What You Need to Know About Setting Up Manufacturing Business Analytics

In the manufacturing industry, data analytics has become more than just a byproduct of operations—it’s a powerful resource for driving efficiency, innovation, and growth. Business analytics helps manufacturers make sense of this data, turning it into insights that guide smarter decisions across production, supply chains, and business operations. 

That said, building an effective data analytics system isn’t always straightforward. From connecting data across different systems to ensuring your setup can scale securely, there are plenty of challenges along the way.  

This article breaks down the key elements of successful business analytics for the manufacturing industry, including infrastructure, data integration, data analytics, AI, and practical tips to get the most from your data. 

Whether you want to improve operations, enhance forecasting, or prepare your analytics for the future, this guide will help you take the next steps with confidence.

Benefits and KPIs of Manufacturing Business Analytics

As part of the business intelligence stack, business analytics relies on various solutions to help analysts create detailed reports. These reports form the foundation for company management to make decisions about production-related processes, such as research and development, new product launches, waste management, and more.  

Additionally, they enable tracking key business metrics (KPIs), which can guide decisions about restructuring departments, scaling operations up or down, or assessing the productivity of sales and marketing initiatives.

Business analytics solutions are often cloud-based platforms like Power BI, designed to process vast amounts of data and deliver clear visualizations and data analytics of the different branches of an enterprise. However, even if you see the value in adopting such a solution, your leadership might have questions—these tools often represent a significant investment for the company. Typically, technology investments are evaluated from three key perspectives:

1. Does it increase revenue?
2. Does it help save money?
3. Does it improve overall efficiency across departments?

Below are the key benefits of business intelligence solutions to help you make your case to management or the board:

• Making Smarter Decisions - Data analytics gives you clear, real-time insights into things like sales, inventory, and costs. This means you can make decisions based on facts, not guesses, which helps you work more efficiently.
• Improving How We Work - By looking at production data, you can figure out what’s slowing you down and make processes smoother. It’s about finding small tweaks that can snowball to make a big difference in the long term.
• Saving Money - Data analytics helps you spot where you might be overspending—whether it’s on extra materials, machine downtime, or delays in the supply chain. Fixing these means you can save money without cutting corners.
• Preventing Problems Before They Happen - With the right data, you can predict issues like equipment failures before they cause major disruptions. That keeps production moving and avoids unexpected headaches.
• Focusing on What Customers Actually Want - By analyzing trends, you can see what’s really in demand and adjust the production to match. This helps you avoid overproducing things people don’t need—or running out of what they do.
• Seeing the Big Picture - Data analytics gives everyone a clear view of how the business is doing, from the factory floor to the balance sheet. This makes it easier for teams to work together and plan ahead.

For a deeper understanding of the benefits, we’ve written an article that explores business intelligence from both a business and innovation perspective. We encourage you to give it a read if you’d like to dive further into the topic.

General KPIs to Track in Manufacturing 

In a manufacturing company, keeping track of the right Key Performance Indicators (KPIs) is key to working more efficiently, cutting costs, and making smarter decisions.  

The business intelligence team helps keep an eye on these numbers to make sure they support the company’s overall goals. Here are some of the most important KPIs to focus on:

• Production Efficiency
  • Overall Equipment Effectiveness (OEE): Analyze data for equipment usage, factoring in availability, performance, and quality. Very helpful when custom building a manufacturing operations solution to reduce downtimes with predictive maintenance.
  • Cycle Time: Measures the time to complete a production cycle, highlighting delays or inefficiencies.
• Operational Costs
  • Cost Per Unit: Tracks the cost of producing a single unit for better cost control.
  • Material Utilization: Monitors how effectively raw materials are used versus wasted.
• Quality Metrics
  • Defect Rate: Percentage of defective products versus total output.
  • First Pass Yield (FPY): Measures products made correctly without requiring rework.
• Supply Chain Efficiency
  • On-Time Delivery Rate: Tracks how often orders are delivered as scheduled.
  • Inventory Turnover: Measures how frequently inventory is sold and replenished.
• Workforce Productivity
  • Labor Utilization Rate: Evaluates how effectively labor is used in production.
  • Downtime per Employee: Tracks non-productive time to boost efficiency.
• Financial Performance
  • Gross Margin: Monitors profitability by comparing production costs to revenue.
  • Return on Assets (ROA): Measures how effectively assets generate profits.
• Environmental Impact
  • Energy Consumption: Tracks energy usage to identify savings opportunities.
  • Waste Reduction: Measures progress in reducing waste and improving sustainability.

