Lean manufacturing approach to increase packaging efficiency

• Main Authors: GOZALI, Lina, KURNIAWAN, Irsandy, SALIM, Aldo, MARIE, Iveline Anne, TJAHJONO, Benny, LIANG, Yun Chia, GUNAWAN, Aldy, SIE, Nnovia Hardjo, SUSENO, Yuliani
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Lean Manufacturing; Non-Value Added; Value Stream Mapping; Waste; Computer Sciences; Operations and Supply Chain Management; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8612; https://ink.library.smu.edu.sg/context/sis_research/article/9615/viewcontent/LeanMfg_Packaging_pv.pdf
• Institution: Singapore Management University

The company upon which this paper is based engages in flexible packaging production, especially pharmaceutical products with guaranteed quality, trusted by consumers. Its production process includes printing, laminating, and assembling processes. Production activities are done manually and automatically using machines, so various types of waste are often found in these processes, making the level of plant efficiency nonoptimal. This study aims to identify wastes occurring in the production process, especially the production of pollycelonium with three colour variants as the highest demand product, by applying lean manufacturing concepts. The Current Value Stream Mapping (CVSM) used to map the production process shows the amount of Value Added Time of 107.676 minutes, Non-Value Added Time of 311.450 minutes due to engine displacement and setup, and total lead time of 455.724 minutes with a 23.627% Cycle Efficiency. Transportation in the factory creates the largest waste with a distance of 340 meters. Further analysis using the fault tree analysis method and 5W+1H method results in a layout proposal and minimization of machine setup time activity projected with Future Value Stream Mapping (FVSM) to increase to percentage cycle efficiency 14.05% and decreasing process lead time of 169.939 minutes.