Manufacturing Technologies Pdf

[Ma Layssard]

Over the last few years, ABI Research has compiled numerous market data reports in the manufacturing sector composed of deep insight into market share analysis and highly segmented, service-specific forecasts. In total, smart manufacturing spending will reach US$950 billion by 2030, which is nearly triple the spending in 2021. In this post, ABI Research shares 11 forward-looking technologies used in manufacturing and evaluates what kind of solutions the top verticals are spending their money on.

Join an exclusive group of manufacturing technology implementers like yourself by signing up for the recently launched Innovators Network. Weigh in on discussions about cutting-edge technologies for industrial markets, read ABI Research's latest findings, and assess investment trends in various markets and industries. Joining this community allows you to help shape the narrative about manufacturing technology solutions, participate in surveys and interviews, and optimize your implementation strategies through data-backed analyses. Sign up today!

Industrial IoT (IIoT) connections include technologies like sensors, trackers, video surveillance, wearables, and other connections that help manufacturers better understand the flow of operations. These connections reveal key data to operators, such as equipment condition, predictive analysis, product location, etc.

IIoT devices are how manufacturers gain operational visibility within the digital factory or the supply chain. Our analysts saw many additive manufacturing firms embracing the IIoT at the International Manufacturing Technology Show (IMTS) last year.

Or, as another example, manufacturers can deploy massive IoT devices to geolocate Returnable Transport Assets (RTAs). These battery-powered IoT trackers can pinpoint the location of pallets, racks, roll cages, kegs, crates, totes, trays, containers, and other logistics assets. This can prevent theft and reduce costs associated with misplaced assets.

Simulation software can be used for manufacturers to see how a product/component or a production line will behave under carefully selected conditions. That way, operators know how to optimize their designs in a way that bolsters productivity and adheres to regulations.

Digital twins are the digital mirror of real-world objects like sensors, devices, machines, processes, systems, people, or even entire facilities. Manufacturers deploy digital twins to drive optimal business outcomes. They provide connectivity, metadata management, data management, increasingly advanced analytics, and often integration with business applications and process systems.

More advanced (high-fidelity) digital twin technologies contain analytic models (physics-based or increasingly machine/deep learning-based) that enable prediction and simulation. As a result, this allows manufacturers to compare expected versus actual behavior, "what-if" scenarios, and continuous improvement of models through feedback loops. All modern digital twins can interact with an IoT platform or an Application Enablement Platform (AEP).

To understand the benefits of a digital twin, consider the case of Consumer Packaged Goods (CPG) manufacturing giant Mars Inc. The company has leveraged the Azure Digital Twins IoT platform since 2020 at its 160 manufacturing facilities worldwide. Using the Azure digital twin, Mars staff are able to predict outcomes and run production line simulations accurately. Moreover, using digital twins has also benefited Mars in meeting its sustainability goals. The technology helps the firm improve water stewardship at plants and reduce waste and Greenhouse Gas (GHG) emissions.

Supervisory Control and Acquisition Data (SCADA) systems are the next digital manufacturing technology on the list. Industrial SCADA systems acquire and present data to the Human-Machine Interface (HMI) and central operating terminals. This, in tandem with Programmable Logic Controllers (PLCs), manages industrial equipment, while a historian stores time series, alarm, and event-based data.

A related technology is Human-Machine Interface (HMI) software. HMI software provides and allows manufacturers to design graphical interfaces to monitor and control production processes and assets locally. HMI software can run independently from SCADA, simply as a local visualization and control tool for a single asset or process.

The Real-Time Location System (RTLS) is a much-needed technology in the manufacturing industry. The manufacturing sector is losing hundreds of billions of dollars related to the loss of products and equipment. These financial losses are compounded by the subsequent operation downtime induced by the loss of products and equipment.

