{"id":{"repo_id":"ohiolink","oai_identifier":"oai:etd.ohiolink.edu:kent1299095930"},"canonical_url":"https://search.dev.ndltd.org/etd/ohiolink/oai:etd.ohiolink.edu:kent1299095930","repository":{"repo_id":"ohiolink","name":"OhioLINK","base_url":"https://etd.ohiolink.edu/acprod/odb_etd/ws/oai/oai"},"display":{"title":"COST REDUCTION THROUGH ASSEMBLY POSTPONEMENT IN MASS CUSTOMIZATION","abstract":"This research focuses on the assembly postponement strategy to reduce costs in mass customization scenarios. Assembly postponement is a technique of delaying the product differentiation point when assembling multiple items from common components at the beginning of the production process. We extend Lee and Tang's (1997) assembly postponement network model by accounting for supply uncertainty and customer service levels agreement. A Bayesian Belief Networks (BBNs) approach is used to calculate inventory levels and costs resulting from customer service level requirements. To illustrate our model, we use the customization of a desktop PC as an example. The results from this dissertation provide several managerial and practical implications. First, our study account for customer service levels satisfaction in mass customization scenarios. Second, we show how practitioners can minimize operational costs for customized goods while accounting for supply uncertainty.","abstract_html":"This research focuses on the assembly postponement strategy to reduce costs in mass customization scenarios. Assembly postponement is a technique of delaying the product differentiation point when assembling multiple items from common components at the beginning of the production process. We extend Lee and Tang's (1997) assembly postponement network model by accounting for supply uncertainty and customer service levels agreement. A Bayesian Belief Networks (BBNs) approach is used to calculate inventory levels and costs resulting from customer service level requirements. 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Assembly postponement is a technique of delaying the product differentiation point when assembling multiple items from common components at the beginning of the production process. We extend Lee and Tang's (1997) assembly postponement network model by accounting for supply uncertainty and customer service levels agreement. A Bayesian Belief Networks (BBNs) approach is used to calculate inventory levels and costs resulting from customer service level requirements. To illustrate our model, we use the customization of a desktop PC as an example. The results from this dissertation provide several managerial and practical implications. First, our study account for customer service levels satisfaction in mass customization scenarios. 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