Data has long been the lifeblood of manufacturing. Companies have used it to increase efficiencies, improve performance and productivity, and reduce waste. With the advent of Industry 4.0 and the Internet of Things (IoT), the amount of data at hand has grown exponentially. Manufacturers are beginning to deploy big data analytics for two essential tasks: Gain control of the vast amounts of data their company creates and ensure they have access to the right information to drive productivity and enhance decision-making. So what should you look for in the best big data analytics solutions for the manufacturing sector?
It’s often said that the amount of data in the world doubles every two years. Estimates have suggested it will reach 40ZB (zettabytes) by 2020, bumped to 44ZB when IoT is taken into account. Although, other estimated put the amount of data from IoT devices alone at 600ZB by 2020. IoT is increasingly important to manufacturing where connected devices – such as sensors or meters – are being deployed in many facets of production and the supply chain. The data from these devices is growing at over 50 times the speed of traditional business data. And, this is only one source of data that manufacturers must manage.
Big data analytics software helps provides a solution by analyzing the large volumes of structured and unstructured data gathered from a wide variety of sources, including social networks, videos, digital images, IoT devices, and enterprise applications. They uncover patterns and trends that will deliver actionable insight to the business.
Manufacturing executives understand the benefits of big data analytics. Recent research from LNS Research and MESA International found that better forecasts of product demand and production (46%), understanding plant performance across multiple metrics (45%) and providing service and support to customers faster (39%) are the top three areas where big data analytics software can improve manufacturing performance.
However, manufacturing organizations have been slow to adopt the technology. Figures suggest that less than 20% of manufacturers have implemented a big data analytics solution. In recent research from Industry Week and OpenText™, two-thirds of manufacturing executives surveyed felt they were not maximizing the potential benefits of big data analytics tools for operational insight and decision-making.
Key use cases for big data analytics in manufacturing
Implementing a big data analytics solution has the potential to help almost every part of a manufacturing organization. Key use cases include:
Operational efficiency relies on the availability of the machinery in the production process. As the adoption of IoT grows, a big data analytics platform can minimize downtime by automating the data mining and data analysis from IoT sensors within the machine and can even automate its operations. Manufacturers can use the best big data analytics software–in combination with the Internet of Things – to see the status of the machine, and the parts within it, to determine when a machine can be brought online or shut down to prevent an issue. This approach is often called preventive maintenance and advanced predictive analytics algorithms ensure you receive the optimum productivity and uptime from your most valuable assets.
Developing new products is costly and the failure rate for new industrial products is over 50%. Big data analytics software can analyze data from support engagements, social channels and the web to unlock the “voice of the customer”. This helps identify trends and market changes that can be fed into the design of new products, increasing the chance of a successful launch. In addition, the best big data analytics platforms can analyze the information from the different teams within the development process to quickly identify errors and potential issues.
Big data analytics tools capture machine-level information to boost production yield and throughput. It can see how many products are produced at what cost and effort. This information can be stored in a central data store to ensure it quickly feeds quality systems to identify problem areas and conduct root cause analysis based on real-time rather than historical data. When spoiled runs in pharmaceuticals, for instance, can cost millions of dollars, better quality assurance (QA) brings immediate returns.
Traditionally demand forecasting has relied on the analysis of historical sales data, often using spreadsheets. By contrast, modern big data analytics tools for demand forecasting offer a comprehensive view of data across the user’s business processes. Further, they can apply advanced analytics to effectively identify recurring trends and anomalies in that data and align this with customer sentiment data to gain a clearer picture of future demand.
Excellent customer experience has become an essential part of every business. The best big data analytics solutions draw customer data from a wide variety of sources. This is then drawn together in a central data store where the data can be normalized and synthesized together to enable in-depth data analysis. From this, your big data analytics tool can synthesize data from a wide range of sources to provide a ‘single source of the truth’ for every customer. You can identify customer preferences, buying trends and engagement levels while beginning to personalize communications to customer touchpoints such as account or service information.
Supply Chain Optimization
Modern supply chains are evolving and becoming ever more complex. The best big data analytics solutions deliver supply chain visibility to instantly know key supply chain information such as which suppliers are performing well, whether they products they make are good quality, and how many orders are delivered on time.
The key benefits of big data analytics software
There are many benefits from using the latest big data analytics solutions. The best big data analytics platforms should deliver:
The journal of the Ivey Business School of Western University in Canada says that the use of big data analytics “is becoming a crucial way for leading companies to outperform their peers. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value. Early adopters of big data are using data from sensors embedded in products from children’s toys to industrial goods to determine how these products are actually used in the real world.”
The data in manufacturing is often used only for operational purposes, but the best big data analytics tools allow data from Internet of Things (IoT) sensors to be blended with data from enterprise systems and customer channels to encourage true innovation and business agility.
Big data analytics software can help companies innovate in how they operate, how they work with customers and suppliers and how they identify new opportunities for revenue generation. In the MIT Sloan Management Review Research Report Analytics as a Source of Business Innovation, 68% of respondents agreed that analytics has helped their company innovate.
In fact, some manufacturers have begun to use big data analytics tools to create entirely new data-driven business lines. A good example is the automotive industry where – with the rise of the connected car and connected consumer – car manufacturers are as much as software company as a car manufacturer. In fact, a McKinsey report showed that the amount of customers willing to switch car brand for better connectivity doubled in a year.
In the NewVantage Partners Big Data Executive Survey 2017, 49% of companies surveyed said that they had successfully decreased expenses as a result of a big data project. An enterprise-wide big data analytics platform can reduce costs in areas such as preventative maintenance to reduce the support burden; improved forecasting to reduce inventory levels; and automated or robotic process automation to reduce the amount of manual labor within any key business process.
Improved Customer Service
Manufacturers are beginning to use big data analytics solutions to examine social media, customer service, sales and marketing data. This can help them better gauge customer sentiment and respond to customers in real time. Armed with actionable insight from customer data, a manufacturer can begin to move from mass production to mass customization where products are increasingly personalized to the individual customer or market segment.
Why choose OpenText for big data analytics?
OpenText is at the forefront of developments in Business Intelligence, big data analytics and AI-powered analytics. Analytics has always been a large part of our solution portfolio and many companies – such as Knorr-Bremse – use our advanced analytics to achieve success in key areas such as preventative maintenance.
Including in OpenText’s analytics portfolio is OpenText™ Magellan™ – a flexible, AI-powered Analytics platform that combines open source machine learning with advanced analytics, enterprise-grade Business Intelligence, and capabilities to acquire, merge, manage and analyze big data and big content stored in your Enterprise Information Management (EIM) systems.
Download our big data analytics in manufacturing white paper to find out more.
Editor’s note: This is an installment in our “AI Glossary” series of blog posts, offering guidance on key areas of artificial intelligence and analytics. Look for future posts in this series over the months to come.