Performance Evaluation of Machine Learning App Approach to Modular Arrangement of Predetermined Time Standard

Merging Modular Arrangement of Predetermined Time Standard (MODAPTS) and techniques used in the fourth industrial revolution (4IR) such as Machine Learning (ML) can start to improve the user experience of time standards. This study used Artificial Neural Networks (ANN) applied as chatbots to see whether ML could indeed improve MODAPTS in terms of ease, pace, and accessibility. The conventional and ANN methods were compared with assistance from logistics engineers, and the ANN approach. A chatbot using ANN was created and packaged on an html page presented to the research participants making use of a mobile device. The experiment used five material handling written scenarios to emulate the observation process, looking at the traditional approach when conducting a MODAPTS time study then followed by the ANN solution making use of the chatbot. ANN was found to be 0.25 min faster at a prediction rate of over 90% when the chatbot was in use. The result showed that machine learning could indeed be used with MODAPTS to equal performance and potentially improve the use of the time standard. The neural network was able to accurately predict the MODAPTS code of 94.7% of the 262 activities entered by the research participants. The potential to add other ML learning techniques and time study methods exists, the template is flexible enough to be moulded into a tool that all engineers can adapt in their different working environment.