In this special guest feature, Piyush Jain, Founder and CEO of Simpalm, discusses the many ways in which Big Data has positively influenced the manufacturing industry. Simpalm is a mobile app development company in the USA. Piyush founded Simpalm in 2009 and has grown it to be a leading mobile and web development company in the DMV area. With a Ph.D. from Johns Hopkins and a strong background in technology and entrepreneurship, he understands how to solve problems using technology. Under his leadership, Simpalm has delivered 300+ mobile apps and web solutions to clients in startups, enterprises and the federal sector.
We have seen multiple waves of the industrial revolution in the last 200 years. With the current fourth industrial revolution, all things are getting connected to the internet including machinery and equipment. These machines (i.e. plants, hardware sensors, CCTV cameras, robotic machinery etc.) produce a large amount of industrial data, which is valuable. This data is different from the internet big data generated by social media, blogs and other sources. Industrial big data is used by managers, decision-makers, policymakers to improve the process, machines and predict future needs. Predictive maintenance and real-time monitoring can be done with industrial big data, probability of failures can be detected, and maintenance cost can be reduced. For example, in chemical plants, getting insights about the fluid/gas flows in pipes can be helpful to predict the maintenance timing. Big data strategy is being rapidly adopted by the manufacturing industry to increase efficiency and productivity. Advanced analytics helps decode complex manufacturing processes, it replaces human-made decisions with automated algorithms and makes production more efficient and fast.
What is Big Data?
Big Data refers to the huge volume of data that can not be stored and processed using the traditional approach within the given time frame. Being able to utilize this huge amount of data can be beneficial for industries, there are big data technologies available to leverage the processing of such a large amount of data. For example, Hadoop is a framework designed to store and process data in a distributed data processing environment with commodity hardware with a simple programming model. It can store and analyse data present in different machines with high speed and low cost. There are other technologies available such as MongoDB, Rainstor, Hunk and more.
The BMW Group relies on the intelligent use of production data for efficient processes and premium quality, it is the best example of generating value from big data. Building a car generates a massive amount of data throughout the value chain. The BMW Group uses its smart data analytics digitalization cluster to analyze this data selectively and enhance its production system. Results from intelligent data analysis make an effective contribution towards improving quality in all areas of production and logistics.
Big Data has positively influenced the manufacturing industry in many ways
- Process improvement leads to increased yields and more efficient manufacturing.
- Methods such as “neural-network techniques” and “Machine Learning” compare the impacts of various production factors.
- Improvements in supply chain management have led to improved delivery time and less risk.
- Personalised production enables firms to cater to individualized or specific needs and more.
Major Challenges Faced in Implementation
Identifying the Need
The big data strategy is all about gathering the information and using them to transform the way a business operates. A manufacturing firm carries out many processes in production, it is crucial to understand the need for big data strategy for improvement of a specific process. It is certainly required to start with identifying what problem is needed to be solved otherwise, we can go on a mindless exploration of a big mountain of data and hope that eventually we find something in there. Most of the time, manufacturing firms spend a significant amount of time and resources in capturing the random data and processing them for a result, it does not bring any benefit in most of the cases. It is a challenge to identify the actual need and gather data that can help you to achieve the objective. For example, if a firm needs to address the stock loss issue, it is required for the firm to gather all the data produced while warehousing and storage for further execution.
Selection of Data
There is a tremendous amount of data that is generated internally such as customer transaction data, internal supply chain data and lots of performance data across the firm. Handling these data alone is sometimes a challenge for many firms, but that’s not where all values can be created, it is very important to understand what other data sources are available. For example, we can bring external data into the play such as weather and climate data, traffic pattern data, price comparative data to understand what other prices are being offered in the market. It is a challenge to determine which data to use, how to source it, how to get it together into an integrated form that can be used across the firm.
Transformation Capabilities
The toughest part of big data implementation is the transformation capabilities, it is important understanding the real impact of data often requires a lot of strategies, team efforts and time spent. Getting people with the right skills who have the capabilities to use the latest mathematical techniques and the latest statistical methodology to work with data and bring benefits. It is required to recruit people who have been operating in the same way for many years in the industry. Creating an efficient team of skilled professionals is a real change management challenge. In many cases, companies take the existing people and train them in new methods, processes and skills, it is required to supplement the existing team with the people with experience in a different environment.
Wrapping up
As the data is growing, manufacturing firms are applying analytics to get significant value with better speed and efficiency. However, after spending millions in data analytics, companies are still not able to see the benefits due to the challenges that remain unaddressed. These challenges occur at all the levels of implementation, like capturing the right data, processing it fast and analyzing it. Also, big data lacks emotional intelligence, companies have to also figure out ways to make an emotional impact with big data.
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