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Manufacturers have been able to reduce waste in production processes and improve product yield and quality over the last 20 years by implementing Six Sigma and Lean approaches. However, the volatile nature of today's manufacturing segment, particularly in chemicals, pharmaceuticals, and mining, has necessitated a more granular approach to identifying and correcting flaws in the process.

One such solution is big data in manufacturing. And there are signs to back it up, such as the fact that the global big data in manufacturing domain is expected to reach $9.11 billion by 2026. The technology's use case has played a significant role in shaping this.

 

What exactly is big data?

The technology is defined as high velocity, high volume, and high variety data sets that aid in information processing, enhancing insights, assisting with decision making, and automating processes.

Another definition of big data is a technology that consists of a diverse and complex set of data that is gathered through multiple resources and requires an advanced processing approach such as cloud computing or machine learning to provide key business insights.

 

The technology is primarily composed of three key components –

Variety – Businesses have access to a wide range of data, which can be classified as unstructured, semi-structured, or structured data.

Velocity –  is the rate at which data is transmitted. Typically, data is stored in memory, but businesses also employ real-time processing mechanisms.

Volume – The technology processes a large amount of data, which includes structured, semi-structured, and unstructured data.

Now that we've looked at what big data is, let's take a look at how the manufacturing industry generates data.

 

Where does big data analytics for manufacturing come from?

In addition to general sources of data generation such as loyalty programmes, online marketing analysis, and social media monitoring, the industry collects data using a variety of software.

CRP, MES, and CMMS software sets, among others, are integrated with machines to generate big data in the manufacturing space.

The data sets generated by these software and machines can then be used to form patterns, identify problem areas, and develop data-backed solutions.

To generate this massive amount of data, the industry now requires a strong set of user-friendly technology stack. Syoft employs some of the best industrial data analysis tools available:

 

How does big data analytics for manufacturing come about?

 

What role does big data analysis play in manufacturing?

The advantages of big data in manufacturing range from several preventive measures to assisting with predictive decisions. Let us investigate the various methods that highlight the significance of data analytics in the manufacturing industry.

1. Increased competitive advantage

The manufacturing industry has historically been at the epicentre of technological advancements Whether it's mobile connectivity, industrial IoT, or next-generation hardware, the data generated by all of these different mediums contributes to increased competitiveness. Data provides greater insights into market trends, a better understanding of customer needs, and forecasts for future trends. In short, it provides everything that gives manufacturing firms a significant competitive advantage.

 

2. Reduced downtime

Hardware downtime can be a significant productivity hazard in the manufacturing domain. It not only wastes employees' time, but also necessitates extensive maintenance and troubleshooting. The industry's solution to the problem is to use industrial data analysis to perform preventive and predictive maintenance on their hardware. It aids manufacturers in maintaining to keep track of hardware quality by analysing efficiency and working on a daily basis

 

3. Improved Customer Experience

Manufacturing firms are now utilising advanced sensors to provide big-data powered alerts to field technicians regarding maintenance requirements, RFID tags to monitor the condition of units, and data-driven reports that offer accurate suggestions for improving customer services.

 

4. Management of the supply chain

Big data analytics in manufacturing enables manufacturers to track the location of their products. This ability to track down the location of a product using technologies such as radio frequency transmission devices and barcode scanners eliminates the problem of products becoming lost or difficult to trace. For customers, this means that businesses are capable of providing them with a more realistic delivery timeline

 

5. Production administration

Identifying market needs and the volume of goods required is one of the key productivity indicators of a manufacturing company.

When big data in manufacturing did not exist, businesses relied on human estimates, which resulted in goods being produced in excess or shortage. Big data assists businesses in gaining important predictive insights that help them make better decisions.

 

6. Quick response to market demand fluctuations

Real-time manufacturing analytics, specifically in the CRM system, can assist manufacturing companies in forecasting the future in real-time. CRM data analysis can reveal differences in order and consumption.

Patterns that can be used to drive production adjustments Furthermore, big data-driven intelligence gathered from the CRM can aid in knowing what customers are asking for and then preparing production in a cycle in such a way that the time to respond is minimised.

 

7. Accelerating the assembly

Businesses can now segment their production and identify the units that are manufactured faster thanks to big data analytics in manufacturing. This informs manufacturing companies about where they should concentrate their efforts in order to maximise output. It would also assist them in identifying the areas in which they are most efficient, as well as those in which they need to improve.

