business intelligence

The role of business intelligence in the manufacturing industry

The relentless inflow of data from various manufacturing sources, coupled with the pressure to derive meaningful insights in real-time, can feel overwhelming. How can you possibly keep up with the demands of analyzing vast datasets while ensuring your insights translate into tangible improvements in production processes and outcomes? The truth is, you're not alone in this struggle. According to the McKinsey Global Institute, manufacturing is the most data-prolific industry there is, generating 1.9 petabytes of data every year. Amidst this data surge, there lies immense opportunity waiting to be unlocked through the power of business intelligence (BI.)

With advanced analytics tools and intuitive dashboards, BI empowers you to make sense of the chaos, providing clarity within the complexity of your operations. Whether you're monitoring production metrics, optimizing supply chain logistics, or forecasting demand, BI equips you with the tools you need to succeed in today's fast-paced manufacturing landscape.

What is manufacturing business intelligence?

Manufacturing business intelligence involves gathering, analyzing, and interpreting manufacturing operations data to gain insights and inform decisions. This includes data from production machines, supply chain systems, quality control processes, and more. Manufacturing BI enables companies to optimize operations, improve product quality, streamline processes, reduce waste, and enhance competitiveness. It plays a crucial role in achieving operational excellence and driving continuous improvement in the manufacturing industry.

Key components of manufacturing business intelligence include:

  • Data collection: Gathering data from sources such as sensors, machinery, enterprise resource planning (ERP) systems, customer feedback, etc.

  • Data integration: Consolidating data from different sources into a unified database or data warehouse for analysis.

  • Data analysis: Applying various types of analytical techniques, such as descriptive, diagnostic, predictive, and prescriptive analytics to extract insights from the data.

  • Reporting and visualization: Presenting the analyzed data in a meaningful way through reports, dashboards, and visualizations to facilitate decision-making.

  • Taking action on insights: Identifying areas for improvement based on data insights and implementing strategies to enhance efficiency, reduce costs, and increase profitability.

Business intelligence for manufacturing

The role of business intelligence in manufacturing is multifaceted and pivotal to operational success. Here's how BI impacts various stages of manufacturing:

  • Strategic planning: In the strategic planning phase, manufacturing companies leverage business intelligence to make informed decisions. Insights derived from market trends, consumer preferences, and competitive landscapes help identify growth opportunities and assess risks, guiding the formulation of long-term strategies.

  • Product development: Throughout product development, data-driven insights obtained from BI processes aid in gathering feedback from customers, suppliers, and internal stakeholders. This feedback is instrumental in enhancing product designs, features, and quality to meet market demands effectively.

  • Production optimization: Efforts to optimize manufacturing processes rely heavily on BI analytics. By monitoring key performance indicators (KPIs) like equipment downtime, cycle times, and defect rates, manufacturers identify bottlenecks and streamline operations for improved efficiency and productivity.

  • Supply chain management: By analyzing data from suppliers, distributors, and logistics partners, BI allows for real-time monitoring of inventory levels, lead times, and transportation costs so manufacturers can mitigate risks and enhance overall supply chain efficiency.

 

  • Demand forecasting: Accurate demand forecasting is facilitated by BI tools that analyze historical sales data, market trends, and customer behavior. By predicting future demand patterns, manufacturers optimize production schedules and inventory management to minimize stockout costs and excess inventory.

See how Canadian Tire was able to grow sales by 20% during the pandemic by forecasting demand with ThoughtSpot.

  • Performance monitoring: Performance monitoring across the production process is enabled by BI analytics. Manufacturers track metrics such as overall equipment effectiveness (OEE), throughput, and yield to identify performance issues and drive continuous improvement initiatives.

Benefits of business intelligence for manufacturing

Inventory optimization

Harnessing the power of BI, you can leverage insights derived from vast amounts of data to inform decision-making processes at all organizational levels. This enhances strategic planning and resource allocation. For example, you might use BI to analyze production data and determine optimal production schedules based on market demand and resource availability.

Increased operational efficiency

Through the implementation of BI tools, you can pinpoint inefficiencies within your operations, such as production bottlenecks or inventory discrepancies, enabling you to streamline workflows, minimize downtime, and optimize costs effectively. For instance, you might use BI to identify equipment that frequently breaks down and proactively schedule maintenance to prevent unplanned downtime. Additionally, you might track and analyze expenditures across various operational segments to identify cost-saving opportunities, and optimize procurement processes, further enhancing operational efficiency and maximizing profitability.

