In the truest sense of the word, manufacturing is the engine of the world economy. In fact, the manufacturing industry is one of the most valuable industries to any economy.
For example, the GDP of the two largest manufacturing hubs in the world, China and the USA, had contributions of $4 trillion and $2.3 trillion respectively from the manufacturing industry.
Without manufacturing, we would not be able to access the products and services we love and use. From the cars we drive to the phones we communicate with, we depend on the manufacturing industry to deliver our beloved products to us.
Ever since the industrial revolution transformed how we approach manufacturing, the business world has been looking for new ways to streamline the process even more; leading companies are outsourcing their manufacturing processes to more efficient offshore plants, and manufacturers are automating their production processes to reduce human errors and improve quality.
With the help of these advancements, leading manufacturers across the globe can produce thousands of products with tighter tolerances and minimal human intervention.
The most critical advancement in the manufacturing industry in recent years, however, has been the integration of data analytics into the manufacturing process, which has given manufacturers enhanced production capabilities.
In this post, let’s explore the benefits of data analytics in manufacturing and how it’s improving product quality across all industries.
Traditionally, introducing a product design to the market involved a lot of trial and error. Most often than not, the first iterations of the product design had an underwhelming welcome among consumers due to the less than ideal ergonomics and design.
Today, however, with the power of data analytics, manufacturers can test their designs for efficiency and ergonomics without ever making a physical prototype.
Modern data analytics tools utilise machine learning and artificial intelligence to create computerised product designs and help manufacturers put them through their paces.
In automobile manufacturing, for example, even wind tunnel testing has been moved to systems powered by data analytics.
We need to talk about the rise of automated manufacturing when considering the benefits of data analytics in manufacturing.
Today, in many industries, the manufacturing process involves minimal human interaction—everything from delivering raw materials to quality control of the finished products is executed through advanced algorithms powered by data analytics.
The tight integration of data analytics also allows manufacturers to eliminate issues due to human error. A recent study found that 23% of unplanned idle time in the manufacturing process is due to human error.
With automated manufacturing systems, manufacturers can minimise unplanned downtime by optimising the analytics algorithms to detect potential anomalies in the production process or equipment and conduct proactive maintenance.
Deciding how much to produce is perhaps one of the most critical decisions across the entire production process. Overestimating the demand can lead to overproduction, costing the manufacturer millions of dollars in the process.
Underestimating market demand, on the other hand, can tarnish the reputation of the company due to the untimely delivery of products and services.
Therefore, manufacturers need to estimate their demand accurately to maximise their profit.
Data modelling and predictive analytics use historical demand data and simulate future market conditions to produce accurate demand forecasts for the future, helping manufacturers meet the market demand without wasting their resources.
Today, many industries across the globe are going through the fourth industrial revolution—driven by the power of data analytics and digital infrastructure—and the manufacturing industry is no exception.
The benefits of data analytics in manufacturing enable modern manufacturers to enhance capabilities to deliver more quality and efficiency in the production process.
Businesses need to invest in big data analytics. Processing big data is an immense challenge, which few other tools can do in a timely mannner. On the other hand, tools for big data are explicitly designed for this and can process large amounts of data promptly. Not only are the tools equipped to handle terabytes of data, but the tools also come with several features that lead to higher quality insights, lower costs and better productivity. Here are 6 essential features of analytical tools for big data.
6 features of big data analytics
If corporations are to glean any meaningful insights from this data they must have a data analytics model that processes data without seeing a significant increase in cloud service and hardware costs.
However, data scientists usually build data analytics models by experimenting with smaller data sets. They must scale the model from small to large, which can prove to be a considerable challenge. The ideal big data analytics model should have scalability built into it to make it easier for data scientists to go from small to large.
Some projects may require data scientists to make changes to the parameters of a data analytics model. However, making changes is risky because one change to the parameter can cause the entire system to breakdown. A system breakdown brings the entire project crashing to a halt. Results are prolonged and costs go up because the project is delayed beyond the expected deadline. However, big data analytics tools with version control can prevent this from happening. Version controls are the systems and processes that track different versions of the software.
With version control, it’s much easier to revert to a previous version of a big data analytics model if the system crashes. Thus, reducing delays and keeping the project within budget.
Simple integration process
Big data analytics tools integrate data from different sources like data warehouses, cloud apps and enterprise applications. A lot of time is spent customising the integrations to make sure third-party applications are properly connected, and that data processing is smooth. Analytics tools with a simple integration process can save a lot of time for data scientists allowing them to do more vital tasks such as optimising the data analytics models to generate better results.
Better data exploration
Data exploration is a discovery phase where data scientists ‘explore’ the big data they collected. The purpose is to discover connections buried within the data, understand the context surrounding a business problem and ask better analytical questions. Analytics tools that facilitate the process save a lot of time. The tools allow data scientists to test a hypothesis faster, identify weak data quickly and complete the process with ease. Some analytics tools even come with visualisation capabilities, which makes data exploration even quicker.
Identity management is a system that contains all information connected to hardware, software and any other individual computer. An identity management system is a boon to businesses because it helps with data security and protection. This is because identity management systems can determine who has access to what information, thus restricting access to a handful of computers. Therefore, identity management is vital for keeping information safe.
Reporting capabilities of big data analytics include location-based insights, dashboard management and real-time reporting. These reporting features allow businesses to ‘remain on top’ of their data. Hence, if there are meaningful connections found in data or actionable insights discovered, the company will know about it instantly. Thus, business leaders are in a better position to quick action and handle critical situations in a timely manner.
Business leaders can take action quickly and handle critical situations well. Without reporting features, it would be difficult to understand what is being analysed, what the results are and what the overall progress of the project is.
Big data analytics tools are essential for businesses wanting to make sense of their big data. The tools come with several features that make big data processing much easier to accomplish. However, without the right tools, it’s impossible to process data in a timely manner to get accurate results. Some of these features include better reporting, data exploration, version control, data integration and simple integration.
For more information on big data analytics tools and processes, visit Selerity.