Let's innovate...Digital Engineering & Smart Manufacturing

Digital technologies and manufacturing methods, like digital engineering, are profoundly affecting business, employment, and our approach to various tasks. The wave of digital transformation is significantly reshaping the engineering industry, which has historically progressed quickly. Emerging technologies are accelerating product development, streamlining transportation, and enhancing real-time data management. As the reliance on digital technology increases, engineering is becoming a more data-driven field. Experts assert that it is crucial for businesses to adopt the proper technologies for sustainable growth within the industry now more than ever. Leveraging data and AI can help reshape engineering, manufacturing, infrastructure, and capital projects, leading to more efficient and sustainable operations, increased productivity, improved customer engagement, and overall growth.

But before we proceed, a question that arises in my mind, whether we truly grasp the essence of Digital engineering and manufacturing.

Digital engineering and manufacturing involve blending digital technologies with comprehensive engineering and manufacturing expertise to design, create, maintain, and improve products, services, and assets. This method transforms traditional practices by utilizing innovative technologies such as digital twins, robotics, augmented reality (AR), virtual reality (VR), cloud computing, data analytics, and artificial intelligence (AI). Digital engineering includes a variety of crucial digital processes and capabilities that form the basis for intelligent products. These processes—like data architecture, software design, engineering, and development are geared towards producing more tailored and advanced products and services. Meanwhile, digital manufacturing employs sophisticated technologies to enhance and automate production processes. This approach lays the groundwork for developing a highly efficient, resource-conscious, and flexible manufacturing landscape aimed at increasing productivity, reducing waste, and speeding up development cycles.

This is how Digital engineering and manufacturing appears today from the perspective of statistics.

  • $1B Is the average investment companies will make in manufacturing and supply chain resiliency in coming months.
  • 68% of companies plan to boost investments in multi-skilled workforce over 3 years to enhance agility.
  • 78% of companies will use multiple manufacturing sites in 3 years’ time to boost flexibility, responsiveness and sustainability.
  • $1.6T is the potential revenue growth left on the table annually by companies, as engineering, supply, and production vulnerabilities are exposed by disruptions. 

So, huge opportunity on the table for digital engineering companies to digitize what they make and revolutionize how they make it.

In a continually evolving technological landscape, embracing digital engineering and manufacturing isn’t merely a strategic choice—it’s crucial for effectively responding to disruptions and improving adaptability. These methods offer a competitive edge in navigating the ever-shifting market. Additionally, digital manufacturing goes beyond just enhancing efficiency; it also fosters sustainability by reducing resource consumption to achieve environmental goals. Employing digital tools for predictive maintenance aids in decreasing downtime and minimizing raw material waste. In today’s rapid business climate, developing resilience and sustainability is vital. Looking ahead, investments will predominantly target digital solutions, representing 43% over the next three years, while reshoring and relocation will make up 29%, and automating plant sites and warehouses will account for 21%.

Digital engineering and manufacturing fundamentally utilize a variety of advanced technologies to transform the creation of products, services, and infrastructure initiatives. A crucial element of this strategy is the implementation of digital twins, which serve as vital tools. These digital replicas of physical objects or systems allow for real-time monitoring, analysis, and simulation, ensuring that every facet of the design and manufacturing workflow is meticulously fine-tuned for optimal performance and efficiency. Furthermore, augmented reality (AR) and virtual reality (VR) technologies are intricately integrated into the product design process, enabling designers and engineers to visualize and interact with 3D models, fostering collaborative design efforts and decision-making while reducing errors and accelerating design iterations.

Cloud computing delivers the necessary infrastructure for scalable storage and computing power needed to manage the immense amounts of data produced during design and manufacturing. This technology facilitates smooth collaboration among remote teams while providing real-time access to and updates on crucial information. By employing machine learning and artificial intelligence (AI) algorithms, this data is analyzed to uncover insights, streamline processes, and predict outcomes, thus aiding data-driven decision-making, such as in predictive maintenance. This concept is referred to as the digital thread. The integration of 5G connectivity ensures efficient and instantaneous data exchange in complex and automated manufacturing and construction environments, enhancing communication and coordination among various components of the development process. For example, autonomous robotics applications bring automation and accuracy to manufacturing workflows, reducing human error and increasing overall efficiency.

