Business FunctionsManufacturing

Digital transformation and automation will drive the future of manufacturing in a post Covid world

By Sridhar Dharmarajan, Executive Vice President & Managing Director Hexagon | MSC Software

Ever since the COVID-19 pandemic broke out in March 2020, it became clear that its impact would go far beyond our health. With travel bans, lockdowns, and social distancing norms coming into play, the effects were widespread. There were disruptions to global and local supply chains, manufacturing was hit, and remote working became the new normal. Governments, industries, and common people scrambled to find ways to keep their heads above the water in this challenging time.

The pandemic hit when the labour pool had already been shrinking steadily, both in developed and developing economies. Therefore, it was already established that digitization and automation would define the future of manufacturing. However, COVID-19 has accelerated this trend. Concepts such as digital transformation and automation that were viewed as futuristic suddenly became critical to ensuring business continuity.

In my opinion, the way forward for any industry is to become Autonomous. Autonomous is cognitive automation; essentially the industries should use the data, which is the new oil, generated in the various divisions of the organization and analyze it to take informed decisions at every step of the Design & Manufacturing Process.

Smart Connected Manufacturing

COVID-19 drove home the need to rethink planning and operations to ensure that infrastructure and services can run as efficiently and effectively as possible despite possible disruptions. Digital transformation initiatives can play a vital role in the manufacturing industry’s recovery and renewal by optimizing the manufacturing process.

Integrating the entire manufacturing process into a single digital thread enables manufacturers to work towards achieving a cohesiveness to ensure optimization at each stage. Each part that is manufactured has a perceived quality and performance quality that is decided by design, manufacturing, and quality departments. Therefore design, engineering, manufacturing, and measurement/metrology all must work together to ensure that the final product is manufactured as designed. The whole ideas to ensure that “As Designed, As Manufactured and As Measured” is the same.

It all comes down to the ability to contextualize hidden information by enabling the data to travel from the where it is created to where it needs to be used. This is crucial in order to gain multiple insights in the form of downtime reports, production tracking, etc.

In a connected digital world, every department in an organization are connected. A ”digital twin” of the manufacturing process can allow the entire process to be tested out in a digital environment and before it is replicated it in the physical environment, and then continuously monitor the performance to be in line with the prediction and adjust for variances real-time based on the predictions. This requires us to expand our definition and scope of the smart manufacturing ecosystem to include customers, suppliers, and every department of the organization working remotely to improve the quality of design. Each component of the manufacturing ecosystem needs to be worked on to meet the goal of ”first time right” and “fastest to market”.

The Power of Autonomous Solutions

The principles of Industry 4.0 talk about the integration of machine, edge/cloud computing, advanced visualization, mobility, enterprise integration, and AI/ML.

As the digital and physical worlds converge, it allows for consolidation of data as a result of which each department becomes infinitely smarter. Insights from this data can translate to improved quality, greater productivity, lower costs, and sustainable designs.

As organizations seek to revamp their businesses for the post-COVID world, there is an opportunity to closely examine processes and relook at operations to optimize them for the future. Adding intelligence and taking cognitive learning approaches to the manufacturing process can ensure that there is continuous learning and incremental improvements.

Making each piece intelligent – design and engineering, manufacturing, and quality – and then integrating them can help drive engineering convergence. As processes get more complex, AI can play a key role to manage big data and enable informed decision making or augmented decision making.

This engineering convergence will play an instrumental role in enabling smart mobility solutions as well as smart design and manufacturing, thereby taking us towards a smarter and better future

 

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