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. The 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, enterprises need both savvy and technology. And yes, big data can be one of the technologies to aid manufacturing organizations in their optimization initiatives! This is why companies now turn to big data consulting to formulate their big data adoption strategies. But if you’re not yet convinced that big data can indeed bring value to your organization or if you don’t know how to start, read on to see some exciting use cases for big data in manufacturing and a smart guide on how to start your big data journey.
Use cases
#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.
Sugar-sweet optimization
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, an enterprise needed a better way to manage raw materials delivery. They decided to use their suppliers’ route details as well as weather and traffic data provided by trustworthy external sources 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.
Predictive maintenance
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 on the shop floor. Such predictive maintenance reduces reaction time from 4 hours to 30 seconds and cuts costs. In 2017, thanks to big data and IoT, Intel predicted saving $100 million. This doesn’t look surprising at all: according to the research, predictive maintenance has appeared on companies’ radars exactly in 2017 and has got straight to top 3 big data use cases.
#4. After sales
Hull cleaning
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 analyze in real time 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.
A guide on how to start
Now, after looking through these crashing big data manufacturing use cases, you may be impressed. You may even rush to seize big data’s powers to outrace your rivals. But it’s not all that simple. Before stepping on it and starting your big data adoption project, you need to know the tricks that may be waiting for you. So, here’s a guide to make your big data adoption ‘ride’ as smooth as possible.
Ready… Set…
To prepare for a big data adoption project, you need to find the right approach. Rather than getting obsessed with the idea of big data, rushing to get the budget and then failing to extract value from it, first, you should lay the groundwork for the possible future ‘novelty.’ The following steps aim at that and are characteristic of business-IT alignment. So, let’s look at them from the perspective of improving product quality in an enterprise.
Step 1. After reading enough about the possibilities of big data, look through your business strategy and understand what goals in it can be achieved with big data’s help.
Step 2. As an IT professional, you should get more details on your company’s manufacturing problems and needs. The best way to do it is talking to the engineering management at your factory and asking them how the quality improvement process is going. Chances are, the process is problematic and no solution has yet been found, which is where you – very cautiously, without too much IT slang – explain that such challenges can be solved with a thing called big data analytics.
Step 3. Try to get the consent of the engineering management to prove (if needed) to the company’s top management that they do need big data. And also warn them that their involvement will also be necessary later to help data analysts understand the needed details of the manufacturing process.
Step 4. Determine a certain range of how much a particular big data project costs and talk to your top management about big data adoption and what value it will bring.
Go!
You can’t test big data’s capabilities on complex tasks right at the start. Just like you can’t go to space a few days after deciding to become an astronaut. Manufacturing companies should start out with a simple project (for example, trying to achieve a stable output quality at a vaccine factory). A simple starting project allows you to see how big data can solve your problems with low risks and investments. Which, in its turn, is likely to positively affect your top management’s opinion on big data and encourage them to plan further big data investments (for more serious analytical projects). Whereas an overly complex and high-risk starting project, such as reorganizing the whole production process at the vaccine factory, can forever set them against big data because the project’s high investments can easily disappear without trace.
And if we speak about any big data adoption project more globally, they should always be broken down into ‘digestible’ phases that are to be approached separately. Here, we propose the following phases for your big data adventure:
- Aggregating data.
- Using simple analytical algorithms.
- Turning to more sophisticated analytical methods.
- Incrementally automating your production management.
Aggregating data
Long before any analysis can happen, you have to start aggregating data. In some cases, it’s not a problem at all: you just deploy/add sensors on your manufacturing equipment, prepare data storing facilities and enjoy the flow of ‘freshly-cut’ data.
But in other cases, such as if your production cycle is months- or even years-long, it can prove difficult because you may lack the info on how your production process parameters influence output. And without knowing it, it’s all really a shot in the dark. But don’t get upset: there are ways to fight it. For example, try not to concentrate on the entire manufacturing cycle at once. Rather, focus on one part of your manufacturing process (say, inoculation in cheese production), gather data about it, analyze it and see how you can improve it.
Making analytical baby steps and advancing to big data strides
As your big data solution evolves, you can get different levels of analytics results according to these stages of revealing big data insights in manufacturing:
- At first, you can perform relatively simple big data analysis to make targeted changes in your manufacturing processes (to improve product quality, for instance).
- Then, you can dig your data deeper to find ways to change your business processes. For example, you used to perform reactive maintenance and, with big data, you start preventive maintenance.
- When the time comes, you can even transform your business model, finding a better way to do it through big data analysis (say, you decide to get closer to the customer by making the cars you produce a smart connected product; you deploy sensors on them, analyze data from cars in use and provide after sales services).
