BI systems in industry: how analytics affects production efficiency

The main articles are:

• Business Intelligence (BI)
• Business Intelligence Definition

Production plants generate huge volumes every day, information from data equipment operation and inventory in warehouses to personnel performance indicators. However, without competent processing and analysis, this information remains simply unrelated in numbers.

BI systems (Business Intelligence) come to the rescue - tools for collecting, processing and visualizing data that help companies make informed decisions based on analytics. These technologies allow you to monitor the state of production processes in real time, predict possible failures and optimize resources.

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Ilya Gorbarov, an expert on the implementation of digital solutions, spoke about how BI systems transform production and what tasks they solve, as well as what are the prospects for their further development, in an article prepared for TAdviser in March 2025.

How BI systems are transforming industry

BI systems optimize industrial processes by automating data collection, processing and analysis. This allows companies to:

• Minimize equipment downtime
• reduce costs;
• Improve speed and accuracy of decision-making
• Optimize resource utilization
• identify inefficient workshops and divisions.

BI systems work according to three basic principles:

• Integration of data from different sources. BI combines information from IoT sensors, MES, ERP and other systems, creating a single analytical environment.
• Detailed analytics and visualization. BI systems analyze data using algorithms, identifying trends and presenting information in a convenient format - dashboards, graphs, reports.
• Forecasting and optimization. BI helps to predict future events - equipment failures or changes in demand, allowing you to minimize risks in advance.

Automate data collection and analysis

One of the main functions of BI systems is automation. Collecting data manually is difficult, long and often leads to errors. BI tools do this work instantly, analyzing the information "here and now."

Examples of automation with BI:

• Monitor equipment health in real time to prevent downtime.
• Automatically generate key figure reports KPI for management.
• Optimize logistics and supply chains by predicting needs.
• Monitor personnel performance and identify bottlenecks.

Increase transparency and control at all stages of production

Without BI systems, managers often face challenges. Managers do not have a complete picture of what is happening in production and because of this they inefficiently allocate resources and make decisions for a long time. If you implement a BI system, it will help you track key metrics in real time, receive notification of failures, and respond quickly to changes. This will make management more transparent and efficient.

BI System and Process Management

Traditional methods of controlling production costs are often ineffective: information about changes is late. BI enables companies to reduce costs and improve productivity through accurate cost analysis and process optimization.

What costs the BI system helps to highlight:

• Power consumption. Data analysis helps detect inefficient energy use and reduce electricity costs.
• Material consumption. BI monitors raw material overspend and proposes adjustments.
• Production shutdowns. Analysis of production data prevents downtime and unplanned outages.
• Optimization of labor costs. BI identifies inefficient processes and distributes labor more efficiently.

Optimization of planning, procurement and logistics

Production plants depend on planning accuracy, because even small failures can lead to delivery delays, equipment downtime and financial losses. BI helps to avoid such problems by predicting demand, automatically analyzing supplies and improving logistics.

How BI improves processes:

• Demand forecasting. Analysis of market trends and historical data helps to plan production for future needs.
• Procurement optimization. BI systems analyze the delivery dates, prices and quality of raw materials, helping to select the best suppliers.
• Supply chain management. BI allows you to track deliveries in real time and warn you of potential delays.
• Logistics analytics. Analyzing supply routes and inventory helps minimize costs and speed up processes.

Monitor production equipment and prevent failures

Any unplanned shutdown of equipment is a loss of profit and a failure of deadlines. BI systems allow real-time monitoring of equipment status, forecasting of breakdowns and prevention of emergency situations.

What BI monitoring gives:

• Monitoring of technical condition. BI analyzes indicators from equipment sensors, detecting signs of wear or breakdown.
• Predictive service. BI algorithms predict when equipment may fail and recommend preventative repairs.
• Reduce downtime. Early detection of problems reduces the number of unplanned stops.
• Taking into account the performance of machines and conveyors. BI analyzes the efficiency of the equipment and helps to optimize the load.

