Big Data in manufacturing made simple: Trends, tips, success stories
N-iX
2020-10-22T20:58:55+00:00

Big Data is changing the face of manufacturing as we know it. Gone are the days when data scientists need to mine, organize, and manage volumes of diverse data manually or run experiments to test the hypothesis. With Big Data, manufacturers can predict business outcomes, enhance the quality of their...

Big Data in manufacturing made simple: Trends, tips, success stories

Big Data is changing the face of manufacturing as we know it. Gone are the days when data scientists need to mine, organize, and manage volumes of diverse data manually or run experiments to test the hypothesis. With Big Data, manufacturers can predict business outcomes, enhance the quality of their products, reduce equipment downtime, and optimize costs

More and more manufacturers are adopting Big Data at their plants. As the adoption of Big Data in manufacturing grows, so does its market size. The Big Data market in the manufacturing industry is predicted to reach $9.11B by 2026, according to Fortune Business Insights. So your business should be ready for the era of Big Data too.

Why is Big Data a must in the manufacturing industry? How is Big Data changing the world of manufacturing? How to take full advantage of Big Data in manufacturing? We’ve tried to answer these and a few other questions.

In this article, you will discover:

Why do manufacturers need Big Data?

Big Data in manufacturing offers a wide array of benefits, including lower operating costs, great production efficiency, and improved risk management, to name a few. 

Leading manufacturers use Big Data to analyze the flow of finished goods and respond better to demand signals. IBM states that 53% of manufacturers apply Big Data and Data Analytics to create a competitive advantage for their organizations.

In the time of the pandemic, Big Data has become even more critical. Experts from Gartner claim that the global financial impact of the pandemic on the economy will vary between $2T and $4.5T. No wonder that cost-reduction is manufacturers’ top priority these days.

Big Data in the manufacturing industry helps to rationalize processes and thus eliminate unnecessary expenses. For example, with the help of Big Data, John Deere (Deere & Company) was able to save $900M in inventory control over a period of two years. Also, the Coca-Cola Company was able to save around $45M annually by following big data analysis.

Due to the pandemic, some businesses (24%) felt the urge to invest more in workflow automation and optimization technologies. 18% of companies expect to spend more on data analytics tools and technologies.

Big Data in manufacturing: businesses tend to invest more in workflow automation and optimization

Now, let’s analyze the benefits of Big Data in manufacturing in more detail. 

Benefits of Big Data in manufacturing

Big Data in manufacturing industry: key benefits

  1. Reduced machine downtime

Big Data is used for performing predictive and preventive maintenance. Hardware downtime is a common issue in manufacturing. It requires immediate troubleshooting from engineers, which slows down the whole manufacturing process. Thanks to Big Data, manufacturers can predict machine failures and take proactive measures to repair the equipment and ensure that production doesn't reach a standstill. What’s more, some modern technologies allow self-diagnostics and can automatically shut down to prevent further damage. 

Big Data makes the analysis of vast amounts of information possible. It helps to recognize patterns in the great scopes of data, thus predicting when the machine may fail. So, it gets easier to fix the problem and reduce operational expenses by up to 50%.

Equipment breakdown and scheduled maintenance are part of the game in manufacturing. However, Big Data analytics in manufacturing can reduce the time required for these activities. Forbes states that Big Data in the manufacturing industry can reduce breakdowns by 26% and unscheduled downtime by 23%.

  1. Enterprise growth

Big Data tools help to compare the performance of different sites and pinpoint the reasons for the differences. With Big Data, you can analyze your production plants, develop what-if scenarios, and apply predictive models. Based on factual data gained through Big Data analysis, you can make a well-informed decision about where to open a new factory, which company site should be relocated/closed, or whether introducing a new product, etc. 

  1. Better cost management

With the help of predictive analytics, budget planning becomes easier. It gets possible to understand the costs needed for problem-solving. 

Also, Big Data analytics can help to track and understand the root causes behind overhead costs. Manufacturing businesses can’t start reducing their indirect costs without knowing the average amount they spend on things each month. Big data analysis helps in this area by providing baselines that inform manufacturers of their most substantial indirect expenses. Then, it gets possible to start figuring out where to make improvements.

