LEAD TIME PREDICTION FOR SHEETER MACHINE PRODUCTION IN A PAPER CONVERSION INDUSTRY

Muhammad Talha  Siddique; Muhammad Dawood Idrees; Atif Jamil; Arsalan Ansari; Abdul Sami; Muhammad Rauf

doi:10.5937/jaes0-36919

Muhammad Talha Siddiqui Department of Industrial Engineering & Management, Dawood University of Engineering & Technology, Karachi, Pakistan
Muhammad Dawood Idrees Department of Industrial Engineering & Management, Dawood University of Engineering & Technology, Karachi, Pakistan
Atif Jamil Department of Computer Systems Engineering, Dawood University of Engineering & Technology, Karachi, Pakistan
Arsalan Ansari Department of Electronics Engineering, Dawood University of Engineering & Technology, Karachi, Pakistan
Abdul Sami Department of Electronics Engineering, Dawood University of Engineering & Technology, Karachi, Pakistan
Muhammad Rauf Department of Electronics Engineering, Dawood University of Engineering & Technology, Karachi, Pakistan

DOI: https://doi.org/10.5937/jaes0-36919

Keywords: lead time prediction, manufacturing execution system, machine learning, paper conversion industry, simulation

Abstract

Lead time is a critical performance measure in any manufacturing setting Key Performance Indicator (KPI). The same is true in the paper conversion industry, which has a significant degree of product variability. Due to the great variety of their products, all industries must be able to foresee and plan ahead in order to meet client demand. With contemporary research concentrating on machine learning and simulation techniques, businesses must implement a manufacturing execution system (MES) to track data. However, without such a framework, applying machine learning and simulation approaches becomes difficult. This study introduces a novel method for forecasting lead time (special to sheeter machines used in the paper conversion sector) by combining the time required to process the reel (sheeting time) with the human (setup) elements. The method used to calculate the sheeting time takes product parameters into account, allowing for product-specific lead time forecast. As a result, a very successful 'product-specific' lead time prediction approach for small scale enterprises has been developed that enables production planning without relying on current and data-intensive prediction methods such as machine learning and simulation.

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