Tracking these KPIs is just the first step—how you present and monitor them is equally important for turning insights into action. 

Creating real-time dashboards is a game-changer for keeping track of these metrics and making timely decisions.  

These platforms connect easily to data sources and provide dynamic visuals for things like sales trends, profitability, machinery breaking through predictive maintenance and operational efficiency, helping everyone stay on top of what’s important.

Technical Challenges When Implementing Business Analytics Solutions

Bringing business intelligence into the manufacturing industry comes with its fair share of technical challenges. These often revolve around integrating data from multiple sources, managing complex workflows, and making sure the system can scale and perform well across all operations.

Tackling these issues means building a solid technical foundation that can handle the complexities of production processes while keeping data accurate and systems running smoothly. 

A viable strategy for addressing challenges in building or implementing business analytics solutions in your manufacturing company is to partner with an experienced custom manufacturing software development company. Such a partner can guide you through every step of the development process, from proof-of-concept to the final iteration.  

Their expertise can save you valuable time and effort, potentially avoiding months of trial and error in solving the issues you’re facing.

Data Integration Across Multiple Systems

Integrating data from various systems is one of the toughest technical challenges when implementing business intelligence in manufacturing. Manufacturing environments rely on a mix of tools—ERP platforms, IoT sensors, supply chain systems, and CRM software. If these systems don’t work together smoothly, valuable insights stay siloed, and their full potential is lost.

Common Challenges

• Different Data Formats: Each system generates data in its own way. For example, ERP systems produce structured tables, while IoT devices might generate raw, unstructured logs. Combining these formats into something usable can be tricky.
• Outdated Systems: Many manufacturers still use legacy systems that weren’t designed to connect with modern business intelligence tools, making integration more complicated.
• Real-Time Needs: Business analytics often require real-time data—like production line performance or inventory updates—which can be hard to collect and process quickly.
• Data Quality and Governance Issues: Data from different source systems often varies in accuracy and consistency. For example:
  • The same customer or product might be named differently across systems ("Widget A" vs. "Product 123").
  • Fields like addresses or IDs might be missing, outdated, or formatted inconsistently.
  • Without a common key or standard identifiers, additional effort is required to clean, map, and prepare the data before it can be used effectively. Data governance frameworks are often needed to establish consistency across systems.
• Growing Data Volumes: As the business grows, so does the amount of data. Keeping integrations running smoothly as volumes increase can be a challenge.

How to Overcome These Challenges

• APIs and Middleware: These act as bridges, connecting older systems with newer tools to allow data to flow smoothly.
• Centralized Data Platforms: Using a data warehouse or lakehouse (e.g., Microsoft Fabric, Snowflake, or Databricks) can bring all your data into one place, making it easier to analyze and extract insights.   
• IoT and Edge Computing: Tools like Azure IoT Hub can handle data directly from factory sensors and send it to your analytics systems in real time.
• ETL Pipelines: Automated processes to Extract, Transform, and Load (ETL) data help clean and organize information so it’s ready for analysis.

Why It Matters

Integrating data gives you a complete view of your operations, from production and inventory to customer demand. It also reduces the manual work of piecing together reports and makes it easier to act on insights quickly. With better data integration, you can improve efficiency, save costs, and make smarter decisions.

