The transition to AI has now become an unavoidable task in the manufacturing and distribution industries in Korea. Site managers and practitioners are now at a critical crossroads.
As global competition intensifies, raw material prices and labor costs are continuing to rise. With the addition of stricter ESG regulations and carbon neutral policies, companies are being required to innovate on a completely different level.
In particular, recently, there has been an increasing number of cases in which large manufacturers are demanding that their component suppliers build smart factories, and large distributors are requiring their logistics partners to introduce digital systems.
Amid these changes, the burden on managers and practitioners in the field is increasing. There are various concerns, including the anxiety that the expertise accumulated over many years of experience may no longer be effective, the risk of failure due to the introduction of new technologies, and the possibility of resistance from members of the organization.
In fact, companies that have successfully introduced AI technology have not only dramatically improved productivity and quality, but have also succeeded in increasing the job satisfaction of their employees.
This article aims to present a specific methodology for managers and practitioners in the field to successfully promote AI transformation. In particular, we will focus on practical approaches that can increase the competitiveness of the organization while maintaining the stability of the field.
AI transformation is no longer an option, but a necessity. I hope this article will help you find the right direction for your company and organization.
In order to start AI transformation in manufacturing and distribution, accurate on-site diagnosis must be carried out first. To do this, it is important to analyze the on-site work process in detail and identify actual problems and opportunities for improvement.
On-site diagnosis requires a period of at least three to six months, and active participation of on-site practitioners is essential during this process. Quantitative indicators such as time required for each work process, defect rate, and rework rate, as well as the problems and improvement requirements experienced by practitioners, should be collected in detail.
In particular, the more tacit knowledge has accumulated in the field, the more careful diagnosis is needed. This is because we can discover opportunities to digitize the know-how of veteran workers and thereby increase the practical value of introducing technology.
The pilot project is planned based on the priorities derived from the on-site diagnosis. The most important thing at this stage is to select an area with a high probability of success and a clear way to measure the effectiveness.
Pilot projects typically run for three to six months, during which time the existing and new systems are operated in parallel to verify the results. Field practitioners can directly compare the two systems to identify the advantages and disadvantages of the new system and derive necessary improvements.
During the pilot operation period, it is important to measure performance on a weekly basis and collect feedback from the field. This will increase the stability of the system and naturally ensure the acceptance of the field.
Finally, once the results of the pilot project have been verified, the next step is to expand the project to other areas. In the expansion phase, lessons learned and improvements made during the pilot process can be reflected to build a more complete system.
In particular, it is effective to use the practitioners who participated in the pilot project as internal experts in the expansion phase. This is because they not only have a high level of understanding of the new system, but they can also explain the value of the system in the language of the field.
A phased approach is also required during the diffusion process. Starting with similar processes or business areas and gradually expanding the scope is a way to minimize risk while achieving a stable transition.
In the process of phased AI transition, the participation of practitioners is not just an option, but an essential success factor.
In particular, manufacturing and distribution sites are areas where a lot of tacit knowledge has been accumulated, so it is difficult to create real value from AI systems unless the experience and know-how of practitioners are effectively reflected.
When practitioners participate from the pilot project stage, the system's completeness is greatly improved. For example, when building a quality control system, if the experience of skilled inspectors is reflected, the AI model can learn more sophisticated defect criteria.
In addition, the participation of practitioners also greatly contributes to increasing the system's on-site acceptance. Practitioners who have experienced their opinions being reflected in the system develop a sense of ownership instead of a sense of rejection of new technology.
In addition, the participation of practitioners plays a more important role in the diffusion phase. Practitioners who have participated in the pilot project can play a key role as internal experts and change management agents when expanding the system to other sites.
What is particularly noteworthy is that the participation of practitioners enables continuous improvement of the system. In the process of reflecting and supplementing various situations and exceptions that occur in the field in the system, the feedback from practitioners is the most reliable basis for improvement.
Ultimately, the practitioner-participating process is an essential condition for the success of AI transformation and a key driver of increasing the digital capabilities of the organization. This will be the foundation for creating an innovation culture throughout the organization, beyond the mere introduction of technology.
The expertise of professionals accumulated over decades in manufacturing and distribution sites is a valuable asset in itself. In order to effectively incorporate this on-site expertise into an AI system, a systematic and inclusive approach is required.
First, we accurately identify the detailed steps of the work process through in-depth interviews with on-site experts. In particular, it is important to record and analyze in detail the criteria and response methods used by experts when equipment malfunctions or quality issues occur.
In this process, field practitioners become key players in the project rather than mere information providers. In the early stages of the project, a group of experts consisting of veteran field workers is formed to directly participate in key decision-making processes, from defining system requirements to performance evaluation.
The expertise of the experts is directly reflected in the algorithm design of the AI system.
For example, when building a quality inspection system, the judgment criteria of experienced inspectors are used as training data for the AI model. The system built in this way is further improved through verification by field practitioners.
In particular, during the system testing stage, field practitioners are responsible for establishing evaluation criteria and identifying areas that need improvement. Through this process, practitioners can develop a sense of ownership for the new system.