  1. Enhanced customer service

The success of your manufacturing business depends heavily on the satisfaction of your customers. With Big Data, you can analyze their experience with your product, thus making it better. Deloitte claims that 36% of consumers are interested in purchasing customized products and services, and 48% are ready to wait longer for those goods. So, Big Data, accompanied by IoT devices, can provide valuable insights into your customer experience. 

Why you need Big Data in manufacturing industry: goods customization

Big Data in manufacturing is no longer a choice; it is a must that helps to stay competitive in the vast manufacturing market. 

2020 is a year of innovation and evolution for Big Data, and there are a number of trends that prevail now. Let’s take a look at them in more detail.

Trends of Big Data in the manufacturing industry

#1 Big Data trend in manufacturing: the hybrid and multi-cloud data strategy

The market is moving to interoperable multi-cloud environments with the ability to connect private and public clouds. A hybrid and multi-cloud approaches allow companies to gain visibility and control over all the places where they’re doing business. Thus, manufacturing can introduce innovations in a much more secure and efficient manner. 

There are two main reasons why manufacturers opt for the hybrid and multi-cloud data strategy: cost-optimization and security. Manufacturing businesses choose to store their data in a hybrid cloud for the sake of control over their cloud expenses. According to Flexera, over 36% of manufacturers that use just public cloud state that their cloud spending exceeds their budget. 

What’s more, hybrid cloud and multi-cloud strategies are touted as the secure way of data storage, and 34% of manufacturers choose security and compliance as their top priority. 

So, we expect to see later adopters of Big Data bring the hybrid and multi-cloud methodology to the forefront of data ecosystem strategies.

#2 Big Data trend in manufacturing: a Data lake

A data lake is a trend of Big Data in manufacturing, as it obtains data from all sources, allowing manufacturers to receive and store any information to analyze it later. Data lake loads data from multiple sources and in its original format. A data lake works especially well with manufacturing, as manufacturers usually need various data formats that cannot be stored in data warehouses. 

It is also a cost-effective option of data storage, as a data lake relies on low-cost storage options to store the raw data. What’s more, data can be updated in real-time or in batches. 

Data lakes are a great source of insights for the manufacturing industry due to their ability to make predictions. These predictions can help companies to reduce their costs and improve predictive maintenance.

#3 Big Data trend in manufacturing: Machine learning and AI

Data-driven organizations need Machine Learning and AI as mere fixing of already existing failures is no longer efficient enough. Big Data, Machine Learning, and AI give the ability to recognize patterns in the great scopes of data, thus predicting when the machine may fail.

“Smart manufacturing processes and workflows generate extremely large volumes of data, but the vast majority of it is useless without ML models and AI workflows to identify, infer, or act upon patterns in the data.” - The Forrester Tech Tide™: Smart Manufacturing, Q2 2020, Forrester Research, Inc., April 1, 2020

By predicting failures, manufacturers get the ability to predict and plan costs on problem fixing. So, manufacturing businesses opt for predictive or condition-based maintenance. 

#4 Big Data trend in manufacturing: Computer vision

Computer vision or machine vision is a tool for analyzing dynamic human action in real-time that observes, classifies, and responds to human events as they unfold. Computer vision (CV) brings a wide array of benefits to the manufacturing industry, including time efficiency, reduced costs, and enhanced production accuracy. Let’s view these benefits in more detail.

Firstly, with the help of computer vision, the automated system can operate faster and work around the clock if needed. So, the whole manufacturing process gets more time-efficient.

Secondly, CV-based solutions allow manufacturing businesses to achieve a higher level of accuracy within the accepted tolerance. Specific equipment, in combination with CV algorithms, enables near-perfect precision in both the production process and quality control.

Thirdly, CV-driven solutions can help you to reduce not only labor costs (fewer staff members are needed to control the process) but also operational costs. As there is less waste due to less room for mistakes. 

Big Data in manufacturing industry: Computer visionFor instance, to help our client reduce operational overhead and warehouse downtime, N-iX has developed a Computer Vision (CV) solution for a German Global Fortune 100 multinational engineering and technology company. This solution for cameras installed in warehouses allows automatic detection of arriving packages, scanning barcodes, and changing the delivery statuses of the boxes. 