Handling Complex Data Workflows

Managing data workflows in manufacturing can quickly become a daunting task. With data coming from so many sources—like machines on the factory floor, supply chain systems, and sales platforms—it’s easy for workflows to get tangled. Without a well-organized process, teams might struggle to get the right data at the right time, leading to delays and inefficiencies.

Common Challenges

• Fragmented Data Sources: Manufacturing involves multiple systems, each generating its own data. Coordinating these sources into a unified workflow is challenging. 
• High Data Volume and Velocity: Production processes often generate massive amounts of data in real time, making it hard to process everything quickly and accurately. 
• Data Transformation: Raw data often needs to be cleaned, formatted, and enriched before it’s useful, which adds complexity to the workflow. 
• Workflow Scalability: As manufacturing processes grow, the workflows need to scale to handle more data and more processes without bottlenecks.

Solutions for Managing Complex Workflows

• Workflow Automation Tools: Platforms like Apache Airflow or Azure Data Factory can automate repetitive tasks like data extraction, transformation, and loading. 

• Data Orchestration: Orchestrating workflows ensures that data moves through the system in the right order, from collection to visualization, without delays or errors. 

• Streamlined Pipelines: Using tools to create efficient data pipelines reduces lag and ensures data is processed as soon as it’s available. 

• Modular Architecture: Designing workflows in smaller, independent modules makes them easier to manage and scale as the system grows.

Why It Matters

Efficient data workflows are the backbone of any business analytics system. When workflows are well-structured, teams can quickly access accurate data for decision-making. This is especially important in the manufacturing industry, where delays or errors can impact production schedules, inventory levels, and customer satisfaction. 

Complex workflows don’t have to mean complex headaches. With the right tools and strategies, you can create a system that’s efficient and scalable.

Ensuring Scalability and Performance

As manufacturing processes grow, so does the volume and complexity of the data generated. Ensuring that business analytics systems can handle this growth without slowing down or losing operational efficiency is a critical challenge. Scalability and performance are not just about keeping systems running—they’re about ensuring that data continues to deliver actionable insights as demands increase.

Common Challenges

• Growing Data Volumes: Manufacturing generates a huge amount of data from IoT sensors, production lines, and supply chains. Systems need to handle this increase without performance issues. 
• Real-Time Processing: It's important to analyze manufacturing operations data in real-time, but high-speed processing can strain resources as data scales. 
• Infrastructure Limitations: On-premises systems often struggle to scale as quickly as cloud-based solutions, leading to bottlenecks in data processing and storage. 
• System Downtime: As operations grow, performance issues can lead to system slowdowns or outages, disrupting analytics and decision-making.

Solutions to Address Scalability and Performance

• Cloud-Based Platforms: Solutions like Microsoft Azure or AWS provide flexible and scalable infrastructure to handle growing data volumes and processing needs. 

• Distributed Computing: Using tools like Apache Spark or Hadoop distributes workloads across multiple nodes, ensuring faster data processing and better performance. 

• Optimized Data Storage: Data partitioning and indexing in warehouses and lakehouses improve retrieval times and ensure systems remain responsive even as data grows. 

• Performance Monitoring: Continuously monitoring system performance helps identify bottlenecks early, allowing for proactive optimization. 

• Load Testing: Regularly testing how systems handle increased workloads ensures they’re prepared for future growth.

Why It Matters

Scalability and performance are about preparing your analytics systems for the future. As operations expand, having systems that can grow with you ensures that data remains a reliable resource for decision-making.

Data Architecture and Technology Stack for Business Analytics

The structure of your data systems and the tools you use determine how effectively you can analyze and act on manufacturing data. A well-planned architecture enables smooth integration, efficient processing, and scalable advanced analytics tailored to manufacturing needs. 

When choosing a technology stack, it’s important to consider your current cloud integration and the ecosystem you already have in place.  

Different challenges require different tools, and while the tools listed here are widely recognized as industry standards, your specific needs may vary.  

To identify the right tools for your projects, it’s often helpful to consult with a manufacturing technology consulting partner who can align your business analytics solution with your operational goals.

