Data collection and analysis in a manufacturing company: the key to success

Manufacturing companies must respond quickly to changing market conditions. A crucial element for success is the collection and analysis of production data,  which must be available for real-time analysis. 

In this article you’ll learn:  

• why data in manufacturing is essential,  
• how to collect data in a manufacturing company, 
• how to analyze it,
• what tools can help with data collection and analysis. 

The importance of collecting production data  

Manufacturing data provides valuable information on every aspect of production, from raw material consumption to machine performance and order fulfillment rates. Collecting data is crucial for gathering this valuable information. We have written more about the importance of data in the industry in our article on data driven manufacturing.  

Consulting firm Bain has revealed the results of a survey of 270 manufacturing managers from around the world. The survey found that the group implementing a variety of digital technologies to a greater extent achieve their factory goals more easily. This, however, requires effective data analysis.  

A company that analyzes production data has the ability to:  

1. Identify areas for improvement: Accurate data enables efficient identification of problems in the production process and the implementation of corrective actions.  

2. Optimize processes: Managers can make informed decisions about improving production line layouts, introducing new technologies, or training employees.  

3. Improve efficiency: Accurate data helps optimize resource utilization, leading to reduced costs and increased productivity.  
4. Forecast better: Production data allows for more accurate predictions of future trends and needs, helping companies respond to changes more efficiently.  

Understanding how the data were collected and the significance of the data collected are essential for ensuring data integrity and making informed decisions. 

Production data collection methods  

Data collection will always be tailored to the needs of the company and its production processes. In the case of data collection, there are two fundamental approaches to doing it.  

 
5. Automatic data collection  

This involves sensors, barcode scanners, or RFID systems. Automated systems collect data continuously, enabling real-time monitoring of processes. They are accurate and fast, reducing the risk of errors. Real-time monitoring allows for the immediate detection of and response to irregularities. Challenges can include high implementation and maintenance costs and the need for staff training.  

6. Manual data collection  

According to a survey conducted by Plutoshift, 48% of manufacturing companies still enter data manually. Why? The main reasons are: (i) lack of sufficient funds for automation, (ii) it’s easier to manage data manually when they have fewer customers, (iii) lack of awareness that much of the data entry can be automated, or lack of knowledge on how to begin automating data collection.  

The benefit of manual data collection lies in its ease of adaptation to specific needs and the ability to provide detailed descriptions of the data. However, challenges include labor intensity and greater susceptibility to errors. We covered automatic data collection methods in the case study on data scraping.  

The process of collecting production data  

Here it is worth distinguishing a few key stages:  

7. Selecting indicators: Determining key performance indicators, such as OEE (Overall Equipment Effectiveness), throughput, or production costs.  

8. Choosing methods: Selecting data collection methods tailored to the specifics of production and the company.  

9. Systematic collection: Regularly gathering data to monitor progress and respond efficiently to changes.

It is also crucial to experiment with the data to ensure the quality and reliability of the analysis and model results. 

Analysis of collected production data  

Now it’s time to turn raw data into valuable information. This requires the right tools and the ability to interpret results and draw conclusions.  

10. Transforming data into information  

We begin with data verification. Following that, advanced statistical methods and analytical tools should be employed to analyze the data for trends, patterns, and anomalies. This process helps determine how processes and factors impact production efficiency and identify areas for improvement.  

11. Automatic data analysis  

Machine learning algorithms and artificial intelligence enable automated analysis of large volumes of data. Automated data analysis accelerates the decision-making process by reducing the risk of human error. We discussed the capabilities of analyzing large volumes of data in the article on lessons businesses learnt from the war in Ukraine.  

Tools for collecting and analyzing production data  

Various IT tools can be used for data collection and analysis, including properly selected programming environments and database management systems. For example:  

12. ERP systems  

Enterprise Resource Planning (ERP) systems integrate data from different departments of an enterprise to enable comprehensive production management. Regularly updating and analyzing data from ERP systems helps managers make informed decisions.  

13. Data mining

Data mining, or data analytics, allows for the identification of hidden patterns and correlations within large production datasets. Utilizing advanced statistical and machine learning algorithms enables the forecasting of trends and optimization of production processes.  

14.Identification technologies  

Barcodes, RFID, and other identification technologies automate the data collection process, enhancing accuracy and efficiency. Integrating these technologies with ERP systems enables the continuous monitoring and optimization of production processes.  

The collection and analysis of production data form the foundation of modern production management. With reliable data, you can enhance process efficiency and gain a competitive edge in the market. It is crucial to use the right tools for data collection and analysis to ensure accuracy and efficiency. If you’re grappling with the challenge of integrating specialized databases, we’re here to provide expert advice on achieving this efficiently and effectively. We have extensive experience in such projects, which we have detailed in our article on custom database migration.