Understanding Big Data in The Manufacturing Industry

During the past few years, big data has become a trendy topic like AI or machine learning, but what does it actually mean? And how is it relevant in manufacturing?

Very simply, big data just means a lot of data. We live in an age where we can monitor almost everything in real time and analyze trends. Big data analytics has revolutionized the way companies operate, make decisions, and compete in the current landscape. Manufacturing companies can leverage big data collected from numerous sources, gaining insights into their operations. These insights can be used to drive innovation and boost efficiency.

The hard part isn’t collecting the data, however. It’s knowing how to actually use what you’ve collected to your competitive advantage.

It is important for manufacturing companies to understand big data and its current role in the sector. By knowing how to leverage big data to improve processes, manufacturers can stay one step ahead of their competitors. This includes optimizing supply chains, improving quality control, and performing predictive maintenance.

What is Big Data and Its Role in the Manufacturing Sector?

Big data is an overarching term used to describe enormous sets of data collected from the Internet of Things (IoT) - i.e. computers, sensors, machines, monitors, etc.

Big data is crucial in the manufacturing sector because companies can process and analyze this data to uncover trends, patterns, and insights in their business processes. This makes it easier for manufacturers to understand the strong points and weaknesses of their operations, driving real-time decision-making.

Big data is crucial because it can be used to improve product quality, reduce expenses, and boost efficiency. As an example, predictive analytics can be used to anticipate potential equipment failures and schedule maintenance in advance, drastically increasing safety and driving time and money savings.

Big data can also be used to optimize workflows, eliminate bottlenecks, and improve overall efficiency. A study published by Deloitte indicated that over 70 percent of manufacturers have woven big data analytics into their core processes. This study went on to show how these analytics significantly improved safety, sustainability, efficiency, quality, and overhead expenses.

The Core Components of Big Data: The 3 Vs

• Volume: The volume definition of big data has evolved over the years, but a rule of thumb is that big data generally involves at least a million rows of values. This data can be collected from sensors, social media, enterprise research systems, and other areas and devices. This volume of data requires specialized storage solutions and advanced processing capabilities to be used efficiently.
• Velocity: Velocity refers to not only the speed at which the data is collected, but also the speed with which it can be analyzed. To truly be useful, data has to be collected and analyzed in real-time to help manufacturing leadership to make decisions quickly. This helps manufacturing companies stay ahead of their competitors.
• Variety: Variety covers the different types of data generated, both structured and unstructured. In manufacturing specifically, data could be collected from quality control ports, production schedules, machine logs, and even customer feedback. Robust integration and analytics help yield a comprehensive view of all manufacturing operations, helping leadership make decisions.

How to Use Big Data in Manufacturing Processes

There are plenty of ways that big data has already proven useful in the modern manufacturing industry. A few examples of key big data use cases in manufacturing include:

Product Quality and Control

Big data analytics can dramatically improve quality control by detecting and addressing potential defects in real-time. As an example, General Electric uses predictive analytics to monitor all of its manufacturing processes to spot, address, and rectify any inconsistencies that might be detected. With constant vigilance, manufacturers can reduce waste and ensure consistent product quality. This is critical because it improves customer satisfaction while also reducing expenses tied to potentially defective products.

Predictive Maintenance

Big data also improves the efficiency of predictive maintenance by analyzing information collected from machinery. Then, this information can be used to predict when maintenance might be needed. Siemens uses IoT sensors to monitor the performance of its equipment and predict potential downtime, making it easier to plan accordingly and reduce the chances of machine or equipment failures. By becoming proactive instead of reactive, manufacturing companies can boost the efficiency of their operations and extend the lifespan of their machinery.

Streamlining Supply Chain Management

Big data can help manufacturing companies optimize their supply chain Logistics and inventory management. The goal is to integrate information from suppliers, shippers, customers, and numerous other sources to track order demand and optimize inventory and restock. For example, major retailers, including Walmart, leverage big data analytics to predict product demand and control inventory levels accordingly. Improved supply chain efficiency is crucial for customer satisfaction while reducing overhead expenses.