Data Architecture and Cloud Services  

A strong data infrastructure is the backbone of manufacturing analytics, enabling seamless data collection, processing, and analysis. Here are the key aspects to consider:

• Scalability and Integration: Your infrastructure must scale with growing data volumes from systems like IoT devices, ERP, and MES while ensuring seamless integration. Tools like Azure Data Factory or middleware platforms streamline data flow across diverse sources. 
• Storage and Real-Time Processing: Use data lakes (e.g., Azure Data Lake) for raw data and warehouses (e.g., Snowflake) for structured queries. For real-time analytics, solutions like Apache Kafka or Azure Stream Analytics ensure low-latency data processing. 
• Data Visualization and Reporting: Choose tools like Power BI, Tableau, or Looker to create dashboards that transform complex data into actionable insights. These tools enable teams to monitor KPIs, identify trends, and support real-time decision-making. 
• Governance, Security, and Reliability: Implement strong data governance policies, encryption, and access controls to secure sensitive manufacturing data. High availability designs with redundant systems ensure consistent access to analytics. 
• Cost Optimization: Leverage cloud solutions for scalable, pay-as-you-go models while optimizing storage tiers and using auto-scaling to control costs.

Data Integration and Management

In manufacturing, data is often spread across multiple systems and devices—ERP platforms, MES tools, IoT sensors, and supply chain software.  

For the best performance, these disparate sources need to be integrated into a unified system. Business intelligence tools must support integration through pre-built connectors, open APIs, or SDKs to establish smooth data exchange and synchronization across platforms. 

A key component of this process is ETL (Extract, Transform, Load). ETL pipelines are responsible for preparing data for analysis by cleansing, standardizing, and structuring it into a usable format. 

This step removes inconsistencies, aligns data from different sources, and ensures that analytics outputs are both accurate and reliable. Advanced ETL tools like Azure Data Factory or Talend can automate these tasks, handling high volumes of data efficiently. 

For technical teams, ensuring proper data integration and management goes beyond simply connecting systems—it involves creating a scalable and secure framework for ongoing data processing.

Data Storage

Efficient data storage is vital for manufacturing analytics because it keeps both real-time and historical data accessible and usable. A data warehouse is ideal for storing structured, aggregated historical data for detailed analysis and reporting.  

For real-time needs, an operational data store can provide quick access to current information, while data marts can serve specific teams. 

Key Considerations for Data Storage

• Data Lakes for Raw and Semi-Structured Data
  • Data lakes, such as Azure Data Lake or Amazon S3, are ideal for storing large volumes of raw, semi-structured, or unstructured data.
  • They enable flexible storage for IoT logs, machine sensor data, and other non-relational datasets, preparing them for later processing and analysis.
• Data Warehouses for Structured Data
  • Data warehouses like Snowflake, Azure Synapse or Microsoft Fabric Data Warehouse are designed for structured data and complex queries.
  • They provide the performance needed for tasks such as KPI reporting, trend analysis, and financial forecasting.
• Hybrid Storage Strategies
  • A hybrid approach combines data lakes for raw storage with data warehouses for processed and structured data, ensuring both flexibility and speed.
  • Technologies like Delta Lake enrich Parquet-based data lakes by adding features like ACID transactions, data versioning, and schema enforcement. These capabilities make it easier to handle structured and semi-structured data with the reliability of traditional data warehouses.
• Performance and Cost Optimization
  • Implement storage tiering to separate frequently accessed (hot) data from rarely used (cold) data. This reduces costs while maintaining performance where it’s needed.
  • Use auto-scaling features in cloud platforms to adapt to fluctuating data loads without overcommitting resources.
• Backup and Redundancy
  • Data reliability is critical in manufacturing, where insights drive operational decisions.
  • Ensure backup and replication strategies are in place to protect against data loss and minimize downtime.

A well-designed storage system ensures that manufacturing data—whether it’s real-time production metrics or historical performance records—is always available and usable.

