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Summary
- The majority of manufacturers (83%), regardless of their geography or business domain, already have a robust cloud strategy in place. In fact, cloud-enabled services are expected to make up almost 50% of all enterprise-level software usage among industrial companies by 2023. The top five manufacturing sub-sectors relying on the cloud include heavy machinery (92%), automotive/OEMs (87%), industrial & assembly (87%), automotive suppliers (81%), and chemicals (81%).
- In fact, cloud-enabled services are expected to make up almost 50% of all enterprise-level software usage among industrial companies by 2023.
- The majority of manufacturers (83%), regardless of their geography or business domain, already have a robust cloud strategy in place. In fact, cloud-enabled services are expected to make up almost 50% of all enterprise-level software usage among industrial companies by 2023. The top five manufacturing sub-sectors relying on the cloud include heavy machinery (92%), automotive/OEMs (87%), industrial & assembly (87%), automotive suppliers (81%), and chemicals (81%).
In the past, traditional business intelligence (BI) tools reported on trends and offered sales projections from data about events that was weeks or months old. That’s the equivalent of figuring out how to drive by looking at the rear-view mirror. The industry is now getting to the point where data lakes gather information within a 24-hour window via extract-transform-load (ETL) processes and allow organizations to run nightly models and come up with business insights that can be deployed to operational systems the next day.
As a next frontier, new real-time streaming systems are allowing businesses to act on new insight within seconds, providing opportunities for applications that personalize user experience, compute dynamic pricing, or make intelligent sourcing decisions in true real time.
Think of the value of real-time systems in terms of three different operations, as illustrated in the figure below:
- How long does it take to respond to a specific event given a complete model?
- How long does it take to run the model?
- How long does it take to “learn” and adopt the model?
The value of these operations increases as we add real-time capabilities to each level as we move down the list.
- How long does it take to respond to a specific event given a complete model?
- How long does it take to run the model?
- How long does it take to “learn” and adopt the model?
The value of these operations increases as we add real-time capabilities to each level as we move down the list.
H2. Business value of cloud-native applications in manufacturing
The majority of manufacturers (83%), regardless of their geography or business domain, already have a robust cloud strategy in place. In fact, cloud-enabled services are expected to make up almost 50% of all enterprise-level software usage among industrial companies by 2023. The top five manufacturing sub-sectors relying on the cloud include heavy machinery (92%), automotive/OEMs (87%), industrial & assembly (87%), automotive suppliers (81%), and chemicals (81%).
Now that cloud adoption is on the rise and business leaders continue to embrace and optimize strategies for digital transformation, cloud migration is no longer a matter of “whether” or “not”. These days it’s the question of maximizing the value of cloud-native solutions that is high on the agenda.
In the long term, more capital-intensive approaches are also possible. New water infrastructure, such as dams and desalination plants, is expensive but sometimes necessary.
According to McKinsey, 95% of the cloud’s value potential is in business-related functions. At the same time, most industrial companies (59%) misplace their focus and concentrate solely on IT optimization. Digital transformation, however, is not merely an IT project – it requires a complete overhaul of the manufacturing ecosystem from application architecture to production operations to the entire development life cycle.
H3. Building more agile smart factories
Until recently, deployment of digitally-powered factories remained elusive due to the limitations in tech capabilities. The development of smart manufacturing analytics platforms enabled industrial companies to align their people, assets, and operations to optimize production, mitigate risks, and augment human intelligence with AI.
Modern cloud-based manufacturing platforms utilize cognitive analytics to interpret, comply with, and learn from the information gathered from connected machines. An Analytics Platform leverages that real-time historical data to scale and flex with current business needs. The embedded agile flexibility, therefore, allows applications to tune the equipment to adapt to the schedule and product changes with minimal disruptions.
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Contact usH4. Making every data point accessible and actionable
Data is the lifeblood of digital manufacturing transformation, and industrial companies have ample sources to drive insights – from machines, sensors, and programmable logic controllers to different points in the supply chain. However, 68% of data available to enterprises go unleveraged, either because it is unusable, its quality and security are not sufficient, or there are no protocols for proper storage of the collected information.
A comprehensive analytics platform affords manufacturers the ability to connect and manage data assets, sharing the insights across an entire organization. Instead of siloed systems that fail to unlock the real value of data, they get an efficient cloud infrastructure that becomes a central repository for all the information extracted from disparate technologies, equipment, and assets in a factory. That will generate insights and predictions that can boost product quality, process safety, manufacturing-line efficiency, as well as production systems’ economic and environmental resilience.
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GenAI
implementation partner for Google Cloud
7+
of the 25 largest US retailers leverage our digital engagement services
X+
Fortune 500 enterprises co-innovate with us
7+
of the 25 largest US retailers leverage our digital engagement services
20+
best-in-class digital engagement solution providers partner with us
$10B+
digital revenue improvements achieved
H4. Monitoring product quality in real-time
H4. Monitoring product quality in real-time
In traditional manufacturing, product quality is usually measured at a single control point leaving a huge gap in production flow monitoring. It is quite burdensome to identify the exact human, machine, or environmental causes of quality issues in the complex, multi-staged assembly process.
Product quality analytics, an integral feature of the smart factory, can self-optimize performance across a manufacturing ecosystem by relating in-line sensor data to production process parameters and machine settings. Based on the inputs received from the field devices, IoT cloud platforms can predict and detect quality defect trends sooner, allowing for early-stage intervention. Automated quality control, therefore, not only leads to a deeper understanding of specific process situations but also enables manufacturers to take action proactively.
According to Deloitte, deployment of pre-configured cloud-based process monitoring applications could lower scrap rates and lead times, while improving yield and fill rates. This results in a 30% increase in product quality.
H2. An end-to-end data analytics platform for smart manufacturing
The rising awareness of and higher demands for data have influenced how the concept of an analytics platform evolved over time. The journey to data-centricity started with fairly simple enterprise data warehouses (EDWs), which were superseded by next-gen systems called data lakes that later matured into modern cloud-native analytics platforms, such as the ones we build for leading manufacturers.
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