These Big Data trends in manufacturing are expected to evolve further and enable digital transformation in this industry. But how can manufacturers leverage Big Data in manufacturing industry?

Big Data analytics use cases in manufacturing

Big Data has become a big game-changer in most industries over the last few years. It helps companies across healthcare, retail, finance, telecom, media, entertainment, etc. to make smarter decisions and predict business outcomes. 

In manufacturing, however, the biggest value of Big Data is that it can not only forecast problems using data but also actually solve them. The time has come to shift from mere descriptive and predictive analytics to prescriptive analytics. It involves scenario planning where companies plan activities based on data they have to avoid any untoward incident in the future. They can anticipate, for instance, shortage of resources or a reduction in cash flow. Industrial manufacturing companies excel at different types of Big Data analysis techniques. According to the IBM report, they outpace their cross-industry peers in almost all analytics capabilities

Big Data in manufacturing industry: Predictive analytics

So, how can manufacturers benefit from Big Data in their businesses? What are the Big Data analytics use cases in manufacturing?

  • Predictive maintenance

Many manufacturers say it is a serious challenge to foresee when you need to service the equipment. It is difficult to understand the risks of lost production time because of a potential breakdown. Predictive maintenance, in turn, allows manufacturing businesses to reduce the downtime and align maintenance schedules to create as little disruption as possible.

When the machine shuts down, professionals analyze why it has happened and collect data about this failure to prevent such damages in the future. 

Big Data in manufacturing: Predictive maintenance flow

  • Price optimization

Big Data in manufacturing can also be used for price optimization: when the price of the final product differs between suppliers and different customers. When the data is collected, aggregated, and the data warehouse is ready, it is possible to run Data Science and Data Analytics to understand the price for each client. 

  • Image recognition

Manufacturers can also leverage Big Data for image recognition at their manufacturing sites. Let’s say you need a specific spare part and have no idea what it is called, who is a supplier, how much it costs, etc. Searching for this information manually in the system takes a great deal of time. Instead, you can take a photo of this product and upload it to the system, and it will give you all the information you need. This technology is based on the data lake, where the images of the products are collected. 

  • Product line quality control

Thanks to Big Data, it is possible to identify quality issues in line production at the early stages. For instance, with the help of image recognition, manufacturers can check if the final look of the products corresponds to the required quality level. If the products have some defects, it becomes easy to detect them before they reach the customers.

Also, Big Data helps to reduce the number of no-fault-found (NFF) cases. NFF is a unit that is removed from service following a complaint of the perceived fault of the equipment. If there is no anomaly detected, the unit is returned to service with no repair performed. The lower the number of such incidents is, the more efficient the manufacturing process gets. 

  • Production forecasting

To optimize production, manufacturers must anticipate demand. Big Data in the manufacturing industry comes in handy when collecting information about the operations, business, and suppliers. It can help you to prepare better for the future. Analyzing the demand is crucial to avoid any shortage or excess of goods. 

  • Supply chain

Big Data provides valuable insights at every stage in the logistics chain. In transportation, Big Data allows tracking freights as well as weather and road conditions in real-time. Due to that, trucks can be diverted any time on their way when a more cost-effective route is possible. 

In warehousing, with the help of Big Data insights, businesses can monitor or track the stock of supplies available in their warehouses. 

Also, Big Data can help with the traceability of products when it comes to inventory management. It allows manufacturers to track the location of their goods in the entire logistics cycle. 

  • Testing and simulation of new manufacturing processes

Both manufacturing processes and products can be tested before production. This is possible thanks to digital twins, VR environments, and manufacturing process simulations. Using such environments and tools allows manufacturing businesses to optimize operations, detect issues, test settings, simulate scenarios, and predict performance. 

How to effectively adopt Big Data in the manufacturing industry?

There are three significant steps you should take to adopt Big Data in manufacturing. They are:

  1. Establishing transparent business KPIs and calculate ROI

It is vital to establish clear KPI’s and calculate ROI. Suppose you need to validate the feasibility and profitability of your business. In that case, you can undertake a Discovery Phase and, based on calculations for different scenarios, understand what benefits the implementation of Big Data can bring to your manufacturing process. The Product Discovery phase will provide all the deliverables required to efficiently kick off the implementation phase while addressing risks and optimizing costs.