 

8. Detection of hidden risks in the process

The examination of data Around the equipment's previous failures, manufacturers can forecast its lifecycle and set up the proper predictive maintenance schedules, which can be usage or time based. All of this, in turn, aids in detecting gaps, reducing waste and downtime, and assisting businesses in developing a recovery plan in the event of an unexpected failure.

Furthermore, when big data is combined with AI, manufacturers can automate processes so that they self-optimize without the involvement of a human.

 

9. Product customization is now possible.

Historically, manufacturing units have focused on mass production while leaving customization to enterprises serving a narrow market. Data analysis for manufacturing enables customization during the manufacturing process by predicting demand and providing manufacturers with lead time.

Manufacturers can use big data to streamline their manufacturing processes by eliminating waste and forecasting demand. This streamlining saves them time when mass personalization of products is required.

 

10. Increased yield and throughput

Big data technology assists manufacturers in discovering hidden patterns in processes, allowing them to pursue continuous improvement initiatives with greater certainty. This has resulted in an increase in throughput and yield.

 

11. Price reduction

Big data can be used to determine a product's price point. To determine the best price point that suits both customers and businesses, the technology can collect and analyse data from multiple stakeholders such as customers, suppliers, and so on.

 

12. Image recognition

A manufacturing company can find a variety of image recognition-specific big data use cases. Let's look at an example. Assume you need a specific spare part but have no idea what it's called or how much it

costs. A big data-powered image recognition software can assist businesses in capturing images and providing details to manufacturers.

 

Now that we've covered the various reasons why big data is important in manufacturing, let's look at some real-world examples of how businesses have used the technology to improve production efficiency.

 

What are some of the most prominent real-world big data applications in manufacturing?

The manufacturing industry has demonstrated that big data provides numerous benefits to the domain. But how are those advantages being utilised in the real world? Let's look at some real-world examples of businesses to find out.

The Impact of Big Data on Business

Colfax – Application anomaly and pattern detection – Increased asset utilisation

National Engineering Industries Limited (NEI) – Improved visibility around the shop floor, line, plant, and enterprise performance – Preventing unplanned breakdowns through proactive actions

Kia Motors Inc. – Maintenance cost and failure rate forecasting – Production time reduction – Categorization and extraction of complaints from customer surveys to uncover quality issues Siemens – 36% less downtime on the system

German Railways – A 25% reduction in maintenance costs – A reduction in failures that cause delays

Now that we've examined the real-world applications of big data in manufacturing, let's look at how the technology can be adopted in the industry.

 

How can big data be used in the manufacturing industry?

While each project is unique, there are some steps that are common to all projects that call for the use of big data in manufacturing.

 

How to Implement Big Data in the Manufacturing Sector

1. Determine the business's key performance indicators (KPIs).

A big data project should begin with an understanding of what is expected from its inclusion. You will only be able to validate the profitability and feasibility of the project.

 

2. Examine the manufacturing issues

The following step would be to gather information about your current manufacturing requirements and needs. Only when you understand how your manufacturing unit is performing today will you be able to identify opportunities for big data integration. An assessment of your current situation will also assist you in developing a strong quality improvement process.

 

3. Conduct a project cost-benefit analysis.

After you've established the technology's KPIs and analysed the business issues, the next step is to determine the project's cost. Consider all aspects of the project's development, integration, and maintenance when estimating this cost. Once completed, compare this cost to the potential benefits that the manufacturing unit may receive.

 

4. Use big data in the manufacturing process.

After you've identified the processes into which you'll be incorporating big data and conducted a cost-benefit analysis, the next step is to collaborate with a reputable big data company. They will assist you with the seamless integration of technology in manufacturing facilities.

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Last thoughts

As we have discussed throughout this article, big data in manufacturing is the key to manufacturers increasing production efficiency, better predicting anomalies, and gaining a competitive advantage. However, implementing it in traditional systems is neither simple nor sufficient. Big data must be integrated with technologies such as IoT and AI in order to truly benefit from the technology.

 

What manufacturers actually require the assistance of a data analytics service powerhouse like Syoft is required to benefit from technology. Contact us if you want to modernise your manufacturing facility.

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