The global smart manufacturing market is projected to exhibit a significant growth, at a CAGR of around 12.8%, during the forecast period. The rising adoption of AI industrial robots is witnessed in developed countries and certainly seems to be predictable due to the rise in production demands. This includes continued adaptation to the proliferation of automation and IoT, increased resource efficiency, and need for safer and more simplified robotic technologies to work in collaboration with humans. According to IFR (International Federation of Robotics), around one million industrial robots are currently working in industrial units across the globe. Many global institutions and organizations have taken several initiatives for the adoption of AI in the manufacturing sector.
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The manufacturing industry incorporates substantial activities from the past few years, certainly aiming new advancements based on AI. This includes standardized schemes and establishment of ecosystems that engage both the IT and manufacturing industries together, along with the introduction of IIC (Industrial Internet Consortium) in the US, led by General Electric Co., and the introduction of government-led Industry 4.0 initiative in Germany. Several global companies are adopting autonomous decentralized systems to enhance production process. For instance, Hitachi, a Japanese multinational conglomerate, is working to offer numerous innovative manufacturing solutions to industries, based on the “symbiotic autonomous decentralization” model for forming new business and achieving overall optimization of activities by connecting different systems together.
Ai includes the tracking and capture of data, to which, the manufacturers now have access to more resources. Data is being obtained from a range of conventional sources, which includes classic consumer surveys and more innovative applications, such as the IoT and smart sensors to capture machine readings. With AI, the executives in the organization have access to real-time data for every facet of a product manufacturing, from cycle times and inventory counts to warranty claims. With big data analytics, data can be utilized to analyze, identify, and foster growth opportunities by allowing manufacturers to identify new geographic regions to target, expand into niche markets, foster customer intimacy, innovate, improve product life-cycle, increase value-add, and improve profit margins. Such innovations are driving the global smart manufacturing market.
One of the other crucial elements of AI that is expected to gaining popularity and application in manufacturing control systems and pattern recognition is fuzzy logic or fuzzy models. The fuzzy logic was introduced in 1965 and is based on the mathematical theory of fuzzy sets, which is a generalization of the classical set theory. Tools based on fuzzy logic enable inclusion of imperfect information and uncertainties in decision making models, which makes them perfectly compatible for manufacturing decisions. A few instances are the automatic transmission of Lexus automobile, a washing machine that automatically adjusts the washing cycle for load size, fabric type and amount of dirt, and avoice-controlled helicopter that follows commands such as forward, up, left, right:hover, and land. This fuzzy logic is expected to offer growth opportunity to the global smart manufacturing market during the forecast period.
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Global Smart Manufacturing Market-Segmentation
By Component
- Hardware
- Software & Services
By Technology
- Programmable Logic Controller (PLC)
- Supervisory Controller and Data Acquisition (SCADA)
- Machine Vision
- Distributed Control System (DCS)
- Product Lifecycle Management (PLM)
- Others (3D Printing)
By End-Use Industry
- Healthcare
- Automotive
- Aerospace & Defence
- Oil & Gas
- Food & Beverages
- Others (Metals and Mining)
(This release has been published on OMR Industry Journal. OMR Industry Journal is not responsible for any content included in this release.)