Predictive Analytics: How the BI System Helps Predict Events

In industry, mistakes are costly: unplanned downtime, equipment wear, procurement miscalculations and dramatic demand changes can lead to financial losses. Predictive BI systems help predict possible problems and trends.

The program works with data arrays (Big Data), uses machine learning ( ML) and artificial intelligence ( AI) to analyze information from many sources: IoT sensors, ERP systems, sales reports and even weather forecasts. As a result, companies can make more accurate and timely decisions, reducing risk and improving efficiency.

Let's look at the key aspects of predictive analytics in BI.

Using machine learning

Predictive analytics is built on the basis of processing huge amounts of data coming from various production sites. Algorithmachine learning reveals patterns that a person might not have noticed and helps predict future events.

How BI uses machine learning and Big Data:

• Analysis of equipment performance indicators - prediction of wear and probability of breakdowns.
• Demand Modeling - Forecast production volumes and inventory.
• Detection of anomalies - detection of non-standard situations, for example, deviations in the operation of machines or energy systems.
• Optimization of production processes - reducing scrap and losses by identifying problem areas.

Trend Analysis, Demand Prediction, and Downtime Prevention

BI systems help enterprises analyze trends and predict changes in the market. This is necessary in conditions of fluctuating demand and unstable supplies.

How the BI system predicts demand and prevents downtime: A * analysis of seasonality and market trends. BI takes into account historical data and external factors (economic situation, consumption trends, weather conditions) to accurately predict demand.

• Study of equipment load. The system analyzes production volumes and helps to evenly distribute the load.
• Optimizing supply chains. BI predicts possible delays and recommends alternative solutions.
• Prevent downtime. The system identifies potential equipment malfunctions in advance and offers preventive measures.

Implementation of BI systems: key stages

Poor platform selection, lack of integration with existing systems, and personnel resistance can lead to poor BI solutions. To avoid these problems, enterprises need to understand the key stages of implementation, take into account the difficulties and choose solutions that best fit their business processes.

Let's figure out how to properly implement BI systems in production.

How to choose the right BI solution

The choice of a BI system should be based on the specific needs of the business, not just on its functionality. Here are the key criteria that are important to consider:

• Can the system adapt to business processes and is it possible to scale the service?
• What integrations does the service support?
• How convenient is the interface and do you need additional knowledge?
• Is there access control and data encryption?

Integration with systems

For a BI system to work efficiently, it must be integrated with other business tools:

• ERP. Allows you to synchronize BI with financial, personnel, and purchasing data.
• MES. Allows the system to analyze information from production lines in real time.
• SCADA and IoT systems. BI can collect information from equipment sensors and analyze production metrics.
• and CRM. SCM The BI system helps you analyze orders, forecast demand, and manage. supply chains

Implementation Challenges and Solutions

Lack of understanding of BI goals. Companies often implement BI "for show" without a clear strategy. First, define the key business tasks and select the system based on these goals.

Personnel resistance. Employees are wary of new technology or believe BI will make it harder to operate. Provide training, explain the benefits of BI and encourage workers to adapt quickly to new conditions.

Integration difficulties. BI is difficult to integrate with legacy ERP and MES systems. Use APIs and intermediate solutions to connect to other platforms.

Quality of source data. If the data in ERP and other systems is inaccurate, BI analytics will be incorrect. Implement data cleaning and standardization processes before starting BI. High cost and complexity of implementation. BI requires investment in licenses, servers and employee training. At the start, you can consider cloud BI solutions.

Conclusion

BI systems are already radically changing industry, turning data into a valuable resource for production management. With powerful analytical tools, enterprises can:

• Reduce costs by optimizing processes and reducing downtime.
• Improve production efficiency while minimizing time and resource losses.
• Predict risks and prevent emergencies.
• Optimize supply chains and logistics.
• Automate real-time data analysis.

BI implementation is a strategic solution that requires company executives to clearly understand goals, competently integrate with existing systems, and train staff.