Logistics Make-To-Order Manufacturing for Time

Logistics

Make-to-Order Manufacturing for Time Windows in Delivery

Certainty of product definition, accuracy of time window estimates, prerequisite supply inventory availability, and stability of supporting production and supply networks taken together are the constraints that define time windows optimization for make-to-order manufacturing. Wolsey points out that "For this problem, two variants are considered: in the first each order is distinct (client specific), whereas the second orders are indistinguishable (non-specific)" (471). When a product definition is tightly defined through the make-to-order process supply chains are more agile and callable to react (Guiffrida, Jaber, Rzepka, 2008). Worley (471) and others (Guiffrida, Jaber, Rzepka, 2008) state that to the extent there is indistinguishability in product definition there are corresponding stresses on the supply chain, with delivery lead time being the most at risk (147, 148).

Intermediating the level of product certainty vs. ambiguity, supply chain prerequisites by most often built make-to-order product configurations, and lead times of suppliers for aberrant or unpredictable product orders is measured in delivery performance and equated to costs (Guiffrida, Jaber, Rzepka, 2008). Cost-based performance metrics that take into account excess inventory levels, penalties for late deliveries, and the relative performance of serially-based make-to-order supply chains vs. those that are more network-based leads to a series of assumptions regarding modeling improvement deliveries (149). The series of assumptions for the model Guiffrida, Jaber, and Rzepka present align with the concepts of ElHafsi (2000) and Wolsey's Theorem 8 (477) in that the assumption is made of a unitary or even distribution of delivery timeframes. Arguably supply chains do not have this level of consistent, unitary and predictable performance, as the studies of lead time and price quotation in congested manufacturing systems completed by Hadas, Cyplik, and Fertsch illustrate from empirical analysis (356, 357).

Make-to-order manufacturing scheduling for time window constraints then is not as predictable as models suggest. Alleviating variation in performance through unitary-based models (Guiffrida, Jaber, Rzepka, 2008) is only possible with convex transportation networks as defined by Worley (473) are used to distribute risk throughout the supplier network. Dynamic lot sizing, warehouse optimization and theory of constraints modeling as defined by Jaruphongsa, Cetinkaya, and Lee (169) attempt to resolve reduction in delivery variance that the model and cost-based metrics of Guiffrida, Jaber, and Rzepka (148) attempt to resolve. Yet none of these seek to model external demand. The attempts of Worley to define optimization algorithms for indistinguishable orders, non-inclusive time windows or stock upper bounds (479) eventually delivers an algorithm that optimizes to a specific theories of constraint matrix (484) which holds constant supply chain variability and lot sizes. 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).