AI in Lean Management: How Technology Supports Continuous Improvement
For companies that want to keep pace with technological change, artificial intelligence in Lean Management is no longer just a novel trend—it has become a necessity. AI-supported Lean Management can achieve its core objectives – eliminating waste and increasing process efficiency –even more effectively. How does artificial intelligence help companies foster a culture of continuous improvement?
How does artificial intelligence fit into the Lean philosophy?
One of the key principles of Lean Management is the elimination of waste in all areas of an organization’s operations. In the traditional sense, this primarily concerns production—quality defects, overproduction, unnecessary transportation, and downtime. However, when looking more broadly at the functioning of the entire organization, waste resulting from information and communication silos is also a major problem. What exactly is digital time waste, and how can AI be used in the optimization of production processes to eliminate this phenomenon?
What is digital time-wasting?
Digital waste (also known as digital muda) occurs when digital tools—which are intended to streamline work—actually generate additional tasks that are not directly related to task completion. So, rather than boosting productivity, they consume employees’ time without creating value for the organization.
Digital time-wasting most often takes the following forms:
How does artificial intelligence fit into the Lean philosophy? An example straight from the production floor
AI-based lean management can have a significant impact on process optimization in manufacturing facilities – however, such tools must be used wisely.
Let’s imagine a scenario in which a company has information and communication management systems in place. The manager starts the day with a stack of messages, one of which contains information about an upcoming audit. The SOP was updated the day before and placed in a folder. The file name? The same as the previous one, with “v4.0” appended. Employees receive a notification about the need to review the new SOP and about the audit, which will take place in 2 days. However, several dozen frontline employees receive 3 notifications around the same time: the first about a change in the lunch break time, the second about the audit, and the third contains instructions to switch to a different process.
In two days, the current knowledge of all department employees will be tested. In theory, leaders used digital tools to convey knowledge (the new SOP) and information (sending the message). In practice—we can guess what the audit result will be…
How could artificial intelligence in manufacturing help improve this process? Let’s start with the fact that in noSilo, employees always have access to the most up-to-date versions of documents. A team leader can send a message that will appear in the News feed on the team’s dashboard. An even better option is to quickly create a mandatory training session on the new SOP—the employee must review the content and answer 1–2 questions about the update. Once they complete it correctly, the information is recorded in the system. With noSilo, an employee can instantly find a new document with a single query, and a leader, also with a single query, can find out who has already acquired the new competency. This is possible thanks to our AI tools: the Knowledge Assistant and the Manager Assistant.
Knowledge Assistant – How to Improve Access to Information?
Artificial intelligence in manufacturing offers significant benefits in the area of organizational knowledge management. In many companies, documentation is scattered and, moreover, lengthy and complex, making it difficult to quickly find the necessary information.
How does the Knowledge Assistant work in noSilo?
The knowledge assistant in noSilo uses artificial intelligence to automatically search an organization’s documents and generate answers to specific questions. After a query is entered, the system analyzes available materials—such as SOPs, machine manuals, procedures, and health and safety documents—and then provides a structured response containing the most important information.
The knowledge assistant provides:
- just-in-time information retrieval from procedures and instructions,
- the ability to locate specific sections of documents,
- references to the sources of information on which the answer is based,
- and concise summaries instead of lengthy instructions.
In the case of our earlier example, an employee can ask the Knowledge Assistant a question about the current SOP and will instantly receive information about the document, along with a link to the file stored in the system.
Assistant Manager – How to Effectively Manage Talent and Close the Skills Gap?
AI in Lean Management also effectively supports team management. Artificial intelligence can significantly streamline tasks related to analyzing employee competencies and planning their development.
How can you quickly prepare for a meeting with an employee or a team?
The Manager Assistant searches for information regarding an employee’s competencies, training history, and job requirements. Based on this, it prepares a clear summary containing key information about the employee and recommendations for development actions.
The Manager Assistant allows you to quickly obtain information regarding:
- the employee’s current competency level,
- missing training or certifications,
- areas requiring development,
- potential career development paths.
How can you use AI to plan succession and career paths?
The manager’s assistant also supports employee development planning and prepares employees for new roles within the organization. Based on an analysis of competencies and job requirements, the tool:
- identifies competency gaps within the team,
- identifies potential candidates for promotion,
- recommends development activities,
- estimates the time needed to acquire the missing competencies.
Data security – are AI assistants in the workplace secure?
The implementation of artificial intelligence within an organization often raises questions regarding data security and control over information. noSilo AI assistants analyze only data located within the organization’s environment. The system does not use the internet or external information sources. All responses generated by the assistants are based solely on documents previously stored on the platform.
Additionally, the scope of the analyzed data is tailored to the user’s permissions—meaning that an employee has access only to information related to their role within the organization.
Lean Management using artificial intelligence thus allows organizations to harness the potential of modern technology to optimize production processes and manage knowledge, while maintaining full control over the security of company data.
FAQ
No. AI in Lean Management serves solely to support managers in data analysis, team development planning, and decision-making. Final decisions and people management remain the responsibility of humans.
The greatest benefits come from using AI in process analysis, knowledge management, identifying waste, and monitoring team competencies. Artificial intelligence speeds up data analysis, helping with information retrieval and meeting preparation.
Yes. AI can support both smaller and larger manufacturing facilities. It helps with knowledge management, data analysis, and process optimization, regardless of the organization’s size.
The key is to consolidate knowledge in a single, central location and to organize processes, data, and documentation. This will enable AI to effectively analyze information and support employees in their day-to-day decision-making.
Bibliography:
https://mfiles.pl/pl/index.php/Lean_management;
https://vade.com.pl/leksykon/muda-zarzadzanie/;
https://www.uj.edu.pl/wiadomosci/-/journal_content/56_INSTANCE_d82lKZvhit4m/10172/156571206.
She has many years of experience in the manufacturing environment, gained both in team management and in the coordination of internal processes. Her practical experience in operational and managerial work has given her a deep understanding of the challenges faced by managers and production employees – from work standardization and communication to competence development.
This experience forms the foundation on which she bases her current expert activity. In her articles, she shows how digital solutions and modern management methods can realistically support the daily work of production plants, making processes more transparent, orderly, and effective.
