Success with AI depends on how much value businesses can extract from data. Not just with AI, data can absolutely make or break businesses. It is therefore imperative for organisations to adopt a data-first approach – having decision-making from infrastructure to talent acquisition all ladder up to support the data value chain.
AI technologies help businesses make more informed decisions at each stage in the production process in real time, enabling them to rectify issues as soon as they arise and improve the quality of output. In addition, human Subject Matter Experts work together with the AI platform to monitor its decisions and provide corrections to help the system learn and become more effective. Further improving itself, AI platforms also deploy deep learning to build and refine their own rules to spot potential errors or defects – self-improving on a constant basis to strengthen its production capabilities.
With the ability to help manufacturers use data to optimize processes, AI is key to manufacturing success in this digitally-driven age of Industry 4.0 . Its potential to bring new levels of scale, customer service, decision quality and operational efficiency to processes is more significant than ever today.
Jeff Nygaard, EVP & Head of Operations, Products and Technology, Seagate Technology in a candid conversation with ETCIO, shares key imperatives in the journey towards AI-enabled manufacturing and what they’ve learned from our smart manufacturing journey at Seagate:
Build a foundation for machine learning : A solid foundation of automation and data-enabled connectivity, with different tools generating data that can be analysed, is key for successful machine learning. These tools must then be connected to an edge computing database that supports the need for reduced latency and real-time processing. Manufacturers must have the right smart machines and data collection points to extract sufficient insight to help create optimized operations.
Robust IT infrastructure: IT infrastructure must be customised to support the use of AI and the edge devices that enable real-time data gathering, analysis and use. It’s important that enterprises build on their core cloud computing infrastructure to include hardware and software platforms that gather and manage the huge amounts of data flow from automation. This will enable them to better support the requirements of real-time data processing and make smarter decisions instantly that drive up productivity, efficiency and customer satisfaction.
Address the skills shortage: Employees must be retrained and reskilled to be effective in the changing manufacturing environment. A key driver of success in this new economy is data agility – the ability to extract insights from data fast and efficiently. Organisations will have to build a talent base that’s adept with using big data to make decisions, whether sourcing new talent with the right skills or retraining and reskilling employees. Seagate is preparing for this reality through Citizen Data Scientist training programme, enabling employees to build competencies required for machine learning and AI, and apply them in their respective roles.
Long-term planning is crucial for success: The journey to automation and AI is a long-term strategy. Reaching automation alone often takes at least 5-10 years, as it often involves product redesigns to make products more automation friendly and to allow for data gathering. As this progresses, increasingly sophisticated data feeds enable integrated demand and supply chain planning. This in turn improves overall production, business planning and profitability.
Change management : Ultimately, change management is important since employees are often nervous about new technology, especially AI. Managers must address fears about job loss and concerns about AI being abused to “watch over their shoulders”. The most effective implementation of new technology also require you to adjust or redesign existing processes.
“One way to get people comfortable with working with machines more closely is to humanize the machines. Autonomous carts in Seagate’s factories ferrying material between areas – these carts have faces and names on them. We even have a robot in one of our facilities that roams the office area handing out snacks. People see these robots, and are starting to say hello to them and call them by name,” adds Nygaard, emphasising that these small gestures help employees become more comfortable interacting with machines in roles, never seen before.