We now recognize that there are vast opportunities for digital transformation ahead of us. It's time to harness data and AI to revolutionize our engineering, manufacturing, and infrastructure initiatives. This will lead to more efficient and sustainable operations, enhanced productivity, improved customer engagement, and growth. Let's innovate through digital engineering and manufacturing.

  • 76% of companies report difficulties in expanding AI implementation throughout their organizations. So let's Keep up with customer demand through innovation. Enhance our conventional R&D and engineering processes by leveraging data, AI, and the Cloud. Accelerate our market entry and adapt to the constantly evolving needs of our customers using insights from digital threads and digital twins.
  • The global industrial automation market is expected to reach $395 billion by 2029, up from $205.86 billion in 2022. Let's leverage data and AI to achieve comprehensive adaptive automation. Develop our strategy for fully autonomous — and sustainable — operations by integrating data, cloud, and AI. Utilize groundbreaking technologies to turn the factory of the future into a reality.
  • 76% of customers expect more customization and faster fulfillment. Customers are increasingly seeking personalized and interconnected products and services. Transitioning to platform-driven physical products and platform services will help unlock value and shift towards an as-a-service model.
  • 68% of organizations anticipate raising their robotics investment by as much as 5% annually. Let’s improve their manufacturing and warehouse operations by implementing automation and robotics to foster collaboration between humans and machines, tackling labor shortages while enhancing efficiency, sustainability, and productivity.
  • Repairing a failed asset costs 1.8 times more than implementing predictive maintenance. Let's assist them in minimizing asset-related risks by improving transparency. Transform their asset management by leading them to implement a thorough strategy that evolves from a reactive approach to a preventive, predictive, and ultimately prescriptive one.
  • In the next three years, 65% of the manufacturing sector is expected to transition from a product-focused to a service-oriented revenue model. Let's help them establish managed services to operate, automate, and enhance processes with tools designed to improve customer experience while increasing efficiency—thus unlocking new revenue opportunities.

This continues indefinitely. Let's innovate...

***********************
****************
**********


Comments

Post a Comment

Popular posts from this blog

Generative AI & Meta-Leaders

In the wake of advances in generative AI and large language models (LLMs), there is excitement about what the technology means for businesses. But we should not forget that this advancement is also posing a catch-22 situation to the traditional leadership. The transformative potential and rapid acceleration of generative AI are creating an imperative for them to act quickly. They should rethink their skillsets and develop new skillsets like – ability to make better sense of data insights, ability to ask better questions, ability to learn from smart mistakes etc. It is true that ChatGPT has taken the world by storm. In just 5 days since its launch in November last year, it gained one million users, a feat that no other popular online services have managed to achieve. But it is not the only generative AI tool, others include Midjourney and Dall-E for images generation, GitHub Copilot for coding, and even conditional generative adversarial networks (cGAN) such as Pix2Pix, developed by
Read more

Good automation! Select right process for RPA

Someone said very rightly – “Automation is good, so long as you know exactly where to put the machine.” In all my previous blogs I wrote about BOT failure, ROI Modelling, Cost saving, RPA Governance, CoE, POC, POV etc. But then I realized that before taking a deep dive into any of these subjects, first we need to have a right RPA process identification & prioritization methodology in place. Everybody across the industry agrees that Robotic process automation (RPA) can make a big difference for organizations tormented by repetitive routine processes – the kind that suck up the productivity of people whose time would be better put toward more important work. But many are confused by what seems to be a deceivingly simple question — which are the right processes for RPA? The poor choice of process for initial pilot is the leading root cause for failure to meet customers’ expectations. Robotic Process Automation (RPA) doesn’t suit every process and in majority of cases we don’t have
Read more

RPA Exception Handling – Be in control

Companies in every major industry are turning to semiautonomous computers (better known as BOTs) to automate large- and small-scale business processes. This technology replaces human intervention in back-office operations, improving operational efficiency, reducing costs and increasing margins. However, organizations cannot employ this technology effectively unless the BOTs are engineered properly. And one of the most important component of this BOT engineering is Exception handling or in simple words right categorization of the errors and then developing solutions around the same to deal with those errors to meet the expectations of the RPA owner. Occasionally, in the course of its work, an RPA BOT definitely encounter a situation it was not programmed to handle – and, in those cases, it will stall. Those cases can be for foreseeable reasons – security or access constraints – or it can be for unforeseeable reasons – like missing or incorrect data. Increasingly, RPA developers are ab
Read more