Just like every other industry, manufacturing is getting smarter. Most companies now use wireless technologies and sensors to log information throughout the process of building a product. The captured information range from properties of the material to vibrations and temperatures to the customer details and logistics data.
Smart manufacturing can transform the industry by making it more sustainable, profitable, and efficient. For example, cutting the distance between frequently interacting machine elements can reduce power consumption cost and improve the overall speed of the process. Big data analytics can also be used to mitigate the risks associated with manufacturing like delayed deliveries caused by bad weather.
However, big data has a long way to go when it comes to improving the production efficiencies in manufacturing. Top industries like aircraft, semiconductor manufacturing and computing still face challenges when it comes to the implementation of data. Many companies are still clueless about what to do with the data at hand, or how to analyze it to produce actionable insights.
Although the positive impact that the proper implementation of big data can bring about in manufacturing is undisputable, the industry is not prepared for this change. Data science is barely included in engineering or business degrees, which makes it a challenge for the existing workforce to implement a data-backed production model. Most companies do not know what data to capture and when or how to measure it. Data is often stored counter-intuitively in different databases, making it difficult to bring it together for analyses. Companies in the manufacturing sector are also skeptical about investing in newer and less explored ideas.

Collect the right data, the right way

Most companies fail in this part of the big data journey. While some companies think their databases are too large to be analyzed, some get discouraged by the organizational and legal aspects of collecting and using data. When it comes to big data, quality is more important than quantity. Noisy or skewed data will do no good. The first step should be to define the frequency and storage duration of data. Huge volumes of data created by rapid measurements would cost more to host, although the preservation of long-term data is critical for modelling. It is also important to determine averaging periods for different data points. For example, machine vibrations must be averaged on milliseconds while temperatures can go up to 10 minutes or longer. Protocols should also be formed for the security, privacy and protection of data.

Innovation gaps

Most of the information out there on big data is catering to the corporate world. Since manufacturers need straight to the point practical guidance, academic papers that push cutting-edge technology like deep learning and artificial intelligence without proper guidelines on how to apply them fails to provide value to the manufacturing companies. Companies need to know what types of data to be captured, what sensors to use and which stages in the production process to implement them. There should be further research for determining the best practices in the configuration of sensors and their installation.
Below are some steps you can take to bridge the innovation gaps.

1. Develop predictive models

Manufacturers need to know if a new product would meet the customer expectations before they can start mass manufacturing it. A home appliance maker for example, should analyze the current and past sales trends, behavioral data, and any other information available on the performance of the product to improve it further. This will help them better their product or come up with a different one to meet consumer requirements. When making changes in the production line, companies want to be assured that these changes will not negatively impact the quality of their product. The confidence gained from successful predictive models can help them overcome this fear.

2. Put strategies into action

The manufacturing system must evolve to be smarter as information is turned into insights. It all starts with installing sensors to monitor existing equipment for possible inefficiencies and shortcomings. As the overall quality improves over time, more sensors can be added to the system to make further fine tuning. The automobile manufacturing industry for example, has improved the quality of chassis by continuously tracking and optimizing the process settings.

3. Fine tune the predictive model

The predictive models that you use, should be able to factor in uncertainties like wear and tear of the machinery or errors in data occurring from streaming bottlenecks or faulty sensors. This becomes all the more important if the products being manufactured demand high precision like medical equipment or implants. Maintaining the accuracy of data and analytics is of utmost importance for ensuring that the data backed decisions are reasonable.

Kickstarting smart manufacturing

Identifying problems and coming up with the ideal solutions should be the first step in moving towards a smarter operational model. Here are some actionable ideas that can help in kickstarting the change.

1. Create networks to discuss problems

Online forums should be set up for discussing, developing and publishing specifications of upcoming industrial problems. Integration of manufacturing processes with services, optimizing production lines, etc. are good topics to start with. With extensive networks where industry specialists, technologists and data scientists can come together to share problems and solutions, the pace of development can increase by several folds. Although many companies already have internal forums for collecting and exploring ideas, the scope of these are limited and this needs to change.

2. Setting up platforms for sharing, modelling and innovation

As with everything, technology is becoming more complex as time passes. This has created a gap between people who can solve problems using data modelling skills and the ones who understand the industrial needs.  Physical or virtual spaces should be arranged by consortia including academia, government and industry, where experts can interact and develop data models to address the challenges. This will also mean that the parties involved must overcome trust issues and hesitation to share internal information. Such collaborative spaces should entertain transparency to help facilitate innovation. It is also important that SMEs are included since such companies are likely to have fresh ideas and willingness to take newer routes.

3. Policies to support smart manufacturing

Although companies would work towards smarter manufacturing practices based on big data, there should be adequate help and support from government since the risk is very high in some areas as far as private investment is concerned. Apart from helping drive the change by involving in the process, government should also offer financial aid or tax credit to the companies adopting smart manufacturing by updating their equipment. Such investment can go a long way and deliver long term benefits to the industries and economy as a whole.

Conclusion

Since boosting efficiencies of manufacturing operations can have a significant positive impact on the bottom line of companies, adopting a data-backed operational structure should be given high priority without further delay. The combined efforts of technologists, industrial experts and data scientists can bring about the innovation needed to make manufacturing smarter.
Jacob is a tech blogger who is passionate about newer innovations in technology like big data and the Internet of things. He manages content marketing at PromptCloud– A big data company focusing on web scraping solutions. You can follow him on Twitter @jacobpkoshy.