At early stages, you’ll only need the most usual analytical methods, such as correlations and regression analysis. And as your big data competences and needs grow, analytical methods become more elaborate. With time, you’ll ‘employ’ predictive analytics and machine learning. And, as you can image, if you find simple correlations helpful, complex analytical methods will make you feel dizzy with new opportunities.
Production management automation
Automation of your production management is probably the most sophisticated way of using big data in manufacturing processes. This is the point where you as a human being are rarely seen on the manufacturing site. The concept of automated production management is fairly simple: your historical and incoming sensor data is analyzed in real time and the control apps send targeted commands to actuators on your equipment.
A good example of production management automation is the case with General Electric’s wind turbines. Sensors provide data on energy generation and wind direction, according to which the blade pitch is changed to optimize the wind turbine’s efficiency.
An example to make it clear
Suppose your company produces baby food and decides to go big data. The first thing to do here is find the needed expertise to guide you through the adoption project (here, reading a lot and hiring big data consultants would be a good choice). And after gaining a deep big data understanding, you hire needed staff and start data aggregation (deploy/add data sensors on your production floor and prepare data storage).
For the sake of the example, let’s imagine that systematically a few times a month your baby food batches substantially drop in quality. Now, the big data staff (together with the engineering technologists) can find out what causes these quality drops. And they realize that your manufacturing process doesn’t allow for the variations in the quality of raw material (baby food ingredients). If the ingredients’ quality is lower, the machinery isn’t ‘tuned’ to get a better quality output (say, you don’t adjust temperature and cooking times). And besides that, they also find a way to cut your production cycle duration. This big data application (better quality assurance) can be a good first project.
Getting valuable insights quickly and cheaply makes your company more interested in further big data capabilities and more complex analytical algorithms. And in a while, your enterprise starts running predictive analytics, equipment wear-out analysis and machine learning. Among other things, it allows you to perform predictive maintenance, which enables the staff to react to alarming trends on the manufacturing floor before any real damage is caused.
And when the time comes to expand globally, your company decides to go with franchising and use your big data powers to assure and control baby food quality across all your franchisees.
Now, survive
As soon as you start real big-data-adoption action, there will be some impediments in the way (say, from the project management side). And that’s why you need to look out for management challenges that big data can bring in manufacturing:
- Lacking in-house technical skills.
As tempting as it is, you shouldn’t completely outsource the whole adoption project. Otherwise, it will be difficult to gain much-needed big data understanding. Moreover, outsourcing completely is not a way out, because – especially at early stages – you’ll need to experiment a lot. And it is simply easier, if your ‘domestic’ people are involved. Which is why it’s only natural to hire new skilled tech employees or retrain old ones.
Before starting some real action, it would be a good idea toturn to big data consulting, since it can ease the hardships of big data projects and contribute to big data understanding. But before you head towards the closest consultant, there’s something you need to know: it’ll only be advantageous, if you organize a knowledge transfer to your tech employees.
- Missing engineering technologists in the team.
Not only developers work with big data. Your tech-team will need to work closely with engineering technologists. Firstly, because techs need to understand your manufacturing processes and technologists can help with it. Secondly, because your technologists themselves can see precious ways to improve production and its management, if they learn general big data opportunities. So, you should make sure your big data team has a sufficient number of skilled engineering technologists.
- Resisting the new technologies.
Some employees – let’s hope the lesser part – will probably resist big data. And there’s nothing personal about it: for creatures of habit, it’s just more convenient to use the old technologies. Training your staff as well as controlling their usage of the new solution can help deal with this challenge.
You are now ready
The manufacturing use cases show that big data can bring big money and big value. They also show that big data is most widely used for production optimization. And it’s quite logical: big data solutions are really good at finding correlations. While production changes based on sensibly selected correlations can improve yield enormously.
To reap those mighty benefits that big data offers and start using big data in your manufacturing organization, you need to carefully plan your actions. So, let’s rehearse them one more time. You should:
– Find the right approach to your big data. Carefully analyze your business needs, find a way to fulfill them with big data and never chase after trends just for fun.
– Prudently plan your big data adoption. Don’t jump to the most difficult part right off the start. Find a small-scale project to test big data on. Aggregate data, test simple algorithms and then try more daring ones.
– Watch for management challenges. Gain a thorough big data understanding, don’t outsource the project completely and engage a needed number of engineering technologists.
–Epilog–
You could have noticed that almost all the examples of using big data on a manufacturing plant feature sensor data. There’s usually so many sensors at factories now that running them, in fact, seems like piloting a space ship with numerous real-time reports and predictive analytics. And to avoid a ship crash, before getting onboard, the captain of your ship can always turn to big data consulting and sail the infinite space with maximum comfort and security.
