MQTT: The Path to Smart Manufacturing Sustainability

Summary

Smart manufacturing benefits such as downtime reduction of 30-50%, throughput increase of 10-30%, and forecast accuracy increases of up to 85% are being realized as a result of adopting Industry 4.0 technologies like the Industrial Internet of Things (IIoT), artificial intelligence (AI), digital twins, digital threads, augmented reality (AR), virtual reality (VR), according to a 2022 McKinsey study. Helped by Industry 4.0 technologies and the best practices advocated by smart manufacturing, manufacturing industries are being transformed back into an economic powerhouse.  

A key aspect of Smart manufacturing is having an enterprise data augmentation strategy that enables real-time bidirectional communication between the various systems powered by MQTT, paving way towards energy optimization and sustainability. 

How MQTT can help improve energy usage and promote sustainability in manufacturing 

MQTT is a lightweight messaging protocol designed for efficient communication in Industrial IoT (Internet of Things) and smart manufacturing systems. It is an integral part of Smart Manufacturing. It has become the de facto standard for communicating industrial data from on-premise to enterprise or cloud due to various advantages that it provides in optimizing energy usage and promoting sustainability in smart manufacturing.  


Here are some of the advantages: 

Efficient communication packet size and message payloads 

MQTT was created as a very efficient pub sub data communication protocol, which is event-based. The message packet size is only up to 200KB, which helps minimize the amount of data exchanged between industrial devices, systems, applications and the broker, reducing energy consumption. Using MQTT, devices, systems, and applications only receive relevant information, minimizing unnecessary data transfer. This also helps optimize the bandwidth and help reduce costs of operation. MQTT also allows the message payloads to be optimized by using efficient data serialization formats like JSON and protocol buffers to reduce network bandwidth usage and energy consumption. 

Quality of Service (QoS) levels, sleep modes and edge processing  

MQTT provides the flexibility to select the appropriate Quality of Service (QoS) level for message transmission, based on the criticality of the data. This empowers users to optimize their data transmission strategy, ensuring efficiency and reliability. Higher QoS levels ensure message delivery but may result in increased energy consumption. Also given the async nature of MQTT, devices can implement sleep modes during idle periods to conserve energy. Devices can wake up based on MQTT triggers when there is relevant data to exchange. In addition to MQTT clients, local brokers allow much of the data to be processed at the edge, before sending it up to the enterprise broker, reducing the amount of data transferred over the network, thus saving energy. 

Device configuration, management, monitoring and reporting 

Remote device configuration and management can be implemented using MQTT data to optimize device settings, update firmware, and apply energy-efficient parameters. In addition, monitoring systems can be implemented to track energy usage and sustainability metrics. Reports and exception alerts can be created based on predefined thresholds to identify areas for improvement. 

Renewable energy integration and system optimization  

With MQTT, energy consumption and production could be monitored in real-time to optimize the use of renewable energy sources. The data can be used, for instance, to adjust manufacturing processes based on the availability of green energy. Also, MQTT can be used to regularly review and optimize the movement of manufacturing data based on changing requirements, technology advancements, and energy-saving opportunities. 

Predictive maintenance and advanced analytics  

With real-time data movement powered by MQTT, predictive maintenance can be implemented to monitor equipment health. The result is reduced downtime, improved efficiency, and the prevention of energy waste associated with faulty machinery. In addition, advanced data analytics and machine learning models powered by Generative AI can be implemented using MQTT data to provide insights into energy usage patterns, enabling the implementation of proactive energy-saving measures. 

Standardization, interoperability and continuous optimization 

By ensuring that devices, systems, and applications in the smart manufacturing environment adhere to MQTT messaging standards for data interoperability, manufacturers can create a more flexible and scalable ecosystem. Also by regularly reviewing and modifying the MQTT implementation based on changing manufacturing requirements, manufacturers ensure that they are optimizing their systems and future-proofing their investments. 

Combining people, process and technology powered by MQTT to achieve goals 

By creating data movement strategies powered by MQTT, the right smart organizational structure to take advantage of it, and the processes to remove impediments, manufacturers can create a more energy-efficient and sustainable smart manufacturing ecosystem. The key is to integrate the data strategy based on MQTT into an overarching manufacturing strategy that considers the unique requirements of the manufacturing environment and continually seeks opportunities for improvement. 

Check out HiveMQ’s Enterprise MQTT offerings and try the platform for free.

_{This feature originally appeared in the May Sustainability issue of AUTOMATION 2024.}