Data Analysis

Data analysis lies at the heart of business analytics in manufacturing, transforming raw data into meaningful insights that guide decisions across the organization. 

With the right tools and techniques, manufacturers can identify patterns, improve processes, and anticipate future needs, enabling smarter and more proactive decision-making.

Key Aspects of Data Analysis

• Descriptive Analysis
  • Summarizes historical data to understand past performance. 
  • Used for tracking KPIs like production efficiency, defect rates, and delivery times. 

• Diagnostic Analysis
  • Identifies root causes of issues by analyzing anomalies and trends. 
  • Helps answer questions like why a machine experienced downtime or why delivery delays occurred. 

• Prescriptive Analysis
  • Recommends actions based on insights, including predictive data. 
  • Supports decisions like adjusting production schedules or optimizing inventory levels. 

• Predictive Analysis
  • Uses machine learning and statistical models to forecast future events. 
  • Common applications include predicting equipment failures or demand fluctuations. 

• Cognitive Analysis
  • Leverages AI and machine learning to mimic human reasoning and derive insights from unstructured data, like maintenance logs or customer feedback. 
  • Useful for applications such as anomaly detection in complex systems and interpreting textual or image-based data.

Tools and Techniques

• Business Intelligence Platforms: Tools like Power BI, Tableau, or Looker provide visualization and dashboarding to make insights accessible.
• Advanced Analytics Tools: Platforms like Databricks, Azure Machine Learning, or Python-based libraries enable deeper analysis with machine learning and AI. These tools support large-scale data processing, advanced modeling, and collaborative workflows for data science and analytics teams.
• Querying Tools: SQL and data warehouse tools like Snowflake, Azure Synapse or Microsoft Fabric Data Warehouse allow efficient exploration of structured data.

Effective data analysis empowers manufacturers to improve operational efficiency, reduce costs, and enhance decision-making at all levels.

Data Governance and Security

In a sector where sensitive information like production details, intellectual property, and supply chain data is handled, robust governance and security measures are non-negotiable.

Key Aspects of Data Governance

• Data Quality Management
  • Establish standards to ensure data is clean, accurate, and complete.
  • Use automated tools to detect and correct errors or inconsistencies.
• Access Control
  • Implement role-based access controls (RBAC) to restrict data access based on job functions.
  • Protect sensitive information by limiting access to authorized personnel only.
• Data Lineage and Auditability
  • Track the origin, movement, and transformation of data throughout its lifecycle.
  • Maintain audit trails for transparency and compliance with regulations.
• Compliance with Regulations
  • Compliance with manufacturing regulations requires adhering to standards like ISO 9001 for quality management, ISO/IEC 27001 for securing information assets, and GDPR for protecting personal data where applicable. 
  • Manufacturers in defense or export-sensitive sectors must also comply with ITAR and EAR by securing systems against unauthorized access to controlled data. 
  • Collaborating with compliance experts ensures that analytics practices align with these regulations, supported by proper reporting and governance frameworks.

Key Aspects of Data Security

• Encryption and Secure Transmission
  • Use encryption protocols to protect data at rest and in transit.
  • Implement secure APIs for data exchange between systems.
• Threat Detection and Response
  • Employ monitoring tools to detect potential breaches or unusual activity.
  • Develop incident response plans to quickly mitigate security threats.
• Backup and Recovery
  • Create regular backups to safeguard against data loss.
  • Use redundancy strategies to minimize downtime in case of failures.
• Cloud Security
  • Leverage built-in security features of cloud platforms like Azure or AWS, including identity management and advanced threat protection.
  • Regularly audit cloud configurations to ensure adherence to best practices.

Effective data governance ensures data integrity and consistency, laying the groundwork for reliable analytics. At the same time, strong security measures protect sensitive manufacturing information from breaches or misuse. Together, governance and security build trust in the business intelligence system, enabling manufacturers to confidently use data to drive decisions while staying compliant with industry regulations.

Analytics and Visualization Layer

The final layer is where data is visualized and analyzed to extract insights.  

