Digital manufacturing technologies connect systems and processes in all areas of production to create an integrated approach to manufacturing, from design to production and maintenance of end products using an integrated computer-based system consisting of simulation, 3D visualization, analysis, and collaboration tools to simultaneously create product and manufacturing process specifications.
Digital manufacturing has evolved from manufacturing initiatives such as manufacturability (DFM), computer-integrated manufacturing (CIM), flexible manufacturing, and lean manufacturing that emphasize the need for collaborative product and process design.
Many of the long-term benefits of product lifecycle management (PLM) cannot be achieved without a comprehensive digital manufacturing strategy. Digital manufacturing is a key integration point between PLM and machine shop applications and equipment, enabling the exchange of product-related information between design and manufacturing teams.
Towards New approach
New Products and New Ways to Create Them
To respond to these changes in customer needs, companies adapt their methods of operation. New product development (NPD) workflows are being developed, but in different ways depending on the size of the company.
Large companies are focusing on better coordination with their suppliers externally and improving coordination between their engineering, manufacturing, and supply chain teams to integrate customer feedback more quickly.
Smaller organizations prioritize shorter development periods and increase development agility.
The proliferation of software-enabled devices connected to the IoT creates extraordinary opportunities and great pain. Because as the complexity of components increases, it becomes more difficult to bring new products to market. Thus, development performance decreases, success rates suffer, and the return on R&D spending decreases.
How can R&D administrators and product owners respond? Increasing efficiency, increasing efficiency and expanding innovation through new ways of doing things.
Second way is by working on the Reverse Engineering concept of Manufacturing.
Manufacturing Industry Growth Essentials
This Digital Transformation Assessment sought to understand the current business imperatives, how the Coronavirus pandemic has affected priorities, and the strategic role adopting digital technologies will play for manufacturing businesses and their supply chains. The respondent were from different industries.
Looking at the Pandemic challenges and some major challenges in Digital Manufacturing
Despite the new urgency in the manufacturing sector, adopters still have challenges to overcome. These include:
Although early adopters of small and medium-sized businesses have gained a competitive advantage, it is still true that many of these smaller businesses lack the capital resources and pockets of their larger competitors.
Companies of all sizes also tend to focus on ROI, and the formula for successful ROI is different for different sizes. Tying investment dollars to future profitability is also a problem. Many companies struggle with upfront costs, but the costs of not adopting can be greater or even fatal.
A solution may come through the modularity of many of these platforms.
Using cloud-based software and cost-effective hardware, companies can develop incremental plans that focus on a critical area, such as downtime, and plan to scale the system when the results pay off in increased production.
Many manufacturing industries struggle to understand that they need to acquire and train people to successfully adopt digital technology. Typically, the platforms work with artificial intelligence and machine learning algorithms.
However, depending on the industry, other technologies such as digital twins and 3D modeling may be required. Retraining current employees may not be possible. And these new skills are in high demand.
IT and operational problems
IT is particularly difficult because of the competence problem. Expensive optical fiber, servers, and long cables must now give way to cloud-based technology with different requirements.
IT is also responsible for data transfer, security, and access, as well as a number of other tasks that are either unfamiliar or contrary to traditional practices. And many legacy systems may have their own extended software licenses that are now obsolete or not needed in the new digital world.
Corporate Culture Issues
The manufacturing industry has always been considered to be primarily made up of manual workers. And while there are still places for strictly manual work in many industries, many new skills and training are needed to bring users up to the skill level of technicians.
These new technologies often use interactive screens, tablets, and other human-machine interfaces to input and receive information needed by the user.
Training must be provided to develop the skills of employees to understand and use these types of systems and leave paper-based systems behind.
Security is also a concern for many business leaders who understand the importance of digital transformation and see its current and future benefits. Most of these systems are cloud-based and run over the Internet. They can also have Wi-Fi, cellular connectivity, and Ethernet, which can be used to connect old analog devices to the floor. These IoT security issues pose a risk, but they can be mitigated.
With news of hacking emerging almost weekly, many are concerned that such an event could bring production to a halt. Others fear that in critical industries like the pharmaceutical industry, these incidents could be life-threatening. While these challenges are real, security protocols continue to grow and focus on better and tighter security as adoption accelerates. Hackers can get into the system and control entire cycle of production.
Cyber risk can shake up the best initiatives Even for the most trusted production manager, cyber risk is a particularly daunting operational risk. Cyber threats are ubiquitous and can have devastating consequences if smart manufacturing initiatives are implemented without precautions such as creating a cyber risk strategy. Industrial companies are becoming a popular target. Unfortunately, many seem unprepared.
