JOB SHOP SCHEDULING MODEL PROPOSAL TO MINIMIZE TARDINESS IN CV CIPTA SINERGI MANUFACTURING

CV Cipta Sinergi Manufacturing (CSM) is a conventional and CNC-CAD-CAM machining <br /> <br /> company which produces various machining products such as machine components and spare <br /> <br /> parts. CV CSM uses make-to-order system, in which production will be done based...

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Bibliographic Details
Main Author: ATIYA NIM 13414105, HANAYA
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/27673
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Institution: Institut Teknologi Bandung
Language: Indonesia
Description
Summary:CV Cipta Sinergi Manufacturing (CSM) is a conventional and CNC-CAD-CAM machining <br /> <br /> company which produces various machining products such as machine components and spare <br /> <br /> parts. CV CSM uses make-to-order system, in which production will be done based on <br /> <br /> customer’s order. Orders have to be finished in a particular due date. As a make-to-order <br /> <br /> company, CV CSM faces challenge in fulfilling customer’s due date on time. In 2017, 57,8% <br /> <br /> of orders were unable to be finished on time with 9,67 day of delay in average. One of the main <br /> <br /> factors that causes delay in CV CSM is poor scheduling process. Scheduling is done manually <br /> <br /> merely by relying on worker’s judgment without using proper method. <br /> <br /> This study aims to develop job shop scheduling model to minimize tardiness in CV CSM. <br /> <br /> Proposed model is based on linear programming. It has minimization of total tardiness as its <br /> <br /> objective function and considers recirculation constraint. Proposed model is based on linear <br /> <br /> programming. Model computation is performed using LINGO software. <br /> <br /> Proposed model is tested using historical data from CV CSM in the first week of March 2018. <br /> <br /> Proposed model results in 36,36 hour decrease of total tardiness (from 55,44 hour to 19,08 <br /> <br /> hour), or 65,58% from its total tardiness in historical data.