Using an RTLS, the manufacturing equipment can be tracked and traced, and discovered in real time whenever needed. Pallets at manufacturing sites often move between the site and the customer facilities. Tracking them often relies on physical tags and requires substantial labor and time to trace and manage them. Here again, RTLS technologies provide ample opportunity to automate the tracking of these assets.

COVID-19 created an immediate need for remote worker enablement, and Augmented Reality (AR) has excelled at remote assistance and expertise for years. ABI Research has tracked the most active use cases for Extended Reality (XR) in the manufacturing sector, and remote expertise has consistently shown the most active users and active implementations in industrial markets.

By 2025, ABI Research estimates more than 8 million monthly active users of remote assistance in the manufacturing industry. Nearly half of those use smart glasses, and the others use mobile devices.

A manufacturing assembly line worker, for example, would see an AR-enabled overlay that provides workstation instructions. This way, the employee can continuously work without having to look down at physical instructions or receive support from a human co-worker.

When using extended reality in the manufacturing sector, employee training is improved in knowledge retention and efficacy of content. This makes training more efficient both during and after. Finally, instant access to experts, no matter the location, reduces wasted time.

Robotics is an integral aspect of modern manufacturing, especially in the face of labor shortages and increased product demand. In our 2023 trends paper, our analysts pointed out the fact that industrial robot deployment didn't slow down during COVID-19 and will continue to grow in 2023. Robots can automate tasks such as materials handling, product picking, assembly, parts unloading, and other critical processes. There are four main types of robotics that manufacturers can leverage: industrial robotics, Collaborative Robotics (cobots), mobile robotics, and exoskeletons.

Collaborative Robots (cobots) are articulated robots designed to interact with human workers in a shared workplace. While the industry has varying definitions for cobots, ABI Research defines a cobot as follows:

Within the category of mobile robots, there is an important distinction between Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs). While AGVs require external infrastructure to move, AMRs, in principle, can navigate an environment without needing external infrastructure.

There are also Remotely Operated Vehicles (ROVs) that are designed to be remotely operated or teleoperated by human operators. Many robots are remotely operated but have a high degree of autonomy and operate at least partially without fiducial markers or magnetic tape. Therefore, these technologies are classed as AMRs. With the increased adoption of AMRs, robotics manufacturers will have a greater reliance on robotics simulation software and the use of Visual SLAM (vSLAM) for perception.

Exoskeletons are a relatively nascent robotics technology with immense promise in manufacturing. This wearable solution helps human workers perform industrial applications, such as lifting heavy loads or reaching products otherwise out of reach. Exoskeletons mitigate worker health risks like Work-Related Musculoskeletal Disorders (WMSDs). Moreover, exoskeletons are known to provide manufacturers with productivity gains.

Manufacturing Execution System (MES) software is a primary foundation for manufacturing plants in the Industry 4.0 space. The software tracks, documents, and guides the production process in real time to visualize and control the factory floor. These capabilities are essential to improve production and labor efficiency, support comprehensive digital threads, and enable new business models and best practices in the long run.

MES software is used to monitor the factory floor, track, document, and guide the production process in real time, and provide further support in optimizing the production process. Each unit is tracked uniquely, end-to-end, from raw material to final product. Holistically, the software supports Overall Equipment Effectiveness (OEE), a composite of availability, quality, and performance. Architecturally, MES software fits between ERP and process control systems. MES solutions cover a broad range of capabilities, so after implementation and customization by manufacturers, it is rare to find multiple MESs that work the same.

Machine Vision (MV) is a key enabling technology in automation and human-machine collaboration, especially when deployed at the edge. To date, this Artificial Intelligence (AI)/Machine Learning (ML)-enabled technology has been mainly deployed in quality inspection, defect detection, security, and surveillance.

As more and more manufacturers embark on digital transformation, they begin to collect more data to optimize their internal processes. This has led to the emergence of data-driven Deep Learning (DL)-based MV technology. Many startups have introduced novel applications that are DL-based MV. Conventional MV companies have also started to invest in DL startups to develop in-house solutions to cater to this development.

c80f0f1006