Cognex Machine Vision SWOT Analysis


The research aims to develop a SWOT analysis on Cognex, with a focus on deep learning-based machine vision products. Specific goals are the assessment of market competition, pricing strategies, and end-users' view on the company's current and future performance. The SWOT analysis is expected to be part of the fundamental analysis for evaluating Cognex's shares.

Early Findings

  • ResearchAndMarkets expects the global machine market to grow by 8% CAGR between 2020 and 204, which is driven by operational cost reduction due to process control and rising demand from the APAC region and automotive industry. For example, thermal inspection, vision-guided robots in the manufacturing industry, industrial internet of things are the key applications of machine vision technologies. There is an increasing trend of integrating "machine vision systems with vision-guided robot controllers."
  • Driven by the trend of machine vision technologies, the global machine vision camera market is expected to grow by 8.8% between 2019 and 2026. The key growth drivers are: the wide application of cameras outside industrial sectors, increased demand from quality inspection and automation, and rising demand for vision-guided robotic systems and automated devices. In comparison, market growth is constrained by a lack of awareness of such technology, lack of skilled labour, and the complexity of integrating machine vision technologies.
  • Due to the impact of COVID-19, global supply chain disruption, cash flow constraints of businesses, and production halt have resulted in the slow-down of the machine vision market. The global revenue is expected to increase by 6.1% CAGR from $9.6 billion to $13.0 billion between 2020 and 2026. Due to the impact of COVID-19, pharmaceutical and food & packaging industries have seen increasing demand for machine vision solutions and systems, such as the automated quality inspection or assurance solutions in the food & packaging industry. Moreover, with less human involvement in the manufacturing process, automated systems are expected to see significant growth.
  • The global machine vision market faces a series of challenges, such as changing customer requirements in favour of customized products as opposed to standard ones for each industry. Existing technology vendors face the challenge of cost-effective solutions, such as robot cells (v.s. general machine vision), smart camera-based systems, and predictive maintenance (v.s. time-based preventative maintenance). These solutions are expected to add pricing pressure to vendors.
  • As one of the leaders in AI-based machine vision systems and cameras/sensors, Cognex has embarked on an acquisitive growth strategy since 2017 by investing in deep learning technologies. Two acquisitions, one being ViDi Systems in 2017 while the other being SUALAB in 2019, are expected to advance Cognex's leadership in deep learning-based machine vision solutions, such as industrial inspections.
  • Cognex has started to face a significant downturn in business performance in 2019, when consumer electronics and automotive sectors, two of its largest revenue contributors, have experienced a noticeable decline in demand for machine vision systems, especially in China. This has resulted in low doubt-digit revenue decline and shrinking net profit margins because of increasing operational costs. The two sectors together accounted for approximately 50% of Cognex's revenue. In contrast, the logistics sector was expected to see noticeable growth in revenue despite a low base. Cognex's share price has started a downward trend since it reached an all-time high ($72.19) on 24 November 2017, while currently sat at $62.48 on July 13, 2020.
  • In comparison to other deep learning software, Cognex's competitive advantages are: 1) requiring a small number of training images, 2) needing minimal computing power and one GPU, 3) requiring no intervention by machine builders or system integrators, 4) working with high-resolution images to identify almost all anomalies. Overall, it only requires a few minutes to train a deep learning-based model using a small sample of images.
  • Machine vision solutions that use deep learning help manufacturers achieve cost-efficiency and high production yields. ABI Research forecasts the market to increase by 20% CAGR between 2017 and 2023, and reach a revenue of $34 billion in 2023.
  • Unlike conventional machine vision technologies that rely on line-by-line coding, deep learning offers a number of advantages: 1) based on unsupervised learning and can be deployed by practitioners with significant coding experience; 2) deep learning-based machine vision models can detect product defects and quality issues more than what humans are capable of; 3) deep learning techniques allow manufacturers to develop their own models instead of locking into a single vendor product; 4) open-source deep learning techniques lower the market entry barriers for new technology vendors.
  • Deep learning software has gained traction in recent years, regarding machine vision applications. Some open-source deep learning frameworks are available for building and training neural networks, such as TensorFlow, Caffe, PyTorch, SciKit, Keras, OpenNN and MXNet. Although these software libraries are generic tools, they can be used for executing "targetted deep learning in machine vision applications," such as inspecting the labels of radioactive medical imaging products.
  • Moreover, Cognex also faces competition from 11 key market participants in the global machine vision markets, namely Basler AG (Germany), Omron Corporation (Japan), Keyence (Japan), National Instruments (US), Sony Corporation (Japan), Teledyne Technologies (US), Texas Instruments (US), Intel Corporation (US), ISRA Vision (Germany), Sick AG (Germany) and FLIR Systems (US). In the machine vision camera markets, Basler AG, Omron Corporation, Keyence, National Instruments, Sony Corporation, Teledyne Digital Imaging are also key competitors of Cognex.

Proposed next steps:

You need to be the project owner to select a next step.