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Smart quality control and monitoring

Investment in smart quality control and monitoring is on the rise.

Companies already investing in
this opportunity

Azur Drones ● BP ● BMW Group ● Siemens Energy ● PrecisionHawk ● Walmart ● Tesla ● Fujitsu AI ● RSIP Vision
$
36
bn+

investment opportunities in smart quality control and monitoring to 2028

From improving efficiency, and reducing downtime across manufacturing and energy generation sectors, to increasing clinical efficacy. Underpinned by better infrastructure, more data, and improved analysis, we expect to see more opportunities in smart quality control and monitoring over the coming years.

Five promising use cases in

Smart quality control and monitoring

  1. NFC-based printed IoT sensors, supported by digital twin simulations and blockchain transactions for optimised reverse logistics.
  2. Predictive maintenance 4.0 with 5G IoT sensors to optimise equipment performance.
  3. Machine vision systems powered by deep learning to automate quality control processes and detection of manufacturing flaws.
  4. Digital twin simulations and predictive analytics tools to forecast failures, root causes, and initiate early maintenance.
  5. Drone monitoring with LiDAR or thermal sensors allow faster, safer, and accurate advanced monitoring, resource optimisation, and fault detection.

Which sectors are adopting smart control and monitoring?

Improvements to smart control and monitoring are being enabled by a wide range of technologies. From 5G drones for surveillance and remote monitoring and inspection to predictive analytics. The global predictive maintenance market size alone is projected to reach $15.9 billion by 2026, representing a compound annual growth rate of 30.6%.

Any sector involved in manufacturing and maintaining structures can benefit from smart control and monitoring. This could range from energy generation operators such as solar farms, wind farms, power plants, oil rig operators, to transportation or aviation firms.

Use Case

Which companies are investing the most in smart quality control and monitoring?

Oil and gas companies, in particular, have invested in automating their maintenance to cut costs and incorporating advanced analytics and data science to streamline their operations. A plethora of new services now cater to these firms, including IoT-based detection of spontaneous leaks in pipelines. Meanwhile, BP’s drone pilot programme to remotely monitor methane emissions using advanced sensor technology in the UK North Sea broke the UK record for the longest commercial drone flight.

Investment is also happening across transportation and logistics, energy, power and utilities and food and beverage firms. Drinks manufacturer Heineken installed a machine vision quality control system to analyse bottles produced at a speed of 22 bottles per second with a 0% error rate and Fujitsu AI has developed an augmented defect recognition (ADR) solution for GKN Aerospace for their aerospace component inspection process that takes minutes rather than hours.

31
%

average annual growth rate in the predictive maintenance market to 2026

$
10
bn

opportunities in remote monitoring and control market to 2028

$
15
bn+

opportunities in the global drone market to 2026

What is a smart quality control system?

A smart quality control system is a system that integrates different emerging technologies to improve efficiency and reduce downtime. Importantly, it includes operation and maintenance, as well as manufacturing. The goal is to be able to measure and monitor operations and schedule maintenance before an issue arises.

What technologies are enabling smart quality control and monitoring?

Smart quality control is creating opportunities in automated monitoring, quality control, predictive maintenance and predicting clinical efficacy.  

A range of technologies underpin smart quality control and monitoring. Automated monitoring is underpinned by developments in drones, sensors, reinforcement learning, machine learning, wireless sensors, 5G telecommunication and computational modelling.  Improvements in AI and machine learning are driving more accurate predictive analytics.

To get the most out of emerging smart quality capabilities, a complete redesign of processes is often necessary, but often challenging due to the number of stakeholders involved. The best place to start is to identify the areas where costs and failures are highest. Positive internal success stories internally help demonstrate the effectiveness of the automation vertically and horizontally meaning there is buy-in from other stakeholders.