The experience of big data in manufacturing: 12 real-life use cases
Much earlier than today’s manufacturing giants, Henry Ford came up with a smart ‘move’. He paid a maintenance team at one of his factories not for repairing equipment but for the time spent in a recreation room, when no breakdown occurred. Result: workers were more productive and downtime costs dropped.
But today, even Henry Ford’s genius wouldn’t be enough to optimize manufacturing processes. Now, to stay competitive, you need both savvy and technology. This is why enterprises turn to big data consulting. And here are 12 use cases showing the brilliance of big data in manufacturing from 4 different perspectives.
#1. Production optimization
Extracting process improvement
A vertically integrated precious-metal manufacturer’s ore grade declined. The only logical way to avoid loss was to improve metal extracting and refining processes. Using sensor data, the manufacturer’s big data solution identified what factors influenced output the most. And the dominant parameter turned out to be oxygen level. With this insight, the team slightly changed the leaching process and increased the yield by 3.7%. Thanks to big data analysis, the manufacturer now earns $10-20 million additionally every year. Quite a gain, considering the ore grade deterioration rate was 20%.
Chemical yield perfection
A leading European chemicals manufacturer sought to improve yield. Using sensors, their big data solution analyzed how each input factor influenced production output. It analyzed temperatures, quantities, carbon dioxide flow and coolant pressures and compared their influence rates to one another. As a result, they revealed that carbon dioxide flow rates hugely affect the yield. And by slightly changing the parameters, they achieved a significant decrease in raw materials waste (by 20%) and energy costs (by 15%), and impressively improved the yield.
Vaccine yield improvement
A huge pharmaceutical company needed to find a way to improve the yield of their vaccines. To do that, the company’s big data solution analyzed their equipment sensor data, revealed interdependencies between various production parameters and compared how each of them affected the yield. Then, 9 most crucial parameters were identified, reviewed and adjusted to optimize the manufacturing process. It improved vaccines’ yield by 50%. Now, the company additionally makes $5-10 million a year per one substance.
High humidity levels and low-quality raw materials badly affected the taste of sugar of a large sugar manufacturer. To fight it, they used a big data solution (with a machine learning capability) to analyze sensor data and find correlations between the parameters contributing to the best sugar quality. Using this insight, the manufacturer managed to find a way to quickly influence product quality and achieve a unified sugar standard regardless of external factors. It allowed them to reduce production costs, increase customer satisfaction and simplify workloads.
#2. Quality assurance
Early-stage vehicle quality assurance
As early as 2014, BMW used big data to detect vulnerabilities in their new car prototypes. Data was collected from sensors on the tested prototypes and cars already in use. Due to big data analysis, BMW’s solution (probably integrated with their vehicle design and modelling software) spotted weaknesses and error patterns in the prototypes and in cars already in use. It enabled engineers to remove uncovered vulnerabilities before the prototypes actually went into production and helped reduce recalls of cars already in use. As a result, BMW can not only ensure higher quality at early stages, but also reduce warranty costs, boost brand reputation and probably save lives.
Jet engine design
Rolls-Royce uses big data extensively. And one of their most interesting manufacturing big data experiences is connected with modelling new aircraft engines.
At the design stage, their software (integrated with a big data tool) creates simulations of new jet engines and analyzes terabytes of big data to see whether the new models are any good. This allows the company to find weaknesses before the model gets to production, which reduces defect-related costs and helps design the product of a much higher quality.
#3. Enterprise management
Data-driven enterprise growth
Using big data in manufacturing, companies can tackle global development challenges, such as transferring production to other countries or opening new factories in new locations. Companies’ historical and external data analysis can establish whether it’s still profitable to run factories in current locations or at current scopes by building predictive models and what-if scenarios.
Besides, in the right hands, big data can help explore oceans of unseen opportunity, such as offering new products or even conquering new markets.
Accessible raw materials
To avoid costs connected with supply chain failures, one enterprise needed a better way to manage raw materials delivery. They decided to use their suppliers’ route details and weather data provided by a trustworthy external source to identify the probability of delivery delays. To do that, their big data tool (quite possibly integrated with their MRP) used predictive analytics and calculated possible delays and raw materials shortages. Based on these calculations, the enterprise worked out a supply-related emergency plan and is now able to run their production uninterrupted and avoid excessive downtime costs.
Intel’s factory equipment live-streams IoT-generated data into their big data solution (probably integrated with MES). The analytics solution uses this data for pattern recognition, fault detection and visualization. It allows engineers to see what tendencies require their immediate attention and what actions are needed to prevent serious breakdowns. Such preventive maintenance reduces reaction time from 4 hours to 30 seconds and cuts costs. In 2017, thanks to big data and IoT, Intel predicted $100 million saving.
#4. After sales
As a standard after-sales procedure, a client requested Caterpillar Marine to do an analysis of how hull cleaning impacts fleet performance. Caterpillar’s big data solution (integrated with their Asset Intelligence platform) analyzed data from sensors on ships running with and without cleaned hulls. Then, it found correlations between the client’s hull-cleaning investments and fleet performance. Soon, Caterpillar concluded that their client needed to clean hulls more often (every 6.2 months, not 2 years) and that related investments paid off. As to the manufacturer, big data allowed them to ensure the most efficient exploitation of their products and improve the company’s image.
Wind farm optimization
As a proponent of after-sales with a personalized approach to customers in manufacturing, General Electric helps power producers use big data at 4 levels.
Level 1. Wind turbine’s sensor data analytics enables power producers to optimize turbine’s blade pitch and energy conversion automatically.
Level 2. Wind farm monitoring software compares sensor data to predicted values and recognizes performance patterns, which helps power producers perform preventive maintenance at the farms.
Level 3. Power producers use historical and real-time data to build predictive models, find correlations, detect faults and recognize patterns to optimize the farm’s work.
Level 4. The data is visualized and presented to top management for global-scale informed decision making.
Connected aircraft engines
Besides the designing stage, Rolls-Royce also uses big data to provide after-sales support to their clients and make their aircraft engines a connected and smart product.
At the after-sales stage, Rolls-Royce operational centers in real time analyze tons of data fed from engine sensors and generate insights into their performance. If any defect or alarming tendency is noted, engineers can immediately take necessary actions to avoid catastrophic results.
This approach allows Rolls-Royce to increase their product quality, significantly reduce costs, ensure safe flights and provide high-level services to their clients.
These use cases show that big data can bring big money and big value. They also show that big data in manufacturing is most widely used for production optimization. And it’s quite logical: big data solutions are really good at finding correlations. And production changes based on sensibly selected correlations can improve yield enormously.
Also, you could have noticed that almost all the cases feature sensor data. Now, there’s usually so many sensors at factories that running them, in fact, seems like piloting a space ship with numerous real-time reports and predictive analytics. And to avoid ship crashes, before getting onboard, captains can always turn to big data consulting and sail the infinite space with maximum comfort and security.