Even for those that do, such an assessment often takes the form of a vulnerability assessment, which is often inadequate due to the complexity of the production environment. Because of the unique combination of information technology (IT) and operational technology (OT) in factories, most require a different approach than IT-only environments.
Risk assessment in an OT environment requires knowledge of equipment and business processes, so alignment with business leaders can be critical. People, process, and technology overlap, and companies should look beyond traditional IT tools to assess and address risks in an OT production environment.
Creating a mitigation and prevention strategy that focuses on security, vigilance, and resilience can be key to managing risk in a smart factory.
Transformations in Manufacturing Industry 2022
It may vary from Industry to Industry
- Expansion into New Markets
- New Product Launches
- IoT Product Development
- Customer-Specific Applications/Portals Improve Process Efficiency
- Automation that helps reduce or eliminate poor quality systems due to inefficient tasks.
- Remote monitoring of assets
- A more detailed digitization roadmap, including tighter collaboration between IT, operations, and management in one location for future factory development.
Industrial IoT One of the main projects that continues to be heavily invested in is IoT—the application of the principles of the Internet of Things in manufacturing and industrial operations. Using smart and connected devices in factories or retrofitting sensors and interfaces to existing devices, IIoT offers the ability to collect and generate data for analysis, as well as the means to implement digital control and facilitate automation.
Digital twins A basic concept that fits well with IIoT is the digital twin. A digital twin is a virtual proxy for a physical system that exists only in digital space. It provides a close representation—or twin—of the structure, behavior, and state of the physical asset it mirrors. By interacting with the twin, a program can examine the past and present state and then analyze what the future state might be.
Digital twins can also be virtual aggregates of multiple physical elements—e.g., multiple jet engines can be combined to represent an aggregate on a plane. Likewise, digital twins can represent a production process, allowing manufacturers to virtually recreate a product and predict its real-world performance through simulation.
Through digital twins, the entire digital lifecycle of a product, from design to build to operation to servicing and decommissioning, can be simulated and examined as part of a concept called Digital Thread. Predictive maintenance Using digital twins with sensors and IIoT, artificial intelligence and machine learning can predict, prevent, and solve problems before they happen.
This type of preventive maintenance helps reduce potential downtime, contain costs, and can increase equipment OEE.
Management of Digital Performance Digital twins and threads typically focus on the factory floor and products. Expanding the idea leads to digital performance management: creating a unified system that includes performance management reports for senior executives, supply chain reporting, and plant performance metrics used by key front-line employees.
With one integrated system that can show financial and operational results, the hope is that when performance gaps are identified, corrective action can be taken quickly. For example, by looking deeper into the performance of a digital performance management system, manufacturers hope to find leverage to extract a few extra points from OEE, increasing production without additional investment.
Additive and distributed manufacturing
Additive manufacturing, or 3D printing, allows manufacturers to create objects in layers, which improves design, production freedom, and reduces waste. Using innovations in material technology, it is possible to build even complex objects with minimal additional costs. This can create lighter, more efficient, greener, and potentially cheaper products, improving operational flexibility and reducing time to market. Additive manufacturing allows greater flexibility by limiting product rework and helps create flexibility in customizing manufactured items. This technology makes it possible to flexibly distribute production to several locations, build products closer to the point of consumption, and help restart production in countries like the United States.
Automation and robotics Automation and robots have been used in industrial production for some time. However, new technologies such as AI and ML are bringing new types of robots to the forefront. Whether it’s product assembly, warehouse operations, or logistics, new automated forklifts, AMRs, and collaborative robots (cobots) can provide a wider range of support functions at lower costs than before.
Automated forklifts and AMR machines can be used to move materials during production in a factory and move finished products in warehouses and distribution centers. Using automation in the product assembly process, from material handling to quality control and packaging, can reduce direct and indirect labor costs. Similar to additive manufacturing, automation allows production to be established in markets with higher labor costs, reducing transportation costs and time to market.
Augmented Reality AR smart glasses with assistive technologies implemented by automation and robots can bring additional efficiency to production. For those working on the factory floor, the goggles can provide guidance on standard operating procedures and enable quick retrieval of product and part information. Early signs suggest that AR can also reduce error rates for tasks involving complex settings. Maintenance of production equipment is also beneficial. AR smart glasses can provide workers with instructions for regular preventive maintenance and access to online documentation for less common problems, enabling faster recovery times and shorter maintenance periods.
