The first three industrial revolutions were a result of mechanization, electricity and IT, respectively.
While the mechanization of the textile industry in the 18th century started the first industrial revolution, the mass production of goods due to electricity paved the way for the second industrial revolution. We’re currently in the middle of the third industrial revolution where production has been transformed by digital technologies like 3-d printing.
The fourth industrial revolution – Industry 4.0 will be led by the emergence of smart and connected IoT devices and technologies. And what will further accelerate the transformation is the combined usage of IoT, big data and AI.
Gartner forecasts that 14.2 billion connected things will be in use in 2019, and that the total will reach 25 billion by 2021, producing immense volume of data.
And manufacturers in particular are quick to adopt this technology.
With the rapid emergence of sensors and other connected devices, we can expect a humongous growth in data – both from a volume and velocity stand point. And in order to harness this data and extract business value from it, adopting AI and big data analytics is imperative.
The ROI from IoT, AI and big data technologies is also very compelling for manufacturers. Improved operational efficiency, better quality, streamlined and automated supply chain, rapid response rates are some of the key business benefits.
However, most manufacturing companies today are yet to fully analyze data – even though they are able to seamlessly collect and store a lot of it.
Data in the age of industry 4.0 is generated from multiple diverse sources. These can include:
And many more! These sources continuously generate data in different formats. For instance, data from sensors is structured, operations data can be semi structured and digital data like images, video is unstructured.
Due to the lack of advanced IT systems which can capture, store, analyze and govern this data, most of it goes unused or under leveraged. This is where modern platforms which are equipped with AI and big data analytics can be used to maximize the value of manufacturing data.
Modern-day manufacturing equipment comes equipped with AI sensors that can categorize normal and abnormal machine vibrations. These intelligent systems will immediately alert maintenance technicians whenever abnormal vibrations are identified. These systems also store and learn from data at each point and become ‘smarter’ as time passes. Every time a new scenario is encountered, these AI systems are reprogrammed to add more information to its arsenal.
Modern-day fault prediction models are AI-powered and can study machinery condition, usage, and other factors to forecast when a fault or breakdown may occur. This gives manufacturers considerable time to schedule repairs or plan for replacements, so as to avoid any last-moment surprises.
Preventive maintenance, on the other hand, involves studying data related to the equipment to accurately determine how often it should undergo maintenance. Since periodic maintenance of the machinery is taking place, the likelihood of machine failure is greatly reduced. There are 2 major types of preventive maintenance which are time-based and usage-based.
Imagine a scenario wherein you have manufactured too much or too less of a product. In both cases you will incur losses due to poor inventory management. In order to align your rate of manufacturing with customer demand, you need to be able to accurately forecast demand. Manufacturers today are hence investing in online inventory management software that can help you align your production with the needs of the supply chain. The software analyzes demand data continuously while mapping it against factors such as holidays, weather data, special occasions etc. to gain an insight into what number of product units to manufacture, and when. Regulating stocks and supply also helps balance the supplier-manufacturer dynamic.
Semiconductor manufacturer Infineon Technologies reduced product failures by comparing information about product quality between the beginning and end of the production process. They correlated single-chip data captured in the testing phase at the end of the production process with process data collected in the wafer status phase earlier in the process. Thereby they identified patterns that helped eliminate faulty chips early in the production process and improve production quality.
When manufacturing and selling a specific product, great pains are taken to decide on a selling price. Multiple criteria such as the cost of the raw materials, production, logistics etc are taken into consideration before deciding on a price to quote with the customer. However, there is always a fair chance that the customer may not be satisfied with the price, leading to low sales of the product. Hence, in order to come up with optimal pricing that is both profitable to the manufacturer while also being fair to the buyer, many companies today are using price optimization analytics. These tools analyze various kinds of data including customer expectations, competitor pricing and inflation rate before suggesting the best possible price that can increase your profit efficiently.
Whenever a customer claims a product warranty, detailed feedback on why the product is being returned/exchanged, is collected by the manufacturer. This information gives us an insight into what the problems with the product are – these could be related to the product design, quality or even the reliability of the item. Instead of just treating this information as feedback, manufacturers must analyze the data to come up with ways to improve the product to meet customer expectations. Today we have warranty analytics software that helps manufacturers to just that – analyze large volumes of warranty-related data from multiple channels and to apply this insight into product improvements.
By analyzing big data, industries can gain insight into customer expectations and demand. One such application is in designing and developing better products by analyzing customer data from multiple sources like social media, surveys, and systems pertaining to customer service, sales and marketing.
For example, by studying how customers react to a certain product design, such as a bottle, industries can gain an understanding on what is the customer trend in terms of size of bottles preferred, shape, colour etc and make tweaks to the product to satisfy the customer. This ensures that for each product that is developed, customer opinion is taken into consideration from very early on. Since products are then designed to meet specific demand, the risks involved in introducing a new product in the market is drastically reduced.
Processing customer feedback data helps design better marketing strategies right from the initial idea generation stage. Hence, we can develop products that are better-suited to the customer’s needs and that have a higher probability of success in the market.
By using data analytics for machine floor optimization, you can reduce wear and tear, maintenance breakdowns and overall operating costs of machines.
Let’s say on a shop floor there are 10 machines producing the same product, with each machine having either varying production capacity or are in different wear and tear conditions. Using IoT sensor data and running big data analytics, you can get recommendations on how many products are to be produced on which machine and improve your production plan and reduce machine wear and tear.
Oil rigs especially the ones in deep seas are extremely expensive and require high maintenance. And one the worst reasons that causes unplanned downtime is unexpected maintenance activities due to equipment failures.
Even though most modern rigs today are enabled with IoT devices, the data being generated is hardly being used for preventive and prescriptive maintenance.
By analyzing the data from these devices using data analytics, rig managers can understand the condition of equipment, how the tool’s usage over a course of time has changed its wear and tear. Thereby, the company can predict the risk of equipment failure in advance. Rig managers can also know how much more a sparingly-used tool can be used. These insights can significantly reduce downtime and costs and prevent accidents.
Gartner estimates that by 2020, there will be 21 billion IoT devices around the world. About 6 percent of these devices will be used by the industry and manufacturing sector. With the expansion of IIot, industries must ensure the security of their data stores and warehouses. Additionally, industries must find ways to use smart data techniques to predict unexpected wastes, detect errors etc.
Big data and analytics can give industries a competitive edge and can help them meet their business goals while bringing in time and cost efficiency. Insights gained from analyzing big data helps us understand consumer expectations, market trends and demand better. This knowledge is crucial to align production to meet consumer needs. Intelligent systems also make our industrial machinery much safer and error-free.
If you’re looking to leverage big data analytics for your organizations or have any questions regarding this topic, please feel free to get in touch with one of analytics and database experts for a personalized consultation. You may also be interested in checking out Acuvate’s business intelligence and analytics solutions for further insights.