This is an open access article distributed under the CC BY 4.0
Volume 20 article 1006 pages: 1009-1015
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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