by Pankaj Raushan, on August 20, 2018
Read this article to learn strategic insights into the rise of smart factories, big data, the industrial internet of things (IIoT) and artificial intelligence. Each of these interconnected trends will help transform manufacturing as we know it.
Smart manufacturing is a broad concept; it is not something that can be implemented in a production process directly. It is a combination of various technologies and solutions which collectively, if implemented in a manufacturing ecosystem, is termed smart manufacturing. We call these technologies and solutions "enablers," which help in optimizing the entire manufacturing process and thus increase overall profits.
Some of the prominent enablers in the current market scenario include:
Companies are constantly investing and exploring how to obtain benefits through the implementation of enablers. If we closely look at enablers, then we will observe that they are either generating data, accepting data, or both. Data analysis will help in making the production process efficient, transparent and flexible.
Smart manufacturing is all about harnessing data; data will tell us “what to do” and “when to do it.” Since smart factories are built around data, cyber security, above all, will play an important and significant role in the entire ecosystem of smart manufacturing. Data security is an important challenge while implementing these enablers.
All the stakeholders of smart manufacturing may be characterized typically in three types of companies which can broadly be called “product and control solution providers,” “IT solution providers or enablers,” and “connectivity solution providers.”
Industrial internet of things (IIoT) is nothing but an ecosystem where every device, machine and/or process is connected through data communication systems. Each machine and piece of industrial equipment is embedded or connected with sensors which typically generate the relevant data. This is further transferred to the cloud/software systems through data communication systems. This huge amount of data has lots of insight which if analysed may help in identifying certain dark areas within the production process. After the analysis of the data, it is sent as feedback to the production systems for any corrective action.
There is huge potential for IIoT in smart manufacturing. You cannot increase production beyond certain limits, so what do you do to increase your profits? You can’t increase production because there is no demand for that. So, you try to look at the backend process and make it efficient. Now this is possible only when you have the precise details about your production process. This is where IIoT comes into the picture. Sensor generating data can be implemented at each process of production so that you can get the data, analyse it and take corrective action to increase the efficiency, thus increasing profitability.
However, it is not so easy to implement IIoT in current and/or old organisations, but you can implement it in newly established manufacturing facilities. This is because results can only be achieved if the implementation of smart manufacturing concept is there right from the start of design process for a manufacturing facility.
Smart manufacturing is not widely implemented; however, it’s there in bits and pieces in some organisations. You can’t change the basic design of machines or a factory system to implement all those sensors and other related technology. This makes the implementation of IoT in current or old manufacturing facility a bit difficult and in some cases impossible.
The IoT in manufacturing market size is projected to grow from USD 12.67 billion in 2017 to USD 45.30 billion by 2022, at a compound annual growth rate (CAGR) of 29.0% during the forecast period of 2017–2022.
Major forces driving IoT in manufacturing market are the growing need for centralized monitoring and predictive maintenance of manufacturing infrastructure. The increasing need for agile production, operational efficiency, and control, and demand-driven supply chain and connected logistics are also expected to drive the market.
The concept of artificial intelligence is old, but it is now finding applications in manufacturing ecosystems. In the last 5-6 years, there has been a tremendous increase in interest and investment regarding AI in manufacturing. This is mainly due to a few reasons, as AI will work only if the data is available, and it has only recently been possible to build the needed capability to:
These have collectively made it possible for AI to be implemented in manufacturing shop floors. Earlier manufacturing was being done by low-cost countries where it is very difficult to justify the high cost of implementation of AI in their manufacturing ecosystems. But due to a rise in wages, now it is possible to implement AI even in countries such as China, which is considered the factory of everything. China is now making a significant amount of investments in artificial intelligence especially for manufacturing and other related applications.
Furthermore, robots with AI capability are also finding significant application in China manufacturing ecosystems. Robotics with AI capability made it possible to involve the perception-based decision making which otherwise was not possible by rule-based algorithms in robots. Predictive maintenance is another important aspect where AI finds significant application. Predictive maintenance enables the capability to determine performance, breakdown and operating conditions of equipment or machine on a real-time basis.
The artificial intelligence (AI) in manufacturing market size is expected to grow from USD 272.5 million in 2016 to USD 4,882.9 million by 2023, at a CAGR of 52.42% during the forecast period.
