Technology continues to be a primary catalyst for change in manufacturing organizations. Indeed, technology advances give companies more possibilities to lift their productivity and flexibility and while it remains difficult to predict how technology trends will play out, executives can plan ahead better by tracking the development of new technologies, anticipating how companies might use them, and understanding the factors that affect innovation and adoption.
14 technology trends
McKinsey identified 14 technology trends divided into 2 main thematic groups:
- Silicon Age, which encompasses digital and IT technologies, and
- Engineering Tomorrow, which encompasses physical technologies in domains such as energy and mobility.
Moreover, the relevance for 20 Industry categories has been assessed on a scale from 1 (minimal) to 5 (high):
The 14 technologies that have been taken under considerations are:
In the following sections we are going introduce the 6 main technology trends with higher impact on manufacturing organizations.
The latest connectivity protocols and technologies power networks with more data throughput, higher spectrum efficiency, wider geographic coverage, less latency, and lower power demands. These improvements will enhance user experiences and increase productivity in industries such as mobility, healthcare, and manufacturing.
Companies have been quick to adopt advanced connectivity technologies that build on existing standards, but newer technologies such as low-Earth-orbit (LEO) connectivity and private 5G networks have seen less uptake to date.
Main connectivity technologies are:
- Optical fiber
- Wi-Fi 6
- 5G/6G cellular wireless
- LEO satellite constellation
In automotive and assembly industry, connectivity could enable preventive maintenance, improve navigation, prevent collisions, enable various levels of vehicle autonomy and carpooling services, and provide personalized infotainment offerings. Connectivity allows retailers to manage inventory, improve warehouse
operations, coordinate supply chains, eliminate checkout activities, and add
augmented reality for better product information
With AI capabilities, such as machine learning (ML), computer vision, and natural-language processing, companies in all industries can use data and derive insights to automate activities, add or augment capabilities, and make better decisions.
Companies are developing and adopting more applications for AI, but organizational, technical, ethical, and regulatory issues must be resolved before businesses can realize the technology’s full potential.
Main Machine Learning methods are:
- Computer Vision
- Natural-Language Processing (NLP)
- Deep Reinforcment Learning
- Knowlledge Graphs
AI solutions can be exploited in different ways in manufacturing sectors:
- Automation of quality testing and manufacturing/assembly processes
- Optimizing energy production and scheduling, detecting equipment defects early to minimize downtime, and analyzing consumer energy use data to inform
- Using autonomous machinery and robots, computer-vision enhanced safety procedures, and 3-D design optimization software
Cloud and Edge Computing
Cloud platforms, built from “hyperscale” data centers that deliver and enable enormous computing and storage capabilities, and connected by fast, high-capacity networks, enable an array of services that grow ever broader and richer.
Increasingly, these platforms also incorporate computational and data resources at network edge nodes located near end users or in their facilities.
These edge resources fulfill needs for low latency (that is, minimal processing delays) in real-time systems such as warehouse automation. Edge resources are also being used more and more in mobile applications such as vehicles.
Ongoing integration of cloud and edge resources will let users extend the cloud’s speed and quality to edge and real-time systems, thereby accelerating innovation, lifting productivity, and creating business value.
Cloud and Edge Computing will benefit the manufacturing sector in different ways:
- Increase in overall efficiency of transportation routes through schedule management, route optimization, etc; reduced reliance of connected/autonomous vehicles on large, distant data centers for access to compute
- Improvements in networking and data latency, increasing effectiveness of other Industry 4.0 technologies (such as the digital twin), leading to better
- Better networking and data latency, which make automated manufacturing technologies more effective, leading to higher overall productivity for aerospace players, while flowing data to cloud platforms for efficient analytics
Industrializing Machine Learning
Industrializing machine learning (ML) involves creating an interoperable stack of technical tools for automating ML and scaling up its use so that organizations can realize its full potential.
ML tools can help companies transition from pilot projects to viable business products, resolve modeling failures during production, and overcome limits on teams’ capacity and productivity.
Experience suggests that organizations that industrialize ML successfully can shorten the production time frame for ML applications by 90 percent (from proof of concept to product) and reduce development resources by up to 40 percent.
While a small number of leading companies have pioneered the industrialization of AI, we expect its adoption to spread as more companies seek to use AI for a growing number of applications.
Main benefits in manufacturing:
- Augmenting design and manufacturing processes through optimizations from AI/ML models (eg, AI models to aid in the 3-D simulations for aircraft design, supply chain optimization for manufacturing, security risk management)
- Using AI/ML to enhance design and manufacturing processes such as predictive maintenance, automated quality testing, and demand forecasting and to provide customer service features such as navigation
Future of sustainable consumption
Sustainable consumption centers on the use of goods and services that are produced with minimal environmental impact by using lowcarbon technologies and sustainable materials.
At a macro level, sustainable consumption is critical to mitigating environmental risks, including climate change. For companies, the production of sustainable goods and services can support compliance with emerging regulations, create growth opportunities, and help attract talent.
While many technologies that support sustainable consumption are technically viable, few have become cost-effective enough to achieve mass scale.
The global push toward decarbonization could accelerate their adoption, as could the emergence of a generation of consumers willing to change their buying patterns.
6 main patterns reflect enhancements in conscious consumption:
- Low carbon: minimizing greenhouse-gas (GHG) emissions over life cycle of production, use, and disposal
- Reduce, reuse, and recycle: reusing materials previously used in a product or created as a manufacturing by-product
- Biodegradable: using materials that can be broken down into chemical constituents in ambient conditions (ie, landfill)
- Waste conscious: minimizing waste through optimized consumption (eg, of water, plastic)
- Biobased: Prioritizing materials intentionally made from substances derived from living (or once-living) organisms
- Nontoxic: following processes that emit fewer chemicals and environmental pollutants during production and use
In this post we have highlighted 6 out of 14 technology trends that McKinsey & Company identified as key technologies for the future.
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