Visual Factory

Image by Creative Safety SupplyOpens in new window

With little or no slack to absorb disruptions, successful execution in a lean environment requires that workers and decision makers be constantly up to date with the conditions in the work environment.

One way lean organizations achieve this is through an approach called Visual Factory, a method to communicate vital information in a workplace.

Visual Factory is a combination of signs, charts and other visual representations of information that enable the quick dissemination of data within a lean manufacturing process.

The visual factory attempts to reduce the time and resources required to communicate the same information verbally or in written form, as both are viewed as a “waste” within the framework of a lean manufacturing process.

The objectives of the visual factory are to:

  • help make problems visible,
  • help employees stay up to date on current operating conditions, and
  • communicate process improvement goals.

With the visual factory, problems can be made visible through the use of charts displayed throughout the workplace that plot trends related to quality, on-time delivery performance, safety, machine downtime, productivity, and so on.

In addition, visual factories make use of production and schedule boards to help employees stay up to date on current conditions. It should be noted that the concept of a visual factory is equally applicable to services. For example, a call center one of the authors visited had a board that displayed updated information on the percent of calls that were answered within the desired time frame.

  1. Lozano, S., Onieva, L., Larraneta, J., & Teba, J. (1993). A neural network approach to part-machine grouping in GT manufacturing. In Proceedings of Robotics (pp. 619 – 624). Mechatronics and Manufacturing Systems.
  2. Arizono, I., Kato, M., Yamamoto, A., & Ohta, H. (1995). A new stochastic neural network model and its application to grouping parts and tools in flexible manufacturing systems. International Journal of Production Research, 33(6), 1535-1548.
  3. Arkat, J., Saidi, M., & Abbasi, B. (2007). Applying simulated annealing to cellular manufacturing system design. International Journal of Advanced Manufacturing Technology, 32, 531-536.
  4. Burke, L., & Kamal, S. (1992). Fuzzy ART for cellular manufacturing. In Proceedings of the Conference on Artificial Neural Networks in Engineering, I, (pp. 779-784).