A method for selecting processes for automation with AHP and TOPSIS

Area of Science:

• Business Process Management
• Artificial Intelligence
• Decision Science

Background:

• Organizations increasingly use robotic process automation (RPA) to delegate routine tasks.
• Effective process selection is critical for successful RPA implementation.
• Current methods for identifying automation-suitable processes are often inadequate, leading to failures and technology avoidance.

Purpose of the Study:

• To propose, demonstrate, and evaluate a novel method for selecting business processes for automation.
• To enhance the accuracy of identifying suitable candidates for robotic process automation.
• To improve the overall success rate of RPA implementations within organizations.

Main Methods:

• Utilized the Design Science Research Methodology (DSRM).
• Combined two multi-criteria decision-making techniques: Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).
• Applied the proposed selection method to a real-life business scenario.

Main Results:

• A validated method for selecting processes for automation was developed.
• The combined AHP-TOPSIS approach demonstrated effectiveness in identifying suitable automation candidates.
• The study provides a structured approach to mitigate risks associated with incorrect process selection.

Conclusions:

• The proposed AHP-TOPSIS method offers a robust solution for selecting business processes for automation.
• Accurate process selection is key to overcoming challenges and improving the reputation of RPA.
• Implementing this method can significantly increase the success of robotic process automation initiatives.