Mayekawa 4.0

                Revolutionizing refrigeration with IIoT

                Meet our customer hero

Mayekawa is a manufacturing company with a focus on industrial cooling and heating systems in addition to other food processing machines. It was founded 1924 in Tokyo, and is represented in over 20 countries. Its Brazilian headquarters opened in 1968 and now employs more than 300 people.


  • Revamping compressors to include a digital, service-based component
  • Striving to offer customers a solution to reduce maintenance costs and energy expenditure
  • Aiming to improve compressor efficiency and on-site usage
  • Struggle to decide whether to build or buy an IoT solution


  • Implemented Mayekawa 4.0: Sensorized equipment that captures manufacturing data
  • Launched powerful remote monitoring and predictive maintenance services
  • Connected 170+ devices, with 4.200.000 data transactions per week
  • Created a reliable data source for machine learning to improve compressor efficiency
  • Improved customer experience by offering tangible energy and cost savings

                    “We’ve been able to learn so much about how our compressors actually run on-site, and we’re using that information to make improvements that directly benefit our customers.  With energy efficiency as a top priority for most companies right now, we are able to use our machine-learning platform to identify opportunities that deliver real value.”

– Jair Junior, Automation Projects Manager, Mayekawa

Meeting the digitalization moment

Manufacturing is undergoing a transformation—new competitors using digitalization in their products are putting pressure on traditional manufacturers to change. Packaging equipment together with services and offering individualized aftermarket solutions is the path forward to new revenue streams. Mayekawa recognized early on that its compressor business was well suited to this format, which is why it went to work developing “Mayekawa 4.0” to address the challenges of Industry 4.0, adjust to the changing market, and stay on top of the competition. Here’s how Mayekawa transformed its compressor business from traditional manufacturer to full-service smart rental service provider.

The best of both worlds: Buy AND Build

Mayekawa was looking to develop a ready-made IIoT solution dedicated to its compressors. And it needed to be robust, fully customizable, and simple to use. First, the company tried building its own platform, but that proved too complex—the network of expertise required was overwhelming, requiring cloud, data and field, hardware and device maintenance, software, and networking experts, to list just a few. It was impossible to have all these experts in-house, so outsourcing was unavoidable. And the off-the-shelf solutions on the market didn’t offer the needed flexibility. Then Mayekawa found Cumulocity IoT—an enterprise-grade, simple to use IIoT platform that was low code, user friendly, and scalable. And even better, it took care of all the complicated security and risk management requirements of the project. With this tool at hand, Mayekawa made quick work of developing their dream IoT compressor platform. The best of both worlds: Buy AND Build!

Connected compressors = better customer experience

In the first phase of implementation, Mayekawa outfitted its compressors with sensors to deliver in-depth data on pressure, temperature, vibration, energy use, and other information about the manufacturing process. Then, using Cumulocity IoT Streaming Analytics, it was able to remotely monitor its compressors on-site. These data insights enabled Mayekawa to understand and enhance its own machines’ performance.

The next step was to use this data to unlock predictive maintenance — so that service technicians no longer needed to visit sites on a scheduled monthly basis but could respond on-demand to actual system alerts in real time. Inefficiencies and errors could be spotted much more quickly, meaning less down-time, lower energy use, streamlined processes and reduced operating expenses, increasing customer ROI. These new services enhanced the customer experience and built stronger relationships. Mayekawa used Cumulocity IoT to build a customer platform with centralized access to all forms and contracts, inspired by customer feedback.

“Our customers appreciate the quick response time, and our service providers also find our platform very easy to operate. They often say that Mayekawa 4.0 is the best solution—totally customizable and adapted to the customer’s reality.” Jair Junior, Automation Projects Manager, Mayekawa.

But Mayekawa didn’t stop there—in the next phase, it implemented two new tools from Software AG: Cumulocity IoT Machine Learning and Integration. This combination supercharged the already existing capabilities—consolidating the data, putting it all together on a powerful, robust platform, and using machine learning to optimize compressor operations. Now, the manual step of evaluating all parameters can be performed by smart ML engines, freeing up team capacity. By uncovering hidden patterns in IoT data by analyzing large volumes and applying sophisticated algorithms, Cumulocity IoT Machine Learning delivers improvements and efficiencies that would likely have otherwise been overlooked. Instead, Mayekawa has the potential to revolutionize compressor energy use, one of the biggest cost factors for running the system. And with today’s volatile energy prices, this is a benefit that will hugely impact the bottom line.

Mayekawa 4.0: For a cooler future

Mayekawa 4.0 has put the company firmly in the modern era. The timing couldn’t be better: with the looming energy crisis, energy efficiency and sustainability are more important than ever. Mayekawa has already set up a strong foundation to continue optimizing its IoT connected compressors. There’s more: the new technology is transforming the company from the inside out, improving efficiency not only for its customers but its own internal processes.

“With our new IoT platform boosted by machine learning it’s not just about smarter solutions, it’s also about our own smarter processes. Our team needs to monitor 25 or more clients and our workforce is already overloaded—which is why we use machine learning to continually improve efficiency—for our customers and ourselves. It’s a win-win.” says Jair.

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