With the high rate of adoption of sensors and connected devices, there has been a high increase in the number of data points generated in the manufacturing industry. The term Big Data refers to increasingly complex, massive data stores that can’t be effectively processed using traditional methods. In the manufacturing sector, Big Data involves collecting information from a variety of sources, including machine sensor data, quality assurance information, data from suppliers, production output, maintenance, financial information, and basically any other measurable process that goes into modern manufacturing.
Manufacturers collect such data for a reason: this Big Data can be processed and refined into business insights that can help massive financial growth, customer retention, savings on maintenance, warehousing, and unexpected downtime, among other things. Using the power of Big Data and manufacturing analytics, manufacturers are able to add value, efficiency, and productivity to their businesses while knowing that the moves they make are calculated and based on accurate data. This increases not only the likelihood of success, but also confidence in the ideas that are implemented. According to some reports, the global Big Data industry in the manufacturing industry was worth $3.22 billion in 2018 and is projected to reach $9.11 billion by 2026, exhibiting a CAGR of 14.0% during the forecast period.
Why is the use of data growing in the manufacturing industry?
To make increasingly complex decisions and gain deeper insights, modern day manufacturers are relying more and more on data from a variety of sources. As more data is gathered from the shop floor and transformed into usable reports, data-driven decisions can be made that were simply not possible before.
Another reason the use of Big Data is growing in the manufacturing industry is because it’s easier to access. The barrier to entry for implementing Industrial Internet of Things (IIoT) devices and smart factory equipment is at a historical low. Manufacturers can easily and affordably measure many aspects of their business, both in terms of data capture and data warehousing & storage. Also, modern markets push manufacturers toward the use of Big Data in order to stay reliable, efficient, and relevant to their target consumers, while remaining competitive in the marketplace. Big Data is allowing manufacturers to take the next step in their journey of continuous improvement. Additionally, the benefits of data in the manufacturing industry range from several preventive level advantages to aiding in predictive decisions.
Greater competitive edge
The manufacturing sector has been at the centre of technological innovations. Whether it is mobile connectivity, industrial IoT, or next-generation hardware, the data that is generated through all the different mediums helps raise competitiveness to the next level. Big Data provides greater insights into market trends, a better understanding of customer needs, and future trend forecasts. In a nutshell, it provides everything that gives manufacturing houses a massive competitive edge.
Hardware downtime can be a major productivity hazard in the manufacturing sector. It doesn’t just hamper employees’ time but also requires a lot of maintenance and troubleshooting. Now, the solution that the manufacturing industry has found for the issue is using industrial data analysis to perform preventive and predictive maintenance on their hardware. It also assists manufacturers in keeping track of hardware quality assessment by analysing their efficiency and daily work.
Greater customer services
The manufacturing industry is now utilising advanced sensors to provide big-data powered alerts to field technicians regarding maintenance requirements, as well as Radio Frequency Identification (RFID) tags to monitor the condition of units and data-driven reports that offer accurate suggestions for improving customer service.
Supply chain management
Big Data analytics in manufacturing enables manufacturers to track the location of their products. This ability to track down the location of a product using new age technologies such as radio frequency transmission devices and barcode scanners solves the problem of products becoming lost or difficult to trace.What this means for customers is that businesses are able to give them a more accurate delivery timeline.
One of the key productivity indicators for a manufacturing industry is determining what the market needs and what volume of goods they need to create. Earlier, when Big Data in manufacturing did not exist, businesses relied on human estimates that led to goods being produced either in excess or in shortage. Also, it helps with giving businesses important predictive insights that help them make the right choice.
Agile response to fluctuations in market demand
The incorporation of real-time manufacturing analytics, specifically in the Customer Relationship Manager (CRM) system, can help the manufacturing industry forecast the future in real-time. The analysis of data can showcase the difference in order and consumption patterns that can be used to drive the adjustment in production. Also, the Big Data-driven intelligence gathered from the CRM data can help with knowing what the customers are asking for and then preparing the production cycle in a way that the time to respond is minimised.
Speeding up the assembly
With Big Data analytics in manufacturing, businesses have the capability of segmenting their production and identifying the units that get manufactured faster. Big Data helps the manufacturing industry know where to focus its efforts to get maximum production. It also helps manufacturing houses identify the areas they are most efficient in, along with the ones they need to work on.
Identification of hidden risk in process
The analysis of data around the equipment’s past failures enables the manufacturing houses to forecast its lifecycle and set up the correct predictive maintenance schedules, which are either usage based or time based. It also assists in detecting gaps, reducing waste and downtime, and assisting businesses in developing a recovery plan in the event of an unexpected failure. Additionally, Big Data when combined with Artificial Intelligence (AI), enables the manufacturers to automate the processes so that they self-optimise without human intervention.
Product customisation is feasible
Manufacturing houses have focused on producing at scale and left customisation to enterprises serving the concentrated market. Data analysis for manufacturing makes customisation possible at the manufacturing process by predicting its demand and then giving the manufacturing houses enough lead time to produce customised products at scale. Additionally, using Big Data, manufacturers are able to streamline their manufacturing processes by eliminating waste and predicting demand. This streamlining helps them with the time they require to do mass personalisation of the products.
Improvement of yield and throughput
Big Data technology helps manufacturing houses find hidden patterns in their processes, enabling them to pursue their continuous improvement initiatives with greater certainty. The result of this can be seen in a rise in production.
The price point of a product can be decided with the help of Big Data. The technology can collect and analyse data from multiple stakeholders, like customers, suppliers, etc, to determine the best price point that suits both customers and businesses.
A manufacturing house can find a range of image recognition specific use cases for Big Data analytics. Let’s see an example: suppose you require a specific spare part, but you don’t know what it’s called or how much it costs. A Big Data powered image recognition software can help businesses capture the image and give the details back to the manufacturers.
Improved customer service
The ability to analyse customer data at every stage of their journey, from marketing to sales to reviews on social media, means that customers are able to receive top-notch, data-driven service that addresses their real wants, needs, and concerns.
Cloud technology will make Big Data more accessible
One of the major advantages of cloud computing is that it allows people to access applications from anywhere. Big Data will most likely become more pervasive throughout the business in the coming years and will no longer be the domain of specialists. Each manager and other nonmanagerial employees, will be assumed to be capable of working with Big Data, just as most knowledge workers today are supposed to have basic computer knowledge to gather data. A study of large data sets will be a requirement for almost every business decision, much as a simple cost/benefit analysis is for manufacturing houses.
Metadata system will be smarter
The organised information that comprises Big Data about the properties of other details is called metadata. This permits huge data measures to be restricted, mixed, captured, and also handled in the distribution and across several data stocks. As the entire process is secure, data is increasingly available and can likewise be used for future undertakings. This is one of the upcoming Big Data trends that will lead to automated metadata processing in the manufacturing sector. These will be gradually designed with AI to allow versatile, dynamic, and fast data systems.
Big Data will continue to grow
In the coming years, there will be a lot of regulation and monitoring of data usage in the public and private sectors. Big Data will continue to grow depending on market projections. This will affect the way companies and organisations look at business information. The companies should be eager to strengthen their efforts to adjust their business operations. Moreover, it has saved operators time, as machining no longer needs to sort the scrap parts, allowing both them and the machines to be focused on producing good parts and generating revenue for the company. Furthermore, manufacturers use Big Data to keep factory floor workers on track through the use of visible statistics that update in real-time. With this, workers are able to understand where they stand in relation to production goals, as well as react quickly to any problems on the shop floor, such as a downtime event.