In-Depth Analysis of the Current State of the Machine Vision Industry

As the concept of smart manufacturing gains traction, more companies are prioritizing machine vision inspection projects. This analysis highlights some common misunderstandings and pain points between vision companies and their clients.

Overall, significant challenges exist at every stage of a vision project, from initial communication to implementation and final acceptance. These issues must be acknowledged, though they are often difficult to resolve. In the early stages of communication, the inherent nature of industrial vision inspection—being driven by customer-specific applications—makes it challenging to seamlessly align any existing standard software or applications with the precise needs of the client.

When a project requirement arises, there is often a prevailing "speed above all" mentality in the industry. Companies feel compelled to respond to customer demands immediately, fearing that a slight delay could give competitors the upper hand. This urgency leads vision companies to invest heavily upfront, including in hardware and personnel. Given the resource-intensive nature of industrial vision products and engineers, a company aiming to compete for a fully customized project inevitably faces significant upfront costs. Engineers spend considerable time on software testing and developing proposals, even as clients frequently change their requirements—a frustrating and exhausting cycle for the engineers.

But does this strenuous effort substantially increase the chances of project success? Unfortunately, the answer is often no. Experience across various industries suggests that out of ten evaluated projects, securing three to four deals is already considered an exceptionally high success rate. This low conversion rate not only increases the financial burden on vision suppliers but also potentially causes them to miss out on other valuable opportunities.

The root cause of this issue lies in the extreme information asymmetry between suppliers and clients. When clients hold the upper hand, vision suppliers must strive to break down this barrier. To be responsible both to the project and the client, suppliers need to gather as much information as possible to assess the feasibility and technical viability of the project.

It is essential to establish an equal communication ground with clients, thoroughly understanding the current level of automation in the client’s production process and the urgency of their needs. Companies should develop their own communication protocols or technical memorandums based on their product or industry characteristics. The choice between speed and efficiency can be determined easily from a business management perspective.

Even if an industrial vision supplier is fortunate enough to win a project, the implementation and acceptance stages often reveal further challenges. It is common for companies to face additional demands or changes from the client during these phases. The pain of such experiences is best understood by those who have been through them.

Once software or product design is finalized, any modifications involve significant costs in terms of manpower and resources. Moreover, this can lead to a never-ending cycle of changes and validations, resulting in wasted resources and even difficulties in collecting payments—a truly frustrating scenario! This is why technical documentation during the initial communication phase is so crucial.

Another major issue that must be addressed is the shortage of skilled professionals in the industrial machine vision field. While machine vision companies are helping clients improve production efficiency and reduce labor costs with their products and systems, they themselves are grappling with a severe talent shortage.

It is worth noting that the majority of companies in the domestic machine vision industry are still small to medium-sized enterprises. These companies are vibrant and innovative in terms of technology, but the talent shortage presents a significant challenge. Machine vision engineers are a critical element of production in the AI era, yet in China, this role has not been elevated to a strategic level, nor has it been given a clear “social identity and function.” The explosive growth of the internet and AI industries in recent years has created a “black hole” effect, drawing in everything around it. Even companies providing industrial machine vision products and systems are struggling to retain their own talent. As a result, the cost of professional talent has skyrocketed, leading to a severe imbalance between supply and demand.

However, there is a silver lining. In recent years, many companies in the industry have started to take steps to address these challenges, seeking greater breakthroughs in corporate governance to attract talent. For example, one company has gradually moved away from the traditional hierarchical structure, transforming itself into a platform for its employees. Each employee and small team operates like a neural network, maintaining efficient operations while striving upward, albeit through challenging but vibrant efforts. In this sense, innovation in company structure often proves more effective than technological advances, enabling enterprises to achieve long-term success.