Cloud Computing News #5: AI, IoT and cloud in manufacturing
This week we focus on how AI, IoT and cloud computing are transforming manufacturing.
Manufacturing.net lists 10 ways cloud computing will drive manufacturing growth during this year:
- Quality gains greater value company-wide when a cloud-based application is used to track, analyse and report quality status by center and product.
- Manufacturing cycle times are accelerated through the greater insights available with cloud-based manufacturing intelligence systems.
- Insights into overall equipment effectiveness (OEE) get stronger using cloud-based platforms to capture, track and analyse the health of the equipment.
- Automating compliance and reporting saves valuable time.
- Real-time tracking and traceability become easier to achieve with cloud based applications.
- APIs let help scale manufacturing strategies faster than ever.
- Cloud-based systems enable higher supply chain performance.
- Order cycle times and rework are reduced.
- Integrating teams’ functions increases new product introduction success.
- Perfect order performance is tracked across multiple production centers for the first time.
According to CIO Review, the challenge with machine learning in manufacturing is not always just the machines. Machine learning in IoT has focused on optimizing at the machine level but now it’s time for manufacturers, to unlock the true potential of machine learning, start looking at network-wide efficiency.
By opening up the entire network’s worth of data to these network-based algorithms we can unlock an endless amount of previously unattainable opportunities:
- With the move to network-based machine learning algorithms, engineers will have the ability to determine the optimal workflow based on the next stage of the manufacturing process.
- Machine-learning algorithms can reduce labor costs and improve the work-life balance of plant employees.
- Manufacturers will be able to more effectively move to a multi-modal facility production model where the capacity of each plant is optimized to increase the efficiency of the entire network.
- By sharing data across the network, manufacturing plants can optimize capacity.
- In the future, the algorithms will be able to provide the ability to schedule for purpose to optimize cost and delivery and to meet the demand.
According to Global Manufacturing, IoT offers manufacturers many potential benefits in product innovation, but it also brings challenges, particularly around the increased dependency on software:
- Compliance: Manufacturers developing IoT-based products must demonstrate compliance due to critical safety and security demands. In order to do this, development organisations must be able to trace and even have an audit trail for all the changes involved in a product lifecycle.
- Diversity and number of contributors, who may be spread across different locations or time-zones and working with different platforms or systems. Similarly, over-the-air updates, also exacerbate the need for control and managing complex dependency issues at scale and over long periods of time.
- Need to balance speed to market, innovation and flexibility, against the need for reliability, software quality and compliance, all in an environment that is more complex and involving many more components.
Because of these challenges, increasing number of manufacturing companies revise how they approach development projects. More of them are moving away from traditional processes like Waterfall, towards Agile, Continuous Delivery and DevOps or hybrids of more than one. These new ways of working also help empower internal teams, while simultaneously providing the rigour and control that management requires.
In addition to new methodology, this change requires the right supporting tools. Many existing tools may no longer be fit for purpose, though equally many have also evolved to meet the specific requirements of IoT. Building the right foundation of tools, methodologies and corporate thinking in place is essential to success.
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