Real-Time Dashboards: For business leaders to make quick, informed decisions, real-time dashboards provide critical visibility. Data pipelines that support real-time analytics can deliver KPIs related to sales performance, supply chain efficiency, and financial metrics. 

Self-Service Analytics: In many organizations, enabling non-technical users to access and analyze data independently is crucial. Self-service tools allow users to explore data, create reports, and drill into insights without relying on data teams. 

In summary, a solid architecture for manufacturing business intelligence should bring together data lakes, data warehouses, reliable ingestion pipelines, and advanced modeling techniques. This combination allows businesses to handle both real-time and batch data processing, providing actionable insights that drive improvements across operations.

Best Practices for Building a Robust Data Architecture

For business intelligence in manufacturing, to deliver value, the data architecture must be designed with both scalability and flexibility in mind. Here are some best practices to consider when building or refining your architecture: 

Focus on Data Quality and Governance: Good data is the foundation of accurate business intelligence. Set up processes to clean, validate, and improve data before it’s used in analytics. Put clear policies in place to define who owns the data, who can access it, and how it meets compliance requirements. According to Gartner, 60% of companies don’t measure the annual financial cost of poor-quality data. This means data quality can directly impact ROI, yet most companies remain unaware of its effects.

Design for Scalability: Use a flexible design that can grow as your data needs expand. A modular setup lets you scale storage, processing, or integration individually without affecting the whole system. 

Organize Data by Use Case: Adopt a Medallion Architecture to structure your data into distinct layers: 

• Bronze Layer for raw data, preserving unprocessed information from all sources. 

• Silver Layer for processed data, cleaned and enriched for operational use. 

• Gold Layer for analytical data, aggregated and optimized for reporting and machine learning. 

This layered approach makes processing easier, supports diverse use cases, and ensures raw data is always available for future analysis. 

Support Both Real-Time and Batch Processing: Your system should handle both real-time data (for immediate metrics like production monitoring) and batch data (for deeper analysis of historical trends). Each method serves different but equally important purposes. 

Make Data Easy to Access: Build integration pipelines to connect data from various systems and automate updates. This ensures all your data sources stay current and ready for analysis when you need them.

Leveraging Machine Learning and AI for Operational Analytics

Implementing machine learning (ML) and artificial intelligence (AI) allows companies to go beyond simply understanding what happened in the past (descriptive analytics). With these technologies, they can predict what might happen next (predictive analytics) and determine the best course of action to take (prescriptive analytics).  

Let’s explore how you can use these technologies to improve your business analytics for your manufacturing company.

Predictive Analytics and Forecasting

Machine learning plays a key role in business intelligence by helping predict future outcomes like sales trends, demand changes, and profitability. With predictive models, manufacturers can plan for demand more accurately, manage inventory more efficiently, and align their resources to better match market needs.

• Sales Forecasting and Demand Prediction: Predictive analytics in sales forecasting uses historical sales data to forecast future sales trends, enabling manufacturers to plan production and manage inventory efficiently. Similarly, accurate demand forecasting models predict market demand, helping businesses avoid stockouts or excess inventory, thus improving profitability. Machine learning algorithms like ARIMA (AutoRegressive Integrated Moving Average), LSTM (Long Short-Term Memory), and Gradient Boosting are often applied to time series data to enhance accuracy. 

• ARIMA: A statistical method commonly used for short- to medium-term demand forecasting. It’s especially useful when there is strong seasonality or trends within the dataset. 

• LSTM: A type of recurrent neural network (RNN) well-suited for capturing long-term dependencies in time-series data, making it highly effective for complex demand patterns in manufacturing. 

• Gradient Boosting: This machine learning technique excels at handling structured data and is particularly effective for profitability analysis by analyzing product-level sales and cost structures. 

For example, manufacturers can apply these models to predict quarterly sales volumes based on historical data, adjusting production schedules accordingly to match market demand. 

