Many companies, large and small, are exploring the possibilities of big data in their businesses. Big data is the engine driving the new-age industry revolution 4.0. There are a few challenges that a manufacturing company has to tackle to successfully imbibe Big Data in their company. In this blog post, we will talk about the big data challenges in the manufacturing industry so that you can address and solve them effectively.
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| Big Data in Manufacturing |
Challenges to Consider While Implementing Big Data Strategy in Manufacturing
Big data is improving the process of the manufacturing industry through innovative ways. According to an estimate, Big Data in manufacturing is expected to reach a gargantuan size of $9.6 billion by the year 2026.
Big data is the engine that is driving the new-age industry revolution 4.0. By combining Big Data with IoT and AI, a manufacturing company can reduce costs, improve the efficiency of its supply chain, and optimize the distribution of its products.
Many companies, both big and small, are exploring the possibilities of Big Data in their business.
While Big Data is an opportunity not to be missed, there are a few challenges that a manufacturing firm needs to address to imbibe Big Data successfully in its firm. A premier Big Data consulting company can understand these challenges and can help you in addressing them effectively.
Read Here: Big Data vs Data Science: Key Differences and Similarities
7 Big Data Challenges Faced by Manufacturers
Getting the Team Onboard
Lack of awareness amongst the management and the employees of a manufacturing firm is one of the first hurdles in the journey towards Big Data. Big data is a new technology, and people will take time to adapt. Remember the look on the face of your old-school employees when you decided to use computers on the production floor? Expect the same weird look when you choose to implement Big Data in your manufacturing company.
Don't go for the home run on the first ball itself; instead, we suggest you take things slow. Make small changes and see their effects. After all, you do not want to disturb your production due to implementation issues in Big Data.
Design robust data management processes by taking the inputs of everyone in the team. Assign Big Data change managers whose job will be to monitor and enforce the data processes.
Integration with Prevailing Systems
We all love to innovate, don't we? But before you don that Steve Jobs hat or T-shirt for that matter, understand that you are not going to throw away your old systems anytime soon. Legacy systems are in general, not designed for Big Data, and you will need to incorporate many changes into your legacy systems to make them compatible to handle Big Data.
You will already have a lot of smart systems like PLCs, distributed control systems, production planning, and ERP systems. Integrating Big Data systems efficiently with the prevailing systems will shorten the learning curve as your people already know how to use legacy systems.
Inaccurate Data
Data is the “big” word in Big Data. You cannot expect accurate results with inaccurate datasets. In the manufacturing language, the finished goods will be defective if there is a defect in the raw material.
We would recommend spending resources on acquiring accurate data as even small errors are magnified in the output, sometimes derailing the entire process.
Big Infrastructure Challenges
Everything tends to be Godzilla size with Big Data, and that includes the infrastructure as well. One of the significant challenges that manufacturing organizations face while implementing Big Data is the massive investment in physical infrastructure. You will require expensive software systems like Hadoop and Spark, trained Big Data analysts, and computers with better processing power than your standard office computer systems. The cost of installing a Big Data system on your premise can run into millions of dollars.
To reduce costs, you could use open source Big Data platforms like Google BigQuery. These systems are pay-as-you-use, meaning that you only pay for the amount of server space and the computing power you use. Such systems are substantially cheaper than buying your physical infrastructure.
Organizational Coordination
Lack of organizational coordination is a huge source of frustration for data analysts. Consider this; you have ticked all the right boxes while installing Big Data systems, but you are still not sure why you are not getting the desired results. Data silos may be the culprit here. Effective utilization of the organization's data is not achieved without proper coordination between various departments.
The result of inadequate organizational coordination in terms of data is that people start thinking that maybe "Big Data isn't for us". Understand that Big Data can work only when different departments within your organization exchange data freely with each other; it helps in making accurate inferences.
To ensure smooth data flow, you will need to
- Take all the stakeholders into confidence
- Decide what kind of data to collect
- Chalk out a robust data collection strategy
- Build a communications structure to keep everyone on the same page.
Better coordination will lead to faster results.
Acute Shortage of Big Data Professionals
Rows and rows of numbers crunched by a super-fast computer are of no use if you are unable to derive meaningful insights from them. It is where the role of expert data analysts and data scientists comes.
The supply of good data analysts and data scientists has not kept pace with the demand. The job roles of data scientists and data analysts are multidisciplinary, and it is hard to find such professionals as they need to be good at identifying patterns within data, which is no easy task.
Security Challenges
Unlike our traditional IT systems, the industrial control systems are not that mature in terms of security, thus exposing them to risks from cybercriminals.
IoT devices are connected via gateways that act as a bridge between the internet and the device. If a hacker gets access to these gateways, then it gives the hacker the power to control these devices from anywhere.
How Cognitive Computing and Blockchain Can Help
Provide the nitro boost to your Big Data dreams by leveraging the power of cognitive computing. In case you are wondering what cognitive computing is, then as a simple definition, we can say that cognitive computing is the area of computing wherein we try to achieve human-like performance from computers.
Areas like speech recognition, natural language processing, and computer vision come under the universe of cognitive computing.
Through proper implementation of cognitive computing, we can achieve the following benefits.
- Create new product categories
- Improving the quality of the product
- Deriving value from manufacturing data
- Automating internal processes
- Enhancing worker safety
- Optimization of energy resources
- Efficient floor planning
- Better supply chain management
Blockchain is another promising technology that has the potential of improving the performance of your manufacturing firm. It is the technology behind the famous cryptocurrency, Bitcoin. Blockchain helps in enhancing the transparency of the process as every stakeholder in the project can easily verify the details at any point of time.
You can derive the following benefits using Blockchain.
- Improved tracking of the product in the supply chain
- Better warranty management
- Automatic execution of a contract through smart contracts
Cognitive computing and Blockchain are two technologies that have the potential to give wings to your data science dreams.
Bonus read: Data Science – Challenges and Opportunities
A Final Note
Big Data is the beginning of a new era in manufacturing. The potential benefits of the technology are enormous, and there is no doubt about the fact that your manufacturing company will reap rich rewards upon implementing the technology. But, while thinking about implementing Big Data into your manufacturing system, you should give serious thought to the various challenges and how you plan to address them. While planning, take into account the existing setup of your manufacturing plant and spare a thought about how your employees will react towards change.
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