Much has been spoken about India’s push towards becoming a manufacturing hub, thanks to the production-linked incentive (PLI) schemes launched by the union government. However, even if manufacturing output is growing in certain segments, most of it is linked to exports while others are associated with assembly line manufacturing that is low on skill.
Of course, this is not to say that the PLI scheme hasn’t bolstered Indian manufacturing. It sure has, based on what the November 2023 numbers show – these schemes attracted over Rs.1 lakh crore by way of investments and posted sales of Rs.8.6 lakh crore, while creating 6.78 lakh jobs, either directly or indirectly.
However, for Indian manufacturing to truly come of age, industry (especially highly capital intensive ones such as autos, steel etc. need to implement digital best practices to bring about actual shop floor transformation. One such idea is the use of digital twinning as a means to rapidly scaling capacity, enhancing resilience and driving better operational efficiency.
The era of digital twinning is here
According to a Mckinsey report, senior executives in industry listed out material and labor constraints caused by rising costs and talent gaps as challenges that kept them awake. There was also a need to improve production visibility through better demand forecasting, inventory processes, manufacturing flexibility and real-time visibility of the shop floor.
“Factory digital twins are becoming a highly sought-after technology to solve these problems,” the survey found. Across industries, 86% of respondents said a digital twin was applicable to their organization. Some 44% said they have already implemented a digital twin, while 15% were planning to deploy one, says the report.
According to the authors of the report, in the fast-paced, continuous operations, factory digital twins or real-time virtual representation of the factory, provides the business leaders with the ability to support faster, smarter and more cost-effective decision-making.
“They can deepen manufacturers’ understanding of complex physical systems and production operations, optimize production scheduling, or simulate “what-if” scenarios to understand the impact of new product introductions, the report notes.
How does digital twinning work?
By providing a comprehensive model of the factory floor, the digital twinning model simulates the outcomes of real-time factory conditions, providing what-if analyses across production scenarios such as process or layout changes. These can also be integrated into real-time decision making in production scheduling with manual reviews or full automation.
Digital twinning cases vary around the operational context of the factories whereby in the early phases of investment and build of a premises, a digital twin can validate layout design, optimize the footprint and estimate inventory size. They can also evaluate spatial parameters for assets such as clearances, ergonomics and employee movement in specific locations.
However, as the operations get more structured, digital twinning can predict production bottlenecks as opposed to spreadsheet-based analytics. Hard-to-predict stochastic processes, inventory buffers, material travel times, and changeovers can all be modeled with high fidelity using live data, the report says.
How does one get started on this journey?
The report noted that most executives surveyed perceive a use case in their organization for digital twins. However, many accept that they aren’t yet ready to implement the process, highlighting challenges related to fragmented and arcane data landscapes, paucity of scalable solutions and lack of in-house talent.
“Overcoming these obstacles starts with adopting an iterative, agile way of working based on continuous testing, validation, and refinement of algorithmic logic. This approach helps increase the digital twin’s accuracy prior to deployment—raising the odds of long-term adoption,” it says.
External support may be needed to fill talent gaps. To design and build a digital twin, one large manufacturer partnered with a cross-functional product team of industrial or manufacturing engineers, operations managers, data engineers or scientists, and IT architects to connect data sources, trial a minimum viable product, and build a scalable solution.