Big data in manufacturing is one of the key priorities of businesses planning to optimize their production process. At present, the need for process automation and greater efficiency is driving the demand for advanced data analysis across the global manufacturing industry.
Leveraging big data in the manufacturing process can help in extracting relevant data from multiple devices. By doing so, manufacturers get valuable insights to boost production, process quality, and operational workflow at their premises.
As per a report, the projected market share of the global big data in the manufacturing industry is USD 509.3 billion for 2028. This value will increase at a CAGR of 12.45% from 2022 to 2028.
Besides production & quality benefits, big data plays a major role in enterprise resource planning (ERP) which helps in automating manual manufacturing operations for added efficiency.
Hence, it makes sense when a manufacturing venture considers investing in big data analytics services to upscale their production units, product quality, inventory management, etc. To get more clarity on how exactly big data analytics can make a difference in the manufacturing sector, this blog is all you need to read.
Table of Contents:
Big Data in Manufacturing: A Brief Overview
Big data is gaining popularity across vast industry domains & manufacturing is one of them. The technology has been helping manufacturers in lowering production wastage along with the benefits of better product quality & higher yields for over 20 years.
With advancing technologies and market changes, a vast segment of manufacturers is realizing the importance of big data analytics in their businesses. There is a major spike in the demand for different types of data analytics in the manufacturing industry especially among those involved in the pharmaceuticals, chemicals, & mining sectors.
Recommended Reading:
Well, understanding the role of big data in manufacturing is not rocket science. Here is how data science is used in real-world manufacturing environments:
-
By giving useful data insights into manufacturing operations, advanced data analytics allow businesses to diagnose and fix flaws in the production process.
-
By implementing big data solutions in manufacturing, operation managers can extract production history. It enables them to discover patterns of the stages and steps involved in the production process. This helps in optimizing the key components influencing the results.
-
By using data analytics in manufacturing, production teams can collect huge datasets from various devices, sensors, or machines. Companies using big data count on these datasets to track, maintain, and analyze the production process, quality, risk, & other areas of the operations.
-
By integrating advanced manufacturing analytics, businesses can track and identify changes in customer behavior. In this way, they can customize goods or products according to the market demands and at a larger scale.
Recommended Reading:
Advantages of Data Science in Manufacturing
Big data is revolutionizing the modern manufacturing process. It involves collecting both structured & unstructured data from factory machines & devices at every stage of the production process.
There is a long list of benefits for companies using big data in their manufacturing operations at present. So, let’s check them out!
Higher Competitive Advantage
Data analytics in manufacturing is a great tool for effectively analyzing operational activity, underlying errors, and product quality. It makes it possible to collect data from different sources such as integrated machines, IoT devices, sensors, etc. As a result, it becomes easier to utilize that data for analytical purposes.
With big data, manufacturing teams get to learn market trends, customer demands, and industry forecasts. And that gives a competitive advantage to manufacturers investing in big data management and analytics.
Do consider the benefits of enterprise business intelligence to get actionable insights and attain high competitiveness in your business.
Reduced Downtime
Issues in hardware or machinery cause major interruptions in the production process. It results in downtime, which is a big concern for any manufacturing firm.
It slows down the production process and hinders the employees’ productivity. With big data in manufacturing, businesses can take the benefits of industrial data analysis to conduct preventive & predictive maintenance on their hardware.
Big data implementation is most likely to reduce manufacturing downtime by keeping track of hardware performance on a regular basis.
We at BluEnt advise clients on how to produce, consume, and govern complex information using the appropriate mix of historical, current and predictive analytics.
We enable our clients to cut through complexities and bring clarity to problem-solving by providing insights that:
-
Help create new revenue-generating opportunities
-
Improve operational efficiencies and visibility across the organization
-
Enable faster problem-solving and decision making
-
Optimize the return on existing business and IT investments
Production Management
Building the right volume of products with the right understanding of the market needs is a core production management strategy.
To avoid overproduction or underproduction of goods, manufacturing firms can count on predictive analytics rather than being reliant on human estimates. Big data analysis empowers manufacturers to take precise business decisions without depending on unclear predictions.
Enhanced Customer Experience, CX
Customer experience matters a lot for any online and offline business, so as manufacturing. Companies using big data to optimize their production process deploy sensors to share routine alerts to the technicians regarding device maintenance & support.
Those RFID-powered sensors enable workers to keep a check on the condition of units and analyze their shared reports to go with the most ideal business decisions.
Need solutions to build a superior customer experience & generate high traffic? Get custom and advanced customer relationship management software to boost overall CX on your business.
Supply Chain Management
Big data technology adds value to inventory and supply chain management. Most manufacturing units work with data analytics companies to set up advanced systems for recording & tracking the location of products after being dispatched.
Backed by domain expertise and experience regarding the challenges you face, BluEnt’s SCM solutions offer you:
-
Increased profitability through reduced supply chain operational costs
-
Enhanced competitiveness through improved customer service and satisfaction
-
Improved revenues and expanded market share through effective management of growth and expansion
-
Ability to emerge as supply chain pioneers
To start optimizing your supply chain,
They use modern hardware such as radio frequency transmission devices and remote barcode scanners to trace product locations. It enables them to give the most realistic timeline for product delivery.
Recommended Reading:
Easy Product Customization
Manufacturing data analysis helps enterprises to customize products as per consumer needs. Unlike in earlier days, manufacturers are no longer depending on product teams to improve their products according to the target audience.