 

Increased quality control and defect reduction

Another benefit of BI in manufacturing is tracking product quality through sensor data. This helps you catch defects early, cut down on rework, reduce scrap, and maintain high-quality standards. BI helps you analyze production line data to identify trends or patterns indicating potential quality issues and take corrective action before defects occur.

Enhanced customer satisfaction

Analyzing customer data and feedback is one of the best ways to gain valuable insights into preferences, buying behavior, and satisfaction levels. By leveraging these insights, you can tailor products and services to meet customer needs more effectively, ultimately enhancing overall satisfaction and loyalty. Additionally, BI can help in predicting and reducing customer churn. For instance, you might use BI to pinpoint which product features are most valued by customers and prioritize product development efforts accordingly.

More efficient regulatory compliance management

By leveraging BI tools to track and manage adherence to industry regulations and standards, you can optimize compliance with various regulatory measures including environmental, health, safety (EHS) initiatives, product compliance standards, and more. An example would be using BI to track and report on emissions levels to ensure compliance with environmental regulations.

Real-life examples of BI in manufacturing

1. Fabuwood cabinetry

Fabuwood, a manufacturer of high-quality kitchen cabinets, utilizes ThoughtSpot's business intelligence platform to enhance decision-making and operational efficiency. Before ThoughtSpot, the company faced significant challenges with its Azure SQL database and legacy BI tool, Power BI. The growing demand for business insights, particularly regarding sales data, posed a considerable obstacle for the data team. Executives didn’t have instant access to sales data to make decisions about product line profitability, supply chain logistics, and manufacturing workflows. Recognizing the need to scale and modernize their BI capabilities, the data team sought a suitable self-service analytics solution.

In their quest, Fabuwood discovered ThoughtSpot and the many benefits of adopting a modern data stack. With ThoughtSpot, the team has streamlined operations by replacing hundreds of manually created reports with automated workflow analytics for the entire manufacturing floor. 

Fabuwood testimonial

2. General Electric (GE)

General Electric (GE) is another prominent example of a manufacturing company that utilizes business intelligence to enhance operational efficiency and drive growth. Through advanced analytics and machine learning algorithms, GE analyzes data streams to identify patterns, predict equipment failures, and optimize energy usage across its manufacturing facilities. This proactive approach to maintenance has enabled GE to minimize downtime, reduce maintenance costs, and enhance overall productivity.

It’s not enough to connect machines. You have to make your machines smarter. You need to figure out the best ways for embedding intelligence into machines and devices. Then you need to develop the best techniques for collecting the data generated by those machines and devices, analyzing that data and generating usable insights that will enable you to run your equipment more efficiently and optimize your operations and supply chains.

Bill Ruh, Former CEO of GE Digital and Chief Digital Officer

3. Toyota

Toyota, renowned for its Toyota Production System (TPS) also leverages business intelligence to streamline operations and improve quality. Toyota employs BI tools to collect and analyze data from various sources, including production lines, supply chain networks, and customer feedback channels. By monitoring KPIs such as cycle times, inventory levels, and defect rates, Toyota identifies areas for improvement and implements continuous process enhancements. Additionally, Toyota utilizes BI for demand forecasting to optimize production schedules and inventory management, ensuring timely delivery of high-quality vehicles to customers while minimizing waste and costs.

4. Boeing

Boeing, a leading aerospace manufacturer, relies on manufacturing business intelligence to optimize its complex manufacturing operations and supply chain networks. Boeing utilizes BI tools to gather and analyze data from aircraft production lines, supplier networks, and maintenance operations, enabling real-time monitoring of production status and quality metrics. By leveraging BI analytics, Boeing identifies opportunities for process optimization, supply chain efficiency improvements, and cost reductions. Additionally, Boeing employs predictive analytics models to forecast demand for aircraft components and spare parts, optimizing inventory levels and ensuring timely delivery to customers worldwide.

Analytics isn’t a magic idea that will deliver results sometime in the future. It’s real and it’s working, here and now. Boeing has been using analytics for years to improve our own operations.

David Longridge, Former Vice President of Sales and Marketing, Boeing Global Services

Driving manufacturing innovation through data

As manufacturers move forward into an increasingly digital and data-driven era, BI will continue to play a pivotal role in shaping the future of the space. By embracing BI solutions and harnessing the power of data, you can unlock new levels of innovation, efficiency, and competitiveness in the dynamic global marketplace. 

ThoughtSpot stands at the forefront of BI, offering essential tools for success in the digital age and beyond. Trusted by leading companies like Siemens, Caterpillar, Daimler, and many more, ThoughtSpot continues to drive excellence in business intelligence.

Request a free demo with ThoughtSpot today to take your manufacturing operations to the next level.