The volatile nature of the present day manufacturing processes have led to the use of big data in manufacturing. The fact that its market will reach $9 billion by 2026 is one thing that validates it as a solution. Big data has made the manufacturing industry much more smart and efficient.
In today’s article, we’re going to put forward some big data use cases in the manufacturing industry and a guide on how to start your very own big data journey. But first why don’t you go over these short reads about product life cycle management software and how PCI DSS helps your business.
What’s Big Data?
Big data is large sets of data arranged in millions to billions of rows. In manufacturing, the big data is sets of data collected at, potentially, every stage. This data is later used to optimize operations, improve utilization of the equipment, limiting human-based errors, reducing costs and assuring quality.
Below are some of the popular use cases of engineering management metrics in the manufacturing industry.
Optimization of Production
This big vaccine producer was in need of improving the production of their vaccine and they used big data for it. The company used sensor data to analyze the factors that impacted the output.
The big data solutions identified nine different factors, revealed their interdependencies and how they influenced each other and the yield. They were then adjusted and optimized. This helped improve the production of the vaccines by at least 50%.
Assurance of Quality
Rolls Royce, a luxury automobile and aero-engine manufacturing brand also uses big data. What is interesting is that they use it in modelling their new jet engines. When they are designing the jet engine, they use simulation models and analyze tons of big data to see if the new engine is up to their expectations or not. This allows the company to know beforehand if there is a weakness in the product. The engine won’t go to production stage unless the possible defects have been removed.
Management of Enterprise
A big enterprise needed a better way to manage its raw material so that it could avoid the costs of supply chain failure. The company used data of supplier route, weather and traffic in order to identify the probability delivery delays.
The team responsible for this task used predictive analytics to find out the probable delays and shortages in raw material. Using this information as the basis, the team came up with an emergency plan related to raw material delivery delay. They are now able to keep their production smooth without facing any costly downtimes.
If the examples above moved you to take interest in big data, then you’ll be glad to know that below we share a short road map on how to set off on your own big data journey.
The Right Approach
This is the first thing you should stress on. If you want to extract value from big data then it is very necessary to find the right approach. To achieve the best business-IT alignment, you should:
- Sift through your business strategy
- Talk to your engineering management team and ask them how a certain aspect of quality assurance is going
- Convince the engineering management team to tell the top management that they require big data
- Now that the top management is convinced, determine the cost of big data and take things forward
Adoption of the Big Data Plan
Ambition is good but always start with a simple project. If you are able to successfully complete your first project, your top management will be positively influenced. The following four are the phases a big data project in manufacturing:
- Collect data
- Don’t use complicated analytical algorithms
- Opt for the sophisticated analytical methods
- Automate your production management step by step
Now that your plan is in action, look out for the following challenges:
- Your in-house team might lack technical skills
- Your in-house and the outsourced team will need to work closely to fully understand the potential of big data
- Some of your employees, not accepting of new technologies and methods, might resist big data
There is no doubt that big data brings in money and value to your company. Big data is widely being used for optimization of production in the manufacturing sector. With the right approach and a well thought out plan you can also incorporate big data in your company. But be wary of the execution challenges you may face.