Innovating Product Design through Data Insights

Data-driven insights are also helpful because they can yield innovative product designs. Manufacturers can collect feedback from customers, compare this information to market trends, and make product design changes to better meet those demands. Nike uses big data to analyze customer feedback and improve the design of its products, better meeting the needs of its consumer base. Manufacturers can take the same approach to engage in continuous innovation and improve product offerings.

Identifying Hidden Risks

Big data can also be used to detect hidden risks that might otherwise be overlooked. These risks could potentially lead to product failures or supply chain disruptions, costing the manufacturer valuable time and money. Ford uses big data to spot anomalies in the production process that might lead to product defects or equipment failures. If manufacturers can use the same process to optimize defects ahead of time, managers can respond with proactive or preemptive measures to mitigate them and ensure smooth manufacturing operations.

Improvement of Yield

Manufacturers need to maximize the value of each individual resource, and big data can be used to improve yield. The goal is to use big data to identify efficiencies and redundancies before correcting and optimizing them. For example, McKinsey discussed how big data and AI were specifically employed to help one of its clients dramatically increase the yield of its production process. Increasing yield allows manufacturing companies to better meet the needs of their clients without increasing overhead expenses.

Optimized Pricing

Dynamic pricing is the norm in a lot of fields today, and this is only possible with real-time big data analytics. The goal is to use big data to analyze demand, compare it to production costs, track competitor pricing, and price products and services accordingly. Amazon uses big data to adjust its prices in real-time based on market conditions and competitor activity. This helps maximize productivity while ensuring that products are competitive while covering costs.

Customer Demand Forecasting

Big data can be used to forecast consumer demand and predict market trends, which is particularly beneficial when manufacturing seasonal products. With this information, manufacturers can adjust production schedules and inventory levels accordingly. Procter & Gamble uses big data to predict customer demand for key products and adjust production lines and prices accordingly. This information allows manufacturers that optimize supply chain processes and inventory restock to ensure products are available when demand increases.

Compliance

Big data even plays a key role in the world of compliance. This information helps manufacturers comply with regulatory standards by monitoring and reporting on various aspects of compliance automatically. As an example, Pfizer uses big data and AI to track adverse events surrounding its medications and vaccines, saving this information for reporting purposes. Manufacturing companies can leverage big data not only for compliance reporting purposes but also for improving products and services down the line.

Image Recognition

Image recognition technology can also be powered by big data as part of the quality control process. For example, Tesla uses image recognition as part of its car assembly procedures to detect imperfections and effects that might not be spotted by human inspectors. This process is critical for ensuring that only the best cars reach the market. Other manufacturers can also incorporate image recognition with big data analytics to provide a more reliable end result.

Real-World Examples of Big Data Use Cases in Manufacturing

There are several specific examples of situations where big data has already proven useful in the manufacturing industry. One of the first examples is the Predix platform from General Electric (GE). This industrial internet platform collects and analyzes data from various pieces of Machinery to optimize performance, predict maintenance needs, and improve the overall efficiency of the manufacturing process. When GE leverages big data using this platform, it cuts costs, reduces unplanned downtime, and improves operational efficiency and productivity.

Implementing Big Data Analytics: Challenges and Best Practices

Change is hard in manufacturing, but there are a number of best practices regarding big data that can make the change process easier. By following these best practices, it can be easier to overcome a few common challenges. Some of the most common challenges and best practices to know related to manufacturing and big data include:

