Key components included:

1.     Capacity planning and SKU selection: The F256 SKU was chosen to support three years of data while meeting performance needs. Direct Lake’s lack of dataset restrictions made it more cost-effective than import mode, which would require a higher SKU.

2.     Improved data access with self-service and AI integration: The solution enabled users to access insights and information faster with Smart Narratives and Copilot chat. Users are now eager to start using AI Skills.

3.     Automated operations and cost efficiency: Azure DevOps CI/CD ensured reliable deployments, while Microsoft Fabric notebooks automated dataset caching. Scheduling optimized Microsoft Fabric capacity, scaling to F256 during business hours and down to F64 during off-peak times.

4.     Data storage: Azure Data Lake Storage Gen 2 serves as the primary data storage. ADLS shortcuts were chosen due to their high performance and fast processing times.

5.     Semantic model mode: Direct Lake mode was selected for the semantic model. This is because Direct Lake mode supports scalability without dataset restrictions, accommodating future data growth needs.

6.     Security and access control: Using 'workspace identity' for ADLS shortcuts eliminated key management, while RLS provided fine-grained control over data access.

7.     Performance optimization: Pre-warming techniques reduced first-load times. Testing demonstrated improved performance with ADLS shortcuts over managed tables. 

Solution impact 

This solution enables both technical and non-technical users to make data-driven decisions with ease. Users access operational data through Excel, Power BI, and Copilot’s chat-based interface, building models for demand forecasting, inventory optimization, and delivery route planning. Users can work with granular data efficiently, experiencing reduced refresh latency and faster load times. The solution eliminated manual tasks, enabling seamless work with live data. All in all, the solution boosts supply chain responsiveness and agility.

Interested in learning more?

As a Microsoft Fabric Featured Partner, MAQ Software brings deep expertise in helping organizations unlock the full potential of Microsoft Fabric. Whether you're looking for guidance on implementing data solutions or optimizing your existing platform, we’re here to support you every step of the way. Contact us at CustomerSuccess@MAQSoftware.com or explore our apps or consulting services on Microsoft Azure Marketplace:

·             Microsoft Fabric: 2-Hour Briefing – Microsoft Azure Marketplace

·       Microsoft Fabric: 4-Week Assessment – Microsoft Azure Marketplace

·       Microsoft Fabric: Accelerated 8-Week Pilot Implementation – Microsoft Azure Marketplace

·       Real-time Intelligence using Microsoft Fabric: 1-Day Workshop – Microsoft Azure Marketplace