Logistics Make-To-Order Manufacturing for Time Research Proposal

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In fact it would have been better to have included convex transportation networks (473) and a corollary for just-in-time inventory management and constraints-based inventory optimization for make-to-order manufacturing (Hadas, Cyplik, Fertsch, 2009).

Product definition ambiguity, manufacturing scheduling variations, supply chain incremental costs due to lack of accuracy in the form of rush charges and inventory management costs and the latency of serial vs. network-based supply chains for make-to-order manufacturing define the models used for cost-based metrics according to benchmarking analysis by Guiffrida, Jaber and Rzepka (150). Make-to-order manufacturing's profitability needs to be defined more thoroughly as a metric, with Guiffrida, Jaber, and Rzepka having set the foundation for this analysis (149). Theories of constraint applied to the wide variation in product certainty vs. ambiguity, presence or absence of serially-based vs. convex network-based supply networks, stock upper bounds and the often-used assumption of unimodal production costs all need greater clarification in the form of cost and time-based metrics.

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Included in these metrics must be a demand component, which is ironically absent from models seeking to optimize make-to-order manufacturing workflows. The exception being the models of price quotation as defined by ElHafsi (357) and defining cost constraints of delivery variance (Jaruphongsa, Cetinkaya, Lee, 2004). Applying the Theory of Constraints (TOC), linear programming and pricing models to optimizing time windows for make-to-order manufacturing requires integration to financial systems and balanced scorecards (BSC) so the insights gained can be used to ensure a manufacturer can benefit from this analysis. With greater integration to financial systems the unimode pricing models and unitary or simple distribution of supply chain reaction times will eventually reflect the unique characteristics of a given manufacturers" supplier base. Quantifying the reduction in time window scheduling errors (152) can also serve as a means to validate and fine-tune model assumptions (Guiffrida, Jaber, Rzepka, 2008).....

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