How To Improve Manufacturing Productivity With Predictive Analytics

Mukund Rao

When you’re in manufacturing, time is money. Factors that cause your production process to go offline cost profitability. But how do you optimize uptime without risking machine breakdowns or malfunctions? One way to get around the problem is through predictive analytics. Used for a wide range of purposes, the applications for this type of technology are exceptional for improving production and even product design. But how important is it to improving profitability? Today’s IoT industrial sector is worth $11 trillion. Using predictive analytics is estimated to save manufacturers $630 billion by 2031. Here’s an overview of how adding this capability improves uptime and profitability.

There are two areas where auto manufacturers can improve their company using predictive analytics. The first involves adding predictive analytics to the production line itself, limiting downtime and improving output and profitability. The second involves using analytics to improve the products themselves, improving price support and customer satisfaction. Let’s take a quick look at both options.

Improve production line productivity

When an automotive company purchases industrial machinery, that machinery will be in service for a long time. In fact, the industry average is over two decades. Over that time period, there’s a lot of opportunity for something to go wrong. When the production line stops, profitability goes down. In the past, a manufacturer had very few options to ensure reliability. It could choose a brand with a reputation for excellent reliability and hope for the best. Otherwise, it could stick to a tight maintenance schedule that may not provide optimum line uptime and productivity.

Fortunately, there’s a better way to go about production line maintenance today. Predictive analytics uses a combination of IoT, cloud, and analytic technology to monitor machine conditions. When certain conditions match up to part or machine failures, the analytics use that data to predict future failures. That way, when a particular condition precedes a larger failure, the manufacturer can make a small repair with minimal downtime for the assembly line. This technology can be included in new machinery or retrofitted into existing machinery. By doing so, you can see an immediate improvement in unexpected downtime.

But what about regularly required maintenance? If your factory’s conditions are optimal to allow the machinery to last longer between servicing, you can lessen the amount of downtime required. But when is the best time to undertake this maintenance? What if you could use regular shifts in demand to your advantage? If you need to perform extensive maintenance, scheduling it during a downtime is a great way to avoid production delays. Predictive analytics can look at past purchasing behavior and find the best times for scheduling this type of maintenance.

Provide better quality control

But manufacturing line uptime isn’t the only area where predictive analytics can improve your bottom line. Many businesses have begun using this technology to improve their product’s quality control. In addition, these same analytic systems can be used to develop service recommendations for customers. This allows the best possible blend of reliability and post-sale revenue streams. At the same time, it provides an opportunity to improve the company’s reputation.

In today’s industry, the time from product development to market has drastically accelerated. This means that the time spent in product development and quality testing is minimized. By adding predictive analytics to the process, high quality and accelerated production can go hand in hand. Flaws in design can be quickly weeded out and changes to specs made to ensure better quality in the finished product. In addition, sensors in the production machinery allow for better monitoring of material quality, freeing up the staff to only focus on exception handling of out-of-spec problems.

When it comes to cars, few brands are as recognized for luxury as Mercedes-Benz. Known for its well-engineered, hand-built engines, the German manufacturer decided to add predictive analytics to its production in 2013. The company recognized it had an opportunity to add predictive analytics capabilities to its engine testing process. This allows the engines to be tested more quickly, speeding up production. At the same time, it still provides more in-depth insights than prior testing had delivered.

Following the success of this project, Mercedes-Benz and other manufacturers have begun to explore the additional opportunities available through predictive analytics. Among options that are being considered is to broaden the engine development process and analytics to improve collaboration across projects. It also provides better business transparency. With the increasing number of options and models being made available, predictive analytics helps manufacturers determine optimal combinations and potential interactions before production begins.

By adding predictive analytic capabilities to your production line, you can improve uptime, output, and profitability. But how do you go about putting this capability in place in a way that works for your company?

Learn how to bring new technologies and services together to power digital transformation: download The IoT Imperative for Discrete Manufacturers: Automotive, Aerospace and Defense, High Tech, and Industrial Machinery. Explore how to bring Industry 4.0 insights into your business today: read Industry 4.0: What’s Next?


Mukund Rao

About Mukund Rao

Mukund Rao is Director of the Automotive Business Unit at SAP. He has been a key contributor to the business unit for over 18 years, focusing on both OEMs and suppliers. Mukund earned his MBA from University of Michigan and M.S. degree in Mechanical Engineering from Oklahoma State University.