4 Essential Characteristics that Make the Factory of the Future
Amid a global manufacturing slowdown and rising inflation fears, digitalisation is widely recognised as key to surviving new challenges. To achieve higher productivity and greater competitive advantage, manufacturers across the world are accelerating their transition to smart factories.
A Strong Business Case to Develop a Smarter Factory
As Singapore strides into the future of advanced manufacturing, this is a pivotal moment for our manufacturers to adopt the next generation of technology in Industry 4.0. Top supply chain solutions include Artificial Intelligence (AI), Internet of Things (IoT), Robotic Process Automation, Precision Logistics, Predictive Quality, and more.
It has also become increasingly business critical to have zero downtime and defects. For instance, Toyota is using IBM Maximo Health and Predict as an enterprise asset management system to create a smarter, more digital factory. The integration of factory floor equipment with AI and IoT technologies is targeted to reduce downtime by 50%, breakdowns by 70%, and overall maintenance cost by 25%.
So, how do manufacturers interested in developing smarter factories begin to integrate technologies into their production facilities and operations?
We’re pleased to have the following industry experts highlight the four important characteristics of a smart factory.
- Mr Neeraj Gupta, Asset Management Solution Leader, IBM APAC
- Mr Zhiwei Lee, Business Development / Industry 4.0 Transformation Leader, IBM Supply Chain
- Mr David Leong, Head of IoT, S & I Systems Pte Ltd
- Mr Nicholas Lee, 1st Vice Chair, Digital Transformation Chapter, SGTech
- Mr Vejay Kumar, General Manager, SMART Modular Technologies
1. Big Data Analytics
Leveraging data effectively will help generate deeper insights and lead to better business decisions.
AI enables higher competencies in data collection—with the use of advanced sensors, embedded software, and robotics—to weave a more complete understanding of complex value chains.
One aspect of this is applying deep learning algorithms to process unstructured data. For example, companies can use software tools to detect danger zones in a coal mine to improve workplace safety.
As data grows, the volume of information rapidly becomes too much for individuals to react. A cognitive supply chain with intelligent workflows can support faster and better decisions, in a cost-optimised manner.
2. Real-time Visibility of Manufacturing Assets
When it comes to monitoring hundreds and thousands of manufacturing assets, real-time visibility can ensure an up-to-date and accurate overview of operational systems. This results in greater transparency of the supply chain, which is one of the most important factors in the running of shopfloor production.
Performant data solutions that deploy automated transmission, command, analysis, and measurement, enable higher efficiency and traceability which are critical in a fast-changing marketplace.
3. From Preventative to Predictive Maintenance
Production downtime always presents a huge cost risk for businesses. Thus, predictive maintenance is a much more effective measure to avoid equipment failures and unplanned maintenance, compared to preventative maintenance.
Industry experts recommend using AI Machine Learning Models to predict asset failures, as they initiate preventative maintenance before an actual failure event. The system uses data from IoT sensors, builds five common predictive model templates, and scores predictive models with Watson Machine Language to reliably determine asset health.
With more advanced software available today, manufacturers can use data to more accurately predict failures and mitigate risks.
4. Automation and Self-Optimisation of Manufacturing Process
Automation technology is also driving rapid change in the manufacturing landscape and empowering human agents by automating repetitive and monotonous tasks.
Visual algorithms, smart workflows, and robotic tools can aid to reduce manual inspections, enabling real-time detection and correction procedures.
Mr Mark Adams, President and CEO of SMART Modular Technologies, recognises the value of automation, “Our vision of a highly digitised and connected manufacturing operations is possible through factory automation which was instrumental in enabling our operations to be highly productive and efficient with a pool of upskilled workforce to compete in I4.0. This is expected to improve yield in the production floor by 20% and increase throughput by 10%.”
Focusing on People-Centric Technologies
Even in the haste towards digital transformation, many businesses recognise the importance of focusing on people as much as technology.
Investment in people is needed to scale an organisation’s digital efforts. Upskilling and maintaining an agile workforce, for example, is essential in keeping up with new technologies.
After all, engaging people and creating digital cultures is the first step towards advanced manufacturing.
Mr Nicholas Lee, 1st Vice Chair, Digital Transformation Chapter, SGTech concludes, “most importantly, it is putting people at the centre of this transformation, and taking care of people in our digital journey.”
Organisations who are kickstarting or have other needs in your Machine Learning journey may contact S & I Systems Pte Ltd for a complimentary digital transformation consultation. You can reach out to:
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Published Jan 2022