The growing usage big data technology, industrial IoT in manufacturing, extensive usage of robotics in manufacturing, computer vision technology in manufacturing, cross-industry partnerships and collaborations, and significant increase in venture capital investments will propel the growth of the AI in manufacturing market.
Blockchain in manufacturing is still at a very nascent stage; however, it is a much-discussed new technology in manufacturing ecosystems. Currently, it is being implemented in financial systems, but companies are exploring its application in manufacturing.
Looking at the capabilities of blockchain, aviation, food & beverage, and medical are some of the industries which could greatly benefit from this technology. These industries, due to some stringent rules and regulations, require full scrutiny of all their suppliers across the value chain. Blockchain could help in maintaining quality control right from the development of raw materials. Currently, most of the attention is on the development of blockchain for supply chain function across the manufacturing ecosystems.
Some of the industries that are actively developing blockchain include apparel, solar energy, mining, fishing, food & beverage, shipping (cargo transportation), fertilizer, healthcare and aviation. The list is not exhaustive, and as the technology matures, more and more industries may get involved in implementing blockchain. Companies such as IBM, Microsoft, GE, Samsung, and Moog are involved in developing and implementing the blockchain in manufacturing ecosystems.
The blockchain market in manufacturing has yet to conceptualize fully, and thus we are expecting the market to start generating significant revenue from 2020 onwards. However, many organizations have already started investing and exploring the benefit from blockchain technology in manufacturing ecosystems.
The next thing that makes a typical manufacturing plant a smart manufacturing facility is the implementation of industrial robots. Industrial robots is not a new concept, it has been in the systems for the last 40-50 years. The only thing that has changed with respect to industrial robots is that they have now become intelligent. Earlier the robots were programmed to do one single task at a time. If you want to do other type of tasks, then you must change the codes.
Now robots are well connected with the sensor network implemented within the manufacturing shop floor, and they get the data from sensors and change their action accordingly. Artificial intelligence is also being slowly implemented in robotics systems, and thus it makes systems autonomous. Through AI, robotics systems are expected to change their actions according to the situation on a real-time basis.
Currently, the majority of industrial robots are implemented in the Asia-Pacific region. Industrial robots play a major role in the automotive industry. Government initiative is considered as one of the important drivers for the development and growth of robotics. The US and China are actively providing all the necessary impetus to drive the demand for robotics further.
Apart from industrial robots, there is new type of robot which is rising and is called collaborative robots. These machines will work alongside humans to support all the work done by humans. For example, a collaborative robot can observe what a human operator at an assembly line is doing, learn the human’s task, and autonomously start performing that same task with the exact same kind of precision. Further, the development in collaborative robots has reached such an extent that it would be difficult to differentiate it from industrial robots with regards to its application. Now, collaborative robots that were supposed to do only light work are now capable enough to complete heavier jobs which were generally done only by industrial robots.
The industrial robot market is expected to be worth USD 71.72 billion by 2023, growing at a CAGR of 9.60% during the forecast period.
The collaborative robots market is expected to be worth USD 4.28 billion by 2023, growing at a CAGR of 56.94% between 2017 and 2023.
The main drivers for the industrial robotics market are increasing investments for automation in various industries and the growing demand from small and medium-scale enterprises (SMEs) in developing countries.
Digital twin is another concept in the ecosystems of smart manufacturing. It creates the virtual model of an asset, process, or system by using the data obtained from sensors in the systems or asset and algorithms for making reasonable projections about the process. Predictive maintenance is one of the important systems which will use digital twins. The benefits of digital twins include potential reduction in time and cost of product development and elimination of unplanned downtime. The rising adoption of IoT and cloud platforms, and 3D printing and 3D simulation software are boosting the adoption of digital twin.
Aerospace & defense, automotive & transportation, electronics & electrical/machine manufacturing, and energy & utility are the major adopter of digital twins. Once the concept of digital twins develops and matures, then we may see its increasing application in non-manufacturing sectors such as retail & the consumer goods market.
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About the Author: Pankaj Raushan is an Assistant Manager at MarketsandMarkets. He has worked for the company for more than 5 years and has been focused on the manufacturing domain. He has completed projects related to emerging technologies, providing industry analysis, go-to-market strategy, growth strategy, and competitive landscape.