Manufacturing industry robotization is spreading into wider range of processes. Determination if robotization is suitable for the company is one of the most critical issues before selecting industrial robot and designing the robot cell. A survey was carried out among Estonian small and medium sized manufacturing enterprises (SMEs) for this study to determine the utilization of industrial robot (IR) in the industry. More specific study of production unit was conducted, using gathered information, to estimate how the objectives of the production cell design were achieved. The aim of the present scientific work is to map the knowledge whether robotization is suitable or not for the company or working processes and to appoint parameters obtained after using the robot cell for practical manufacturing processes. The study results comprise the suitability assessment method with the set of criteria and key performance indicators (KPIs), that best describe implemented production unit profitability and help SMEs to gain additional economic-technical information for future robot-based unit development.
1. Miller, R. K. Industrial Robot Handbook. Springer, New York, 1989.
https://doi.org/10.1007/978-1-4684-6608-9
2. Nof, S. Y. Handbook of Industrial Robotics, Second Edition. John Wiley & Sons, New York, 1999.
https://doi.org/10.1002/9780470172506
3. Koren, Y. Robotics for Engineers. McGraw-Hill Book Company, New York, 1985.
4. New Robot Strategy. Japan’s Robot Strategy. Vision, Strategy, Action Plan. https://www.meti.go.jp/english/ press/2015/pdf/0123_01b.pdf
5. World Robotics Report 2016. International Federation of Robotics. https://ifr.org/ifr-press-releases/news/world-robotics-report-2016
6. Kangru, T., Riives, J., Otto, T., Pohlak, M., and Mahmood, K. Intelligent Decision Making Approach for Performance Evaluation of a Robot-Based Manufacturing Cell. In Proceedings of the International Mechanical Engineering Congress and Exposition, November 9–15, 2018, Pittsburgh, Pennsylvania, USA. https://asmedigitalcollection.asme.org/IMECE/proceedings-abstract/IMECE2018/52019/V002T02A092/276341
7. Pfeiffer, J. Fundamentals on Decision-Making Behavior. In Interactive Decision Aids in E-Commerce. Physica-Verlag HD, Berlin, Heidelberg, 2011, 15‒45.
https://doi.org/10.1007/978-3-7908-2769-9_2
8. Zhang, L., et al. Using Neural Network to Evaluate Construction Land Use Suitability. In Proceedings of the 2010 Second International Workshop on Education Technology and Computer Science, March 6‒7, 2010, Wuhan, China. https://ieeexplore.ieee.org/document/5459823
https://doi.org/10.1109/ETCS.2010.446
9. Saaty, T. L. The Analytic Hierarchy Process. McGraw-Hill, New York, 1980.
https://doi.org/10.21236/ADA214804
10. Kaganski, S., Majak, J., and Karjust, K. Fuzzy AHP as a tool for prioritization of key performance indicators. Procedia CIRP, 2018, 72, 1227−1232.
https://doi.org/10.1016/j.procir.2018.03.097
11. Paavel, M., Karjust, K., and Majak, J. Development of a product lifecycle management model based on the fuzzy analytic hierarchy process. Proc. Est. Acad. Sci., 2017, 66 (3), 279−286.
https://doi.org/10.3176/proc.2017.3.05