• Profitability Analysis: Machine learning also aids in profitability analysis by identifying key factors that influence profit margins, such as cost-to-serve or product-level profitability. Models trained on sales, procurement, and operational data can highlight which products, regions, or customer segments offer the highest profitability, allowing manufacturers to prioritize high-margin products and optimize their operations for better financial outcomes.

Automating Business Insights with AI

AI-driven analytics offers the ability to automate repetitive and labor-intensive tasks, such as reporting, anomaly detection, and even decision-making. This reduces the manual effort required for data analysis, allowing business leaders to focus on strategic tasks rather than operational bottlenecks. 

• Automating Reporting: AI-driven analytics platforms like Microsoft Fabric, Databricks, Azure Synapse Analytics, Azure Machine Learning, TensorFlow, and DataRobot enable the automation of routine reports by generating insights from real-time data. For instance, instead of manually compiling weekly sales reports, AI can be trained to automatically pull data from relevant sources, analyze data, and generate reports with actionable insights. This level of automation accelerates the decision-making process, ensuring that data is up-to-date and insights are timely. 

• Anomaly Detection: AI models can automatically identify outliers or anomalies in data that could indicate potential issues, such as supply chain disruptions, machine malfunctions, or unexpected sales drops. Using machine learning algorithms, these models can continuously monitor data streams and trigger alerts when something deviates from the norm. Azure Machine Learning and TensorFlow provide tools to build these AI models, which can be integrated with existing dashboards or automated alert systems. 

• AI-Driven Decision-Making: Beyond reporting, AI can also assist in decision-making by providing recommendations based on data patterns. For example, if an AI model detects that a particular product is underperforming based on real-time sales and profitability data, it could suggest changes to pricing, promotions, or resource allocation to address the issue. This shifts operational analytics from being purely reactive to becoming a proactive tool that supports better business outcomes.

Tools and Platforms for Machine Learning and AI in Manufacturing

To implement machine learning and AI in operational analytics, organizations need robust platforms capable of handling large-scale data processing, model training, and deployment. Here are some leading tools: 

• Azure Machine Learning: A cloud-based platform for building, deploying, and managing machine learning models. It integrates well with existing Microsoft products like Power BI, making it a strong choice for manufacturers already on the Microsoft ecosystem. 

• TensorFlow: An open-source machine learning framework developed by Google. TensorFlow is highly flexible and supports a wide range of AI applications, from deep learning to time-series forecasting. It is widely used for building complex neural networks like LSTM models for demand forecasting. 

• DataRobot: A platform designed to automate the process of building and deploying machine learning models. DataRobot simplifies model building for non-data scientists by automating many aspects of the machine learning pipeline, from data preparation to model selection and tuning. This allows businesses to integrate AI-driven insights into their decision-making processes quickly.

AI and ML can transform your business intelligence efforts when used effectively. But implementing these advanced technologies isn’t without its challenges.  

You’ll need to make sure your infrastructure is ready—do you have your data properly organized? Are you using cloud solutions? Is your data protected with the right security measures?  

Other obstacles may come up along the way, which is why it’s a good idea to work with your manufacturing technology partner to ensure your business analytics are set up for success with AI.

Conclusion

The benefits of strong business intelligence for your manufacturing company are undeniable. Data is essential for management to make informed decisions about the company’s direction—whether it’s related to production or business operations. From growing or reducing headcount to maintaining or replacing machinery, these decisions become clearer with the use of proper cloud-native business intelligence solutions. 

In addition to these solutions, AI and machine learning can enhance the performance of your business analytics, allowing your data processing to scale without the need to increase the number of BI employees. 

It’s important to note that the technical recommendations in this article are general guidelines and represent widely accepted best practices for manufacturing industry solutions. However, your business may face unique challenges or require tailored software integrations to meet specific needs. 

To further explore manufacturing data analytics implementation, consider partnering with an experienced custom manufacturing software development company to support your initiatives. You can also book a call with our team of experts to discuss how to transform your business analytics ideas into a comprehensive solution tailored to the manufacturing industry.