Ultimately, the AI system should be seamlessly integrated with existing business processes. In the case of production sites, an interface is implemented that allows workers to check and enter the necessary information immediately while minimizing the number of steps they have to take.
In the case of logistics centers, the AI-based demand forecasting system is designed to seamlessly integrate with the existing inventory management system.
When the expertise of the field is respected, practitioners are actively involved, and the AI system is linked to existing work in a natural way, it can provide real value to the field. This will be the starting point for true innovation that goes beyond the simple introduction of technology and takes the competitiveness of the organization to the next level.
For an AI system to be successfully established, it is essential that it be naturally linked to existing business processes. This is because even a system with excellent performance cannot demonstrate its full value if it disrupts the workflow of the field.
To do this, it is first necessary to analyze the work flow and work patterns in the field in detail. It is necessary to understand in detail the order in which workers perform their tasks, what information they need, and how they make decisions.
For example, in the case of a production site, workers can use mobile devices or tablets to check the results of the AI system's analysis while inspecting the equipment.
At the logistics center, the existing WMS (warehouse management system) and the AI-based optimization engine are organically linked, allowing workers to be guided to the optimal picking route without additional system manipulation.
This approach goes beyond simply integrating technology and means respecting the work culture and practices on site. AI systems should be built in a way that gradually improves and supplements existing work methods, rather than drastically changing them.
As a result, the active participation of field practitioners and the natural connection with existing work processes are essential for the success of AI transformation. This will enable the introduction of technology to be an opportunity for innovation that creates practical value rather than causing confusion in the field.
The most important thing in the transition to AI in manufacturing and distribution is to recognize the expertise of practitioners and to value it in a new way. The experience and know-how accumulated over decades in the field are valuable assets in themselves, and the AI system should be a tool that makes this expertise shine even more.
For example, in the field of quality control, AI automates simple inspection tasks, but strategic decision-making for quality improvement is still the domain of experts. On the contrary, with the help of AI, they can focus on more valuable tasks.
The role of on-site managers is also changing to become more strategic. Based on the data provided by AI, they can establish more sophisticated production plans and maximize equipment efficiency through predictive maintenance, thereby increasing their expertise.
To ensure the success of AI transformation, practitioners should be given sufficient time and opportunities to learn and adapt to new technologies. Beyond simply providing technical training, practitioners should be supported to develop new capabilities based on their expertise.
It is also important to provide specific career paths for practitioners. For example, quality inspectors should be shown a clear path to becoming operators of AI-based quality management systems and even quality improvement strategists.
In order to encourage active participation and contribution from practitioners, a system for formally recognizing and rewarding such participation and contribution is necessary. A system should be put in place to objectively evaluate the level of participation in the process of building an AI system, suggestions for improvement, and the results of on-site application, and to reflect such evaluation in personnel evaluations and promotions.
It is also important to share the results of productivity improvement and cost reduction with practitioners as a result of introducing an AI system. This is an important motivator that goes beyond simple monetary rewards and makes them feel proud to be the agents of change.
To successfully lead the AI transformation, new leadership is required from decision-makers. Instead of a one-way top-down approach, bottom-up innovation through continuous communication and participation with practitioners is required.
The management team should directly listen to the concerns and suggestions of the practitioners through regular on-site visits and meetings and reflect them in the AI transformation strategy. It is also important to create an environment where practitioners can freely express their opinions and experiment.
A culture that accepts the trial and error and failures that occur during the change process as opportunities for learning is also necessary. This will enable practitioners to be more proactive in trying new things and, as a result, discover more creative and effective ways to use AI.
AI transformation, creating the future of digital innovation with practitioners
AI transformation in the manufacturing and distribution industries is no longer an option, but a necessity. This is because the introduction of AI technology has become a task directly related to the survival of companies in the face of intensifying global competition and stricter ESG regulations.
AI transformation in the manufacturing and distribution industries is no longer an option, but a necessity. This is because the introduction of AI technology has become a task directly related to the survival of companies in the face of intensifying global competition and stricter ESG regulations.
However, the success of AI transformation depends not on the technology itself, but on the acceptance of practitioners who use it in the field. Expertise and know-how accumulated over decades in the field are key factors that determine the performance of AI systems, and true innovation cannot be achieved without the active participation of practitioners.
Decision-makers should recognize the role of practitioners as innovators, not just users of the system, as they promote AI transformation. It is important to reflect the experience of practitioners in AI systems and support them in further developing their expertise through new technologies.
A phased approach is required for this. A strategy that prioritizes through on-site diagnosis, verifies through pilot projects, and gradually expands by reflecting feedback from practitioners is effective.
A system should also be established to formally recognize and reward the participation and contributions of practitioners in this process.
In addition, it is important to provide opportunities for practitioners to learn new skills and grow. They should be convinced that the introduction of AI systems can be an opportunity for personal and organizational development rather than a threat.
Ultimately, the success of AI transformation begins with the harmony between technology and people. When decision-makers respect the expertise of practitioners and create innovation together with them, our company will be able to achieve true digital innovation. This is the future of AI transformation that we should pursue.
.svg)
