Industry 4.0 is the fourth industrial revolution, where many sectors experiencing gradual shifts, including manufacturing.
This era of industry allows us to focus more on the increased connectivity and data sharing instead of automating single machines resulting in maximized efficiency, improved integrated systems as well as advanced solutions for the industry.
Integrated systems cannot build themselves. Like their name suggests, integrated systems need several leveraged components to act as coherent functions and build a system.
Therefore, cloud computing, edge computing, artificial intelligence (AI), and other advanced technologies are required.
Why Cloud Computing, Edge Computing, and AI?
Embedded systems play a crucial role in enabling intelligence for Industry 4.0. Wireless communication is also becoming an exponential growing trend for the current industry creating inevitable participation of Internet of Things (IoT) as IoT has a large number of sensors, actuators and mobile devices deployed at the network edge according to Ren et al. (2019).
In this context, the role of sensory devices has evolved. Instead of merely converting signals and transmitting information to microcomputers, databases, or controllers, these devices now process raw data directly at the edge and relay the analyzed results through sensor fusion to other devices (Gazis, 2023).
Considering the abundance of data for the sensors and all other wireless devices, the involvement of Big Data for Industry 4.0 is highly significant.
According to ABI Research, Industrial enterprises globally are generating massive amounts of data, with manufacturing alone projected to produce 4.4 Zettabytes (ZB) annually by 2030, growing from 1.9 ZB in 2023 at a Compound Annual Growth Rate (CAGR) of 11.1%.
This makes cloud computing services are remarkably in demand for the industry as the companies are most likely in insufficient processing power with only local servers.
Cloud computing is able to store, process, and analyze Big Data through IoT and external servers as the provider. From the explanation above we are able to know that while Cloud Computing can store and process the Big Data of an industry, there is still a need for additional improvements.
The convergence of edge computing, artificial intelligence (AI), and cloud computing is the new redefining of the industry with edge computing reduces latency critical operations below 10ms and AI powered cloud systems performing complex analytics.
The leverage is able to create hybrid systems where edge devices are focused more in handling the time-sensitive computations, resulting in the improvement of the overall system’s performance including cloud.
Case Studies of the Cloud Integrations
McDonald’s AI-Driven Operations Overhaul
According to New York Post, McDonald’s is the latest fast food restaurant that is currently implementing AI across 43,000 global locations to optimize the service speed as well as customer and employee experiences.
The upgrade is not mainly focused on the cloud but also into the internet-connected kitchen equipment, AI-enabled drive-throughs, and AI-powered management tools to ensure order accuracy.
The company partnered with Google Cloud in 2023 to enable edge computing in its restaurant. This results in faster and more efficient data processing. This initiative strives to reduce employee stress and increase the loyal customer base from 175 million to 250 million by 2027.
Industrial Equipment Maintenance with ADEPOS
The ADEPOS (Anomaly Detection based Power Saving) scheme integrates edge computing for predictive maintenance in industrial settings.
By detecting anomalies early in a machine’s lifecycle and adjusting computational accuracy, ADEPOS can reduce the constant data transmission to the cloud.
This approach resulted in an 8.8x reduction in neuron usage and achieved energy savings of up to 6.65x, demonstrating the efficiency of combining edge computing with AI for predictive maintenance.
Air France-KLM’s Predictive Aircraft Maintenance
Air France-KLM partnered with Google Cloud to implement AI technologies focusing on enhancing operational efficiency. By analyzing extensive data from their fleet, the airline can predict aircraft maintenance needs more accurately.
This leverage has reduced the time required for data analysis in predictive maintenance from hours to mere minutes, ensuring timely interventions and minimizing flight disruptions.
Benefits of Edge Computing and AI Leverage for Industry
With the sheer volume of generated data every day, edge computing and AI provides various amounts of benefits in industrial settings.
Several case studies have shown that the edge computing for the cloud consistently increases the speed needed for the data transmission due to reduced latency.
By combining it with AI driven systems, industries are able to operate with accurate computational data and depend on predictive maintenance.
Future Challenges
The benefits of having edge computations and AI to be leveraged come with many costs. Limited resource of devices, unpredictable network connectivity, and security can be a hurdle for the projections of cloud integration.
Furthermore, there is a concern in developing and managing the applications suitable for the industry with this new cloud integration.
From an industry perspective, while the number of devices may not be a limitation, the operational costs associated with deploying and maintaining integrated cloud solutions can be substantial.
Balancing the expenses of edge infrastructure, cloud services, and AI capabilities requires strategic planning to achieve cost-effectiveness without compromising performance.
The most important and notable vulnerability is the security. Distributing data processing across edge devices increases the attack surface due to how distributed integrated cloud computing is.
This problem also requires better planning for the future of applicating integrated cloud computing to the industry.
Writer: Nabiila Izzati Zatadini
Student College of Universitas Muhammadiyah Malang
Reference
Bajic, B., Cosic, I., Katalinic, B., Moraca, S., Lazarevic, M., & Rikalovic, A. (2019). Edge Computing vs. Cloud Computing: Challenges and Opportunities in Industry 4.0. In DAAAM Proceedings (pp. 0864–0871). DAAAM International Vienna. https://doi.org/10.2507/30th.daaam.proceedings.120
Bose, S. K., Kar, B., Roy, M., Gopalakrishnan, P. K., & Basu, A. (2019, January). ADEPOS: Anomaly detection based power saving for predictive maintenance using edge computing. In Proceedings of the 24th asia and south pacific design automation conference (pp. 597-602).
Gazis, A. (2023). The advancement of microsensors in the age of IoT and Industry 4.0. Microsensors, 1(1).
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Vifflin, N. (2024, December 4). Google Cloud partners with Air France-KLM on AI technology. Reuters. Retrieved April 4, 2025, from https://www.reuters.com/technology/artificial-intelligence/google-cloud-partners-with-air-france-klm-ai-technology-2024-12-04/
Steinberg, B. (2025, March 6). McDonald’s to employ AI at 43K locations to speed up service: ‘Technology solutions will alleviate the stress’. New York Post. Retrieved April 4, 2025, from https://nypost.com/2025/03/06/lifestyle/mcdonalds-to-employ-ai-at-43k-locations-to-speed-up-service/
Editor: Siti Sajidah El-Zahra
Language: Rahmat Al Kafi
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