Manufacturing today is changing rapidly, no thanks to intensifying global competition, growing customer expectations, rising cost of resources, and, of course, Covid-19 induced difficulties in manpower and supply chain management. Also, newer business models with anything as a service, and the gig economy, add to these challenges. To borrow a quote from Charles Dickens, “It is the best of times, it is the worst of times.” In this context, for the manufacturing industry, it is as much a time for rapid innovation and growth, as it is challenging to go about it.
On the demand side, the market puts a lot of pressure on product teams to continuously add more functionalities to products to meet expanding customer needs. On the supply side, incorporating these new features calls for newer technologies, and the skills to leverage those technologies, which current product teams might lack. Yet on the management side, fiscal pressures on profitability are inhibiting the provision of additional manpower or budget to support these ever increasing and rapidly changing demands.
So, what do product teams do? The answer lies in technology, or more specifically, data-driven product development decision making technology that can be quickly and affordably integrated with manufacturers’ current processes for smart product development. Fast advancing AI and IoT technology have made this a possibility in the last two years.
IoT and AI are – Harbingers of the Next Wave of Manufacturing
The confluence of IoT and AI can make a game-changing impact on many aspects of business – starting with Robotic Process Automation (RPA) to automate reporting and/or financial decision making, validate and inform sales and marketing planning, vastly improve customer experience, and specifically for manufacturing − enable smarter product development, efficient production, predictive maintenance and lifespan forecasts.
There’s a global evolution towards “smart connected everything”. But while many manufacturers have a connected product vision, few have all the pieces in the puzzle to quickly and securely develop scalable software to convert their products from disconnected to connected, and smart.
IoT and AI stands to be harnessed. Yet given today’s complex manufacturing operations, manufacturers need sophisticated yet easy-to-use integrated platform tools which can help customers realise more value, without manufacturers themselves having to spend time, effort and money on the infrastructure on which it runs.
For example, they will need reliable and secure device communication technology; edge compute and orchestration platforms to decide if, how, and when to transfer what data between products and the cloud to reduce response latency; and data storage, stream processing, real-time data visualisation and machine learning (ML) tools to make the connected product operate efficiently and maximise end-user benefit. Another important factor is scalability – they need to be able to seamlessly extend the infrastructure from a few to millions of devices.
Taking IoT/AI applications up to factory scale poses three big challenges for organizations − there are too many tools to stitch seamlessly together; the hundred or even thousands of product models in production make scaling up incredibly complex; and the AI ecosystem is constantly changing.
The need for manufacturers to have access to a cohesive open architecture enterprise ecosystem − which connects data from the edge to augmented analytics and end-user applications − is real.
Business Benefits Await
Only when such an ecosystem exist can manufacturers effectively collaborate, generate and share data-driven insights, develop AI-augmented analytics, and create scalable, secure IoT/analytics applications faster and with smaller teams. It also helps realise Digital Twins – both data driven and physics driven.
Impactful IoT-AI-data analytics use cases exist across different industries
In manufacturing, it can be used to predict final quality early in the process, reduce machine downtime through predictive maintenance, and monitor assets in real-time.
In product development, it can be used to build web and mobile applications for end-users, inform future product requirements, and develop, deploy and update code to products over the air.
In financial services, it can be used to automate credit approvals, automatically detect fraudulent transactions, and optimise collections outreach and process.
In sales and marketing, it can automate customer segmentation, prioritise sales prospects, and identify cross-sell/upsell opportunities.
In these times of technology convergence and digital transformation, manufacturers should take an open-architecture approach to combining data analytics & AI, computer-aided engineering, and high-performance computing (HPC), to create innovative and sustainable products for long-term business competitiveness.
Sudhir Padaki is the Director, Data & Analytics at Altair Asia Pacific
Serba Dinamik, an engineering company specializing in operations and maintenance (O&M), engineering, procurement, construction and commissioning (EPCC), worked with Altair to develop a Smart Predictive Maintenance Data System (SPMDS). Maintenance crews use Panopticon-powered dashboards built into SPMDS to monitor every sensor mounted on operating turbines in real time. AI models built with Altiar Knowledge Studio identify potential failures or issues that require engineering attention, and, based on that understanding, take turbines offline only when necessary.
“SPMDS is critically important to our operations. We know in advance when a turbine needs maintenance and we can plan accordingly in advance. This reduces our unplanned downtime and is helping us eliminate unexpected failures.”
– Mohd Azam Mat Nawi, General Manager, Serba Dinamik
“Implementation of the SPMDS is contributing to a new era in company asset management system that will help us increase revenue.”
– Ahmad Badri bin Rozlan, Director, Serba Dinamik