How Analytics in Quality can improve the bottom-line

The manufacturing business is a fickle one. A large amount of resources is on the line and uncertainty is an all-time high. When the growth is still low, the manufacturers are forced to squeeze their every asset for the greatest value.

Sometimes it works, most times it does not. Yet something has to be done to improve productivity and profitability without investing more.

As they say, modern problems require modern solutions. In recent times, many manufacturers came up with a different strategy and it involved targeting their own quality management data. It’s nothing new of course, businesses have been studying data and making decisions since ages. Yet something has changed in the way the data is being processed now. The manufacturing businesses have merged big data with machine learning and Artificial Intelligence (AI) and that seems to be making all the difference.

The question is, does it really work?

How analytics in the quality department can improve the bottom-line of a manufacturing company?

Let’s dig deeper and figure out how data and intelligence work together to help manufacturing companies achieve their targets.

Understanding the new era of Business Analytics fueled by Intelligence

We always think of the future as the time when we’ll be surrounded by flying cars and speaking robots. Look around now, pay attention and you’ll realize that you are already living in the future.

Manufacturing business intelligence is rapidly growing, now it’s only a matter of time until the rest of us follow suit. In fact, 46% of manufacturers believe that implementing data analytics is no longer optional.

This evolvement in data analytics is allowing manufacturing businesses to gain greater visibility across their entire manufacturing process. From shop floor to top floor, everything is more refined, error-free, efficient and giving results.  And it can only be achieved by valuing stats, figures, and records.

Big or small, every manufacturing business is housing a wealth of data. As a matter of fact, manufacturing businesses run on it. From raw materials to orchestrating orders, from production to delivery, manufacturing is extremely data-intensive.

As constructive as this data is, it is still scattered, structured, semi-structured and unstructured. Bigger the organization, the unrulier the data. This makes analytics difficult and inaccurate.

This is where business intelligence and machine learning comes in. It organizes the data, puts them into a common system of record, and hands off the leash to the manufacturers. From there on, analyzing specific data at a specific time by specific personnel is a piece of cake. No error or fault goes missing in the quality department and the business success is guaranteed.

Improvement in Quality brought forth by analytics in the Manufacturing Business

  1. Keeping an Eye On the Supply Chain

Manufacturers know when there’s a problem in the manufacturing process, but they don’t know where. As a result, they end up directing all their focus and energy on improving other aspects of the process.

For reasons unbeknownst to us, the supply chain part of manufacturing often goes ignored, despite being a part that can be the root cause of many problems. Faulty suppliers are galore, or ones that charge a cent more than the others.

Little faults and a few cents doesn’t mean much, but little drops of water make a mighty ocean. A minor fault in one raw material can result in the mass production of products of subpar quality.

Data analytics makes it possible to keep an eye on manufacturing from top to bottom. You can see where the problem has occurred and what decisions can be made to undo that damage.

  1. Detecting the cause of Product Variation

Like we mentioned before, manufacturing is fickle and unpredictable. Without data analytics backing up the manufacturing process, it’s likely that an entire manufacturing business can collapse due to inconsistent quality.

One such way in which analytics prevent that is by detecting the cause of variations in the manufactured goods.

When a specific product is manufactured through an identical process, but in two different batches, the yield variation can range from 50% – 100%.

Variability at this huge range can lead to quality and capacity issues. Once the products are released in the market, it can be besieged by industry regulators for inspection, and from there it can only get worse.

Targeted data analytics avoid that by pointing out the cause of variables. Armed with that information, manufacturers make haste to change their processes and eliminate all possibilities of variations. With that, they are able to maintain consistent quality of their products throughout all the different batches.

  1. Removing the Probability of fault in production Before the Production

A little issue in the manufacturing process can lead to mass production of defected products. Regardless to say, the losses can be spiraling.

Analytics have changed that by allowing companies to develop systems that can constantly gauge performances and detect repairs beforehand.

BMW Groups is responsible for showing the manufacturers how to use data analytics to prevent future costs and quality defects. The company gathers data points from all the manufacturing outlets during the prototype production.

Before giving a green signal for the full production, the company tests the prototype cars, detects faults using data analytics and proceeds to fix it before mass production.

Not only does this maintain the high-end quality of the end product, but also saves the manufacturers from the repair cost following the production.

  1. Testing Products Without Testing Them

The best way to assure quality is to test each and every product before it reaches the consumers. Possible as it is, it is extremely time and cost consuming, given the fact that manufacturing is done in thousands.

And yet it has become possible to assure the quality of every single product without spending much time and cost on it, all thanks to predictive analytics. In this method, data is analyzed from the beginning of the manufacturing process until the finishing of the final product. If there’s any possibility of defected product, the data will notify.

This cuts down on the cost of testing, as well as the number of tests required to perform. At the same time, faultless quality of the product is ensured.

On a large scale as well as small scale manufacturing, a quality defect can easily slip notice and lead to catastrophic consequences. On the contrary, detecting and preventing possible defects and ensuring high-quality products can transform even a startup business into a market leader. And all of this is possible and within easy reach with analytics. Analyzing data in the quality department with modern tools and intelligence can help keep an eye on the most granular areas and allow the manufacturing businesses to improve their bottom-line.


Omnisys Solutions helps companies leverage the data they already have to get tangible dollar benefits. With a focus on business value, Omnisys helps its customers make the most of their technology investments and define the right roadmap for them.

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