Raw Material Risk Prediction Tool

Abstract

Apparel Manufacturing is considered to be one of the most important sectors in a country like Sri Lanka especially when the country’s economy depends on exports. With the introduction of Industry 4.0, the Apparel manufacturing sector is moving towards data driven decision making culture in order to improve the quality and cut down costs in order to be competitive. Due to the sheer volume of the daily transactions processed on a daily basis over multiple data platforms, the apparel manufacturing sector data will be important to derive meaningful insights that will enable to make proactive decisions to improve the performance of the sector. Material Risk is a significant aspect in terms of cutting down the lead time and minimizing unnecessary expenditures in the apparel manufacturing industry. The apparel manufactures will have to bear the cost if there are is any reordering materials due to material issues. In the current context there is not much solutions available for the apparel to predict the material risk beforehand. Hence, introducing a data driven material risk prediction solution that could beneficial to the apparel manufacturing industry.

When creating a data driven solution, it is mandatory to have an understanding of the Apparel Manufacturing domain. Considering the fact, there is only a limited number of researches that has focused on Apparel Manufacturing there is less concepts published, this study has used a dataset from the ERP of a leading apparel company in Sri Lanka. This was done to get a real-life scenario of an Apparel Manufacturing organization to predict the martial risk based on past experiences of the company. The data was pre-processed using statistical concepts to ensure accuracy and mitigate the biasness of the dataset. Concepts such as logistic regression and decision trees have been used mainly for this study. The first time through percentage was assessed in two instance and was broke down to offer methods for improving first time through percentage activities with the utilization of data. The research determines a prediction of materials to improve supply chain in the apparel manufacturing industry.