Computer-aided system for modelling machinery procurement due-date prediction in production industries

Abstract

Machinery procurement is carried out in both government and private sectors. One of the major criteria for the selection of vendors is their ability to meet the due-date of supply. Failure to meet the supply due-date will affect installation time, commissioning time, as well as start- up date negatively. The set target for the period will be difficult to meet and working overtime to recover the lost period will cause over-flogging of machine, increase in wear rate which can lead to the life-span reduction of the machine. To arrest this situation, a stochastic model was developed to predict machinery/equipment procurement flow time and set due-date for the supply of the machine by the vendor. The model utilized data on the procurement activities which were analysed based on optimistic, most likely and pessimistic time for each activity. The stochastic model used Project Evaluation and Review Techniques (PERT) for analysing these three time estimates from which expected time was predicted. The model accommodates procurement strategic decisions, flow chart development, data collection form, network analysis, activities paths determination and critical path identification. This research was able to develop a model for machinery procurement, develop computer software for implementing the model and validate the effectiveness of the model using an existing company as case study. The expected completion time is 45 days, with variance of 2 days while the probability of supplying the machine in not more than 2 days is 86%. This model will find application in small, medium and large scale industries in both developing and developed countries.