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An integrated model for process selection and quality improvement in multi-stage processes
Mustajib M.I.a, Irianto D.b
a Manufacturing Systems Laboratory, Department of Industrial Engineering, Trunojoyo University, Indonesia
b Manufacturing Systems Research Group, Department of Industrial Engineering, Bandung Institute of Technology, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]All production processes produce variance around the desired target value of quality characteristic. This variance affects the product quality level. Accordingly variance reduction needs to be done as the main goal of quality improvement programs. However effort to improve quality of each product unit must take into account to improvement costs. This paper proposes an optimization model for quality improvement in multi-stage processes using a non linear programming model by selecting alternatives process and determining unit of production of each stage to maximize profit as the difference between total income and total relevant cost. Total cost includes manufacturing cost, quality loss cost, rework and scrap cost, and quality improvement implementation cost. This optimization model is implemented in make-to-order manufacturer that produces crimper (a parts of joining plastic packages in packaging machine) which consist of five main stage manufacturing processes. Sensitivity analysis shows that the optimal solution is not sensitive if little changes occur in the constraints scenario. Thus, adding the value constraint on the quality specification, stage capacity, and quality improvement budget will not improve the objective function. © 2010 World Scientific Publishing Company.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Implementation cost,Integrated models,Make to order,Manufacturing cost,Manufacturing process,Multistage process,Objective functions,Optimal solutions,Optimization models,Plastic packages,Process selection,Product quality,Product-unit,Production process,Quality characteristic,Quality improvement,Quality improvement program,Quality loss,Quality loss cost,Quality specifications,Target values,Total costs,Variance reductions[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Process selection,Quality improvement,Quality loss,Tolerance[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1142/S0219686710001788[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]