Big data in manufacturing enable businesses to predict product demand & customize them right at the initial stage of production. In this way, manufacturers are more opportune to deliver customized products at a higher scale.
Recommended Reading:
More Flexibility to Survive Market Fluctuations
One of the biggest challenges of a manufacturing firm is surviving market changes. It’s always doubtful to predict anything about the market fluctuations that can happen due to various factors.
By leveraging business intelligence, manufacturers can determine what exactly customers are expecting from their products. Let’s say, manufacturing teams can use custom CRM applications or software to determine the differences in order & consumption patterns. They can also collect CRM data to get real-time forecasts and adjust their production process accordingly.
In this way, the benefits of using customized CRM applications in a business will make it easier to cope with market fluctuations.
Preventing Underlying Risks in the Process
Recording the history of equipment failures and recurring downtime will be a great advantage for manufacturers facing unexpected glitches in the production process. Similar to the role of predictive analytics in shaping innovative healthcare management, big data in manufacturing works in forecasting and setting up the scheduled maintenance of machines. This further helps in identifying and avoiding potential lags in the process.
Also, the power of AI tools like ChatGPT for business data analytics also boosts process efficiency without involving external manpower.
Price Optimization
Companies using big data analysis can also use relevant insights to finalize the best price points for consumers as well as suppliers. The data collected from buyers and multiple stakeholders will help you analyze and optimize product prices more efficiently.
Recommended Reading:
Real-world Examples of Data Science in Manufacturing
Big data is one of the most preferred technologies by top brands. As per reports, Coca-Cola company managed to save a sum of USD 45 million per year upon using big data analysis for vehicle routing setups. Another renowned company, Deere and Company saved a whooping amount of USD 900 annually by using big data analysis for inventory control benefits.
Surprised? Let’s give you more real-world examples of companies using big data in manufacturing.
-
National Engineering Industries Limited used big data for enhancing visibility in the store, manufacturing plant, and enterprise. It helped them reduce unplanned breakdowns by taking immediate action.
-
Colfax increased the utilization of resources by using big data analytics. It also led them to identify the abnormalities & underlying issues in the system applications.
-
Deutsche Bahn company saved 25% in maintenance costs & reduced operational failures causing delays in the process.
-
Kia Motors lowered overall production time, maintenance costs, and failure rates by implementing big data in its manufacturing process. They also used this technology to categorize customer complaints & determine the quality issues.
-
Siemens Healthineers got benefits from manufacturing data analytics with lesser product failures and system downtime.
Recommended Reading:
Noting the real-world data science use cases, we can say that the manufacturing industry is ahead in the race for big data adoption. Now proceed further to understand how you can integrate big data in manufacturing.
Want to collaborate with a big data expert for your manufacturing firm? BluEnt is the right place to get custom big data solutions to boost your business. Contact us today.
How to Integrate Big Data into Manufacturing
Besides varying project requirements, you should consider some common steps to incorporate big data in your manufacturing process.
The steps involved in the integration of data analytics in manufacturing are:
Step 1: Setting Business KPIs
At the initial stage, you need to define the Key Performance Indicators or KPIs of the project. It’s crucial to validate your intent behind big data adoption in your manufacturing business.
Defining the KPIs beforehand will give you clarity about the profits and feasibility of the technology you will be using in the project.
Recommended Reading:
Step 2: Determining Issues in Manufacturing
Once you note down the major KPIs of your project, the next step is to determine the key requirements in the manufacturing. One needs to have a deep understanding of how the existing production system works and what refinements are needed.
The scope of big data integration will come up once you analyze the ongoing manufacturing process.
Recommended Reading:
Step 3: Analyzing Cost-driving Factors of the Product
Budgeting is a critical step in the process of integrating big data in manufacturing. Upon identifying your project KPIs & the issues in the current process, you need to prioritize cost estimation.
Get the lowest quotes from your big data consultants and make sure they include support & maintenance. In this way, you can balance cost & quality with your enterprise mobility solutions for big data implementation.
Step 4: Integrating Big Data in the Manufacturing Process
After getting a clear picture of the entire process, issues, and costs, incorporating big data is the final step. You need to hire experts at a reputed big data agency to integrate big data in manufacturing.
Meet Your Goals with BluEnt’s Big Data Services for Manufacturing
Big data integration has become one of the core business strategies for manufacturing firms. They’re reliant on data science use cases to improve their production efficiency and cut downtime.
We at BluEnt are the helping hands for businesses aiming to get custom, scalable, and business-centric big data solutions. Our experts are well-versed with the latest technologies & tools for crafting next-gen web and mobile apps, websites, products, and CRM solutions.
Our portfolio reflects more than 1100 projects covering industries from eCommerce and retail, finance, healthcare, manufacturing, travel, and other emerging sectors. Our bespoke specialization includes product development, mobile app development, web app development, web design and development, portal development, and software development.
Need experts to help you with big data solutions? Drop us an email to get a quote.
Frequently Asked Questions
How do manufacturing data analysis used in a smart factory?
Most system integrators & product engineers are relying on data analytics companies to allocate resources & get productivity benefits. They used advanced predictive and analytical tools to give cost-saving options for manufacturing process automation.
What are the best manufacturing data analytics tools?
The most preferred tools for manufacturing data analytics are:
-
Cloudera
-
Tableau
-
KNIME
-
Apache Hadoop
-
Datawrapper
-
Xplenty