Industries need big data
The IT industry has always been interested in big data, and the manufacturing sector is also trying to harness it for better decision-making. Industrial sectors are realizing the potential benefits of big data analytics for complex plant operations and R&D.
Big data analytics can have a tremendous impact on the industrial sector, whether it’s monitoring shop floor operations, managing supply chains, understanding customer preferences, or monitoring the shop floor. All industries are planning or considering using data and analytics to improve their internal processes. Big data can help optimize critical business metrics and provide clear benefits such as improved insight and simplified plant operations.
Areas Of Impact :
- Revenue and profits: Technology executives and Research & Development heads of industrial companies must determine if a new technology is a good investment to generate long-term revenue. If an OEM only has 10% of its field devices connected with software systems, should they invest in next-generation communication interfaces like Ethernet APL? Or should it consider edge devices for more extreme use cases? A rational decision can be reached by analyzing industry use cases, customer behavior and industrial product ecosystem to make a more nuanced decision.
- CapEx and OpEx. Revenue and profits can’t be separated from CapEx or OpEx. These directly affect the operations of the plant and business decisions. If a plant asset experiences frequent downtimes, and thus reduced productivity, the plant engineer must identify the root cause and take steps to prevent future failures. This information can be retrieved from asset performance logs. Analytics can then be used to analyze this massive amount of data to determine the frequency of downtimes. Then, the steps that can be taken to avoid them can be determined.
- Multi-dimensional Data Analysis (Multi-dimensional Data Analysis): Different plants have different parameters that can be used to determine the success of their plant operations. Analyzing data at all levels of the plant architecture provides management with a deeper understanding of different parameters that can be controlled in order to maintain efficient shop floor operations. Factors that affect efficiency, security, turnaround times, and realizability. This information allows manufacturers to cut costs directly and indirectly.
Industries need to develop a data strategy that is efficient in order to gain competitive advantage. How does this happen?
The Ideal Data Strategy for Industries Faced with Digital Transformation
It doesn’t necessarily mean you are winning the game just because you have large amounts of data. The first step towards digitalization is gathering data. Every system, device and equipment that generates data in an IIoT-centric world is responsible. This volume of data can quickly become overwhelming. Ten plant assets are enough to generate 10 million records per day. Data noise is data that does not contain all the data necessary to meet business objectives.
It is possible not to know which data points are valuable. If you want to use data as a key weapon in your industry, it is important to link it to a business value. These are some of the questions that could help you achieve it:
- What are the business objectives?
- What data are needed to achieve this business goal?
- Are the data already available?
- Is this data possible to be used for business purposes?
- What’s the best way to reach this business goal?
Any data that is directly or indirectly related to a business goal should be analysed. Only then can meaningful outcomes be expected.
Big Data Management with Technologies
Only a solid information foundation can help industrial companies achieve their vision of creating business value from big data. Hadoop, MapReduce and NoSQL engines are some of the most popular technologies preferred by manufacturers around the world.
Big Data Implementation Challenges
Despite having a big data strategy in place and a big data infrastructure, there are some factors that prevent manufacturers from realizing the full potential of big-data. These are:
- Lack of common context: Manufacturers will need to adapt to new equipment/devices. It is more difficult to coordinate and integrate all the devices the more they are. Their data may not be in sync due to heterogeneous logics, languages, and make. As an example, suppose that a sensor returning value of ‘1″ means “the temperature has reached normal”, while a sensor returning value of ‘1″ means “the temperature has reached high”. As the number of connections increases, it becomes more difficult to manage.
- Infrastructure not suitable to handle big data: This is more than just gathering big data. It also involves all the supporting systems that allow for the processing, storage, and analysis of this data. Are the networks capable of streaming such large amounts of data without frequent outages? How efficient is the database to store such a large amount of unstructured information? The visualization platform will be able to accurately represent these diverse datasets. These situations are often overlooked by manufacturers who invest in big data technologies.
- Data not impacting decisions: After data has been gathered, it is time to make informed choices. Industrial OEMs are often unsure how the data will impact their business. Multi-dimensional data may indicate a problem but decisions are still made based on intuition. OEMs often use big data analytics to analyze historical events and identify patterns. This data does not reflect or indicate the actual shop floor situations. Retrospective analytics cannot be relied upon to make critical decisions in real time. This hinders OEMs from fully embracing big-data initiatives.
- Legacy Systems Unable To Incorporate Big Data Capabilities. Most equipment and devices within a plant last between 10 and 20 years. If you look at it from the evolution perspective, this timeframe is significant. Some systems are older and some have limited connectivity options. It becomes difficult to integrate IIoT capabilities into such systems. Therefore, big data can only be used to achieve the expected value of modern IIoT.
Conclusion
Complex industrial operations can be difficult. Data is an integral part of plant operations, especially on the shop floor where even small errors can cause downtimes that can lead to costly repairs. Data can be used to examine the digital corners within an industrial enterprise, find out what is working and what isn’t, and then make informed decisions. Big data can be used by industrial decision makers to reduce costs, improve or alter operations, develop effective strategies and identify potential problems before they become serious. Big data can’t be a panacea. It requires planning, diligence, and careful execution.