Challenges

• Data Quality and Integration: It can be difficult to ensure appropriate data quality while integrating it from various sources. If the data is not formatted correctly, or if integration issues develop, it can make it difficult to clean accurate insights and maximize efficiency during the manufacturing process.
• Skill Gaps: Skill gaps are another major issue because the implementation of big data technology requires specialized skills that not everyone has. It is a major investment to train and hire data scientists and analysts with the necessary skills to maximize the value of big data.
• Technological Infrastructure: Developing the necessary technological infrastructure to support this type of database can also be expensive. It requires advanced software, hardware, and network capabilities to address such large volumes of data.
• Security and Privacy: Protecting such large amounts of data while ensuring privacy can also be a major challenge. It is crucial for manufacturing companies to implement strong security measures to safeguard against the threat of data breaches while also complying with all regulatory requirements.
• Change and Transition: Implementing big data Solutions represents a significant transition for the majority of manufacturing processes and cultures. It is not unusual for companies to face resistance from employees, and it is crucial to develop a comprehensive strategy to manage this transition accordingly.

Best Practices

• Establish Clear Business Objectives: When you transition to big data, you must clearly define what you want to achieve. For example, do you want to reduce turnaround times? Do you want to reduce costs? Be sure to define clear objectives that will guide you as you deploy the rest of your initiatives.
• Analyze Manufacturing Issues: You need to identify a specific manufacturing issue that you would like to tackle. Without having a specific issue in mind, you will have a difficult time customizing your big data solution to resolve many of the operational challenges that you face.
• Perform a Cost-Benefit Analysis: Before you implement big data, you must always perform a cost-benefit analysis. You need to figure out exactly what it is going to cost you, in terms of time and money, and what benefits you will enjoy on the other side. That way, you know that it is worth the business investment.
• Start Small: You do not want to implement every change at once. You should always start with small, manageable projects before you scale up your project to include the entire company. This incremental implementation is important because it allows you to adjust your plan based on the initial results of the transition.
• Integrate Big Data Into Existing Projects: You need to find a way to implement big data analytics into your current manufacturing processes. This type of integration is important because it lets you know that you are effectively utilizing the data insights you are gathering from the project itself.
• Find an Experience Partner: It may not be realistic to stand up a big data infrastructure in-house, all at once, especially if you have limited resources. An experienced partner can help you navigate your first few data analytics projects and help you get your feet under you. Look for a partner that will try to understand your business deeply, so they will generate the best outcomes for you.

The Future of Manufacturing with AI and Big Data

Clearly, big data has the potential to change the way manufacturing processes operate, and the emergence of artificial intelligence (AI) is only going to accelerate this transition. When AI and big data come together, it has the potential to improve automation, boost efficiency, and drive precision. AI algorithms are only becoming more advanced, and they will make it easier to implement predictive maintenance with greater accuracy, extending the lifespan of your equipment while reducing downtime in the process. AI also has the potential to change the way you manage your supply chain, allowing you to make adjustments in your supply chain based on real-time market demand and inventory levels.

AI is also leading to the birth of new technologies, such as digital twins and augmented reality. Digital twins are designed to act as virtual replicas of physical systems, making it easier to perform simulations and gather predictive analytics. AR can improve training, maintenance, and quality control by overlaying data for employees, boosting productivity, and reducing error rates.

AI and big data have the potential to drive smart factories, where multiple interconnected systems communicate and collaborate with each other. These factories can leverage machine learning, boosting operations while adapting to changing market conditions. Manufacturing companies that position themselves to take full advantage of these emerging technologies will stay one step ahead of their competitors.

Learn How EnterBridge Can Help

Ultimately, the integration of AI and big data can revolutionize the manufacturing industry by expanding automation, making manufacturing processes more accurate, and responding to the real-time demands of customers. At EnterBridge, we specialize in learning how your business specifically operates and providing custom big data solutions that have been tailored specifically for the manufacturing industry and for your business model. Our services can help you leverage predictive analytics, real-time quality control, and advanced supply chain organizational tools. We are here to help you reduce your overhead expenses while boosting your operational efficiency.

With our experience, we can help you seamlessly integrate these new technologies into your existing manufacturing operations. Our experts are always available to help you maximize the value of each individual resource, so book a call with us today to learn more about how you can use big data to position your manufacturing business for success in the current environment.