  1. Analyzing your manufacturing problems

It is important to get more details about your manufacturing problems and needs. You need to analyze the quality of your final product and the ways it can be improved. After that, you have to consider all the pros and cons of Big Data adoption. Also, you have to understand how the quality improvement process can be enhanced by Big Data in manufacturing. 

  1. Ensuring an effective Big Data engineering process

In addition, you have to remember that the success of any Big Data analytics project heavily depends on the following aspects:

  • finding top-notch Big Data experts that can help you on this challenging path;
  • choosing the right data sources;
  • building an orchestrated ecosystem of platforms that collect siloed data from various sources;
  • cleaning, aggregating, and preprocessing the data to make it fit a specific business use case;
  • applying Data Science or Machine Learning/AI models;
  • visualizing the insights. 

Businesses that benefited from Big Data in manufacturing

  1. Fluke Corporation is a US-based manufacturer and distributor of electronic test tools and software for measuring and condition monitoring. N-iX has helped the client to improve asset maintenance by arming maintenance teams with critical asset information in real-time as well as tools that can efficiently process it. We have ensured reactive and preventative maintenance of equipment based on real-time asset data rather than a predefined calendar. As a result, the client has improved asset maintenance and significantly decreased maintenance costs.
  2. Another N-iX client (under NDA) - an automotive technology company, needed a reliable partner for its digital transformation phase. The client wanted to digitize its manual processes. N-iX team has helped this client to computerize the flow of warranty data between mechanics and vehicle manufacturers using a cloud-based application. With the help of the unified platform that stores all the data in one place, our client has improved the efficiency of data management.
  3. A Germany-based Global Fortune 100 multinational engineering and technology company has partnered with N-iX to redevelop its legacy platform. N-iX works on a computer-vision solution based on industrial optic sensors and lenses and Nivida Jetson devices. This solution will allow this client to manage and track the goods. 

Why choose N-iX as your Big Data services provider?

  • N-iX boasts an internal pool of 1,100+ experts and a team of 70+ data analytics specialists.
  • Our specialists have solid expertise in the most relevant tech stack for implementing Big Data in manufacturing, including BI, Data Science, AI/Machine Learning, Computer Vision, etc.
  • N-iX partners with Fortune 500 companies to help them launch Big Data projects and migrate their Data to the cloud.
  • Our Big Data experts have experience working with open source big data technologies (both on-premise and cloud-based) such as Apache Spark, Hadoop, Kafka, Flink, Snowflake, Airflow, etc.
  • N-iX complies with international regulations and security norms, including ISO 27001:2013, PCI DSS, ISO 9001:2015, GDPR, and HIPAA, so your sensitive data will always be safe.

References:

  1. The Forrester Tech Tide™: Smart Manufacturing, Q2 2020, Forrester Research, Inc., April 1, 2020
  2. All Enterprises Need (Computer) Vision, Forrester Research, Inc., June 14, 2019
  3. Analytics: The real-world use of big data in manufacturing by IBM
  4. Consumer Review: Made-to-order: The rise of mass personalization by Deloitte
  5. Cloud Computing Trends: 2020 State of the Cloud Report by Flexera
  6. Big Data Analytics' Potential To Revolutionize Manufacturing Is Within Reach by Forbes
  7. Leading Through COVID-19: Post-Pandemic Opportunities for Manufacturing Industries by Gartner
  8. Effective ways to reduce manufacturing costs by BASM
  9. Big Data in Manufacturing industry size. Share & Industry analysis by Fortune Business Insights
N-iX Staff
Igor Tymchuk
Delivery Director

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By Igor Tymchuk October 22, 2020

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About N-iX

N-iX is an Eastern European provider of software development services with 1000+ expert software engineers onboard that power innovative technology businesses. Since 2002 we have formed strategic partnerships with a variety of global industry leaders including OpenText, Novell, Lebara, Currencycloud and over 50 other medium and large-scale businesses. With delivery centers in Ukraine, Poland, Bulgaria, and Belarus, we deliver excellence in software engineering and deep expertise in a range of verticals including finance, healthcare, hospitality, telecom, energy and enterprise content management helping our clients to innovate and implement technology transformations.

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