Upscaling With the increased digitization of the production layer and value chain, the qualifications and skills of employees are no longer the same. With the introduction of forklifts, AMR, cobots, and other forms of automation, workers must operate and manage these technologies. Most of the workforce needs technical skills. Warehousing In addition to manufacturing, manufacturers are extending digitization to the storage of manufactured inventories (as well as raw materials, parts, and other inputs), looking for ways to improve asset tracking, reduce inventory costs, and simplify inventory management.
Digitization, use of IIoT, and precise positioning can improve inventory visibility, and manufacturers can eliminate or reduce material handling by using automated vehicles and pallet robots to deliver material. Distribution Manufacturers are not always involved in distribution, but when they are, technologies such as IIoT and GPS can provide real-time delivery tracking and accurate delivery windows. Collect detailed delivery data or even cost data and other KPIs such as shrinkage and on-time performance of different distributors can be fed into the data analysis engine to reduce delivery costs.
Smart ways to enhance
Intelligent procurement and supply is part of a manufacturer’s competitiveness, and investing in supply chain tracking and traceability can help reduce losses and manage volatility.
- Implementing Smart Contracts
- RFID tag form production to end supply chain
- For regulated and valuable raw materials, digital labeling technologies can reduce counterfeiting and ensure compliance.
- Tracking spare parts across supplier networks and ensuring that deliveries and parts communicate in real time can improve production planning and minimize inventory. IoT can play a big role in this tracking, especially when transporting temperature-sensitive goods.
- Manufacturers who invest in digital technology can improve purchasing productivity and benefit from faster delivery.
The majority of digital efficiency is realized in manufacturing processes. Whether a manufacturer focuses on small batches (custom parts for aerospace or rail), mass customization (auto parts, accessories) or large-scale production (durable goods, toys, electronics, automobiles), digital transformation can bring significant value.
Digital manufacturing technologies can increase flexibility, enabling efficiency and increasing production while maintaining quality.
Manufacturing is an industry that has seen a continuous evolution from simple programmable logic controllers (PLCs) to more complex programmable automation controllers (PACs) for process control. On the machining side, the change from manual setup to numerical control (NC) or computer numerical control (CNC) increased flexibility.
As manufacturers continue to seek greater efficiency and move to a flexible space where small batches can be produced profitably, digital enablement is critical and regardless of technology, manufacturers strive to increase overall equipment effectiveness (OEE) and achieve the full benefit of equipment without downtime and defective residuals.
From a process perspective, tighter factory floor integration with design and engineering (CAD/CAM) software allows for faster information flow during design changes, reducing manufacturing and assembly errors.
Using simulation and other verification techniques as part of a new, more dynamic and flexible flow—reflecting the continuous integration and continuous deployment of cloud software development—can accelerate the implementation of design changes.
A modern steel plant in India was looking for the next S-curve in continuous improvement and planned to fully digitize its operations.
In addition to piloting new and promising technologies in individual projects, the X company wanted to follow a holistic approach to the digital transformation of the entire organization.
Two aspects were, among others, capacity building and creating a leadership vision to inspire the organization from the ground up.
As capacity building is central to digital transformation, developing the client’s digital capabilities is essential. Therefore, one can follow three main steps:
Analytics workforce power focused on high-skilled and continuous improvement of the project team.
Identify and define the three roles necessary for successful organizational change: Advanced Analytics measurement, Data Scientist, and Information Architect.
Develop customized learning for each role with specific internal and external modules such as data cleaning and structuring.
- Setting up this ecosystem comprised establishing and formalizing, amongst others,
- An internal networking community on digital topics
- Collaborations with knowledge institutions
- A valuable proposition for digital talent and access to corresponding talent pools
- Partnerships with start-ups and spin-offs
- Customer digital capability development is essential as capability development is central to digital transformations.
Digital transformation training to participants from all parts of the organization—from senior management to individual work units, including maintenance and local IT and non-technical functions.
In addition, a digital ecosystem was created to ensure the lasting impact of the change.
The creation of this ecosystem included, among other things, the creation and formalization of an intranet community on digital topics Collaboration with knowledge institutions .
The value proposition for digital talent and access to similar talents changes the most important strategic priority for producers worldwide.
Despite its importance, most manufacturers have struggled to achieve digital manufacturing success in limited pilot projects at a scale that would bring the technology to its full potential.
A holistic approach to digital manufacturing—one that considers organizational and business fundamentals as much as technological factors—can help manufacturers bridge the gap between pilot success and enterprise-wide adoption.
The good news is that, as several real cases show, deployment is not a mystery and is a success. These have the ability to help align a manufacturer’s vision for digital manufacturing. Learning from these case studies can also help build a solid business model and chart a course for enterprise-wide implementation.