The following topics will be discussed in this post.
- Improve Processes
- Meet Customer Expectations
- Advanced Forecasting
- Better Use of Automation
- Better Product Quality and Lower Costs
- More Competitiveness
Not many industries have been impacted by the big data revolution as much as manufacturing has. Data mining and collecting has always been a central part of manufacturing, but new tools and techniques have made collecting, compiling, and analyzing data much easier.
Companies can also process much larger data sets nowadays and apply all sorts of predictive models to make decisions based on them. Let's take a look at a few reasons why data is becoming increasingly important in the manufacturing sector.
One of the ways in which manufacturing companies can use data is to monitor and improve their processes. They can use agile tools and sensor technology to gauge performance at every step of a process.
They can tell exactly who was responsible for operating a certain station and whether they were underperforming. Furthermore, they can also tell if a particular machine is being overworked and whether it needs to be upgraded.
A company that knows how to use data can quickly identify bottlenecks and make the adjustments necessary. This gives them a significant competitive edge and allows them to remove waste from their process.
Companies can also use methods like Statistical Process Control and SPC software to predict how processes will perform by sampling them in real-time instead of performing a complete process audit.
This allows companies to make changes immediately, remove human error, and reduce costs. If you want to know more about Statistical Process Control and how to use it in your organization, we suggest you check out this link: https://www.hertzler.com/spc-software-for-statistical-process-control/.
Meet Customer Expectations
Data can also be used by manufacturing companies to improve the quality of their products and meet the expectations of their customers better. They can use data gathered from their sales and marketing team and identify pain points with customers.
This data can then be transferred to research and development, who can now find ways to improve current products or future iterations of it. Alternatively, it can be used to develop brand-new products.
Being able to forecast demand and other factors like production costs is extremely important in manufacturing. Accountants can take data and analyze it to have a rough idea of how much demand there will be at specific quarters.
Data can also be used to predict the availability and cost of certain materials, which will then help find out how production costs will fluctuate. Companies can then use this data to inform their pricing strategy.
A manufacturing company that knows how to use data correctly is much less likely to get caught off guard by market conditions. They can make preemptive moves like locking down the price of commodities in an expectation of a rise.
They will also be able to predict how certain decisions and events may affect the price of certain materials or commodities in the future.
Data can also help manufacturing companies manage their inventory better. By using historical data and predictive models, companies can buy just enough material for production and produce just enough units of certain products.
This means fewer useless products being stored.
Better Use of Automation
All manufacturing companies rely on automation, but some use it more efficiently than others, and it's often because they use data better. Data mining and analysis allow them to know whether a machine would allow them to increase their production and at what cost.
They can quickly evaluate how long it will take for a machine to give a return on investment, and if it would be better to have a human employee performing the task.
They can also use data for maintenance and asset management. Companies who know how to use data correctly can actually predict when a machine is likely to fail and when it will need to be replaced.
Big Data analysis has been shown to help reduce health and stall issues in production through integration with machinery.
You even have machines today that can predict when they might need servicing, autocorrect themselves to prevent breakdowns, and predict potential flaws and gaps.
Better Product Quality and Lower Costs
Innate data mining and analysis can also be used to test the quality of products as they move through the production live in real-time. This is a major game-changer and helps companies save a lot of money.
Faulty products don’t have to make it to the end of the line, which reduces waste. Innate data mining technology also reduces the number of tests that have to be carried out. And since these tests are performed by machines, they are inherently more reliable than tests performed by humans.
Companies can also make modifications to their processes and see exactly how it's affecting quality through the use of data. They can then decide if the effects on quality are major enough for them to scrap processes that would allow them to increase production or reduce overall costs.
Companies that don't know how to use big data will eventually fall behind. 67% of companies asked in a recent survey said that they were able to increase their competitive edge thanks to big data analysis. And it's easy to see why when we see how much of an impact it can have on the improvement of processes and overall costs.
Not only that, but companies that understand big data just understand the industry and global economy much better. They are less likely to overspend on materials or marketing. They are also less likely to invest in machines, tools, and solutions that will not benefit their bottom line.
Not only that, but they have a clear view of their margins at all times and how their decisions affect them. This is a major advantage to have in a sector that is becoming increasingly difficult to predict and navigate.
These are all reasons why all manufacturing companies need to make big data collection, analysis, and implementation a priority. You need to get the right tools to collect this data and put it to the right use while having clear plans for constant improvement.