Analysis Poin of View - Paper MPO

Paper: 

Response time reduction in make-to-order and assemble-to-order supply chain design

Writer: NAVNEET VIDYARTHIL, SAMIR ELHEDHLI and ELIZABETH JEWKES

Positioning Paper	Present Models for designing make-to-order and assemble-to-order supply chain under Poisson customer demand arrivals and general service time distribution. Motivation: Strategic importance of response time reduction.
Motivation/ Problems to Be solved	1.    The problem is setup as a network of spatially distributed M/G/1 queues, modeled as a non-linear mixed integer program and linearized using a simple transformation and a piecewise linear approximation. 2.    The problem of designing a two-echelon assemble-to-order supply chain comprising of plants and DCs Serving a set of customers in considered.
Objective of the Paper	1.    To model the effect of congestion on the response time 2.    analyze the trade- off acquisition costs, facility Location and (capacity acquisition costs, and outbound transportation costs in  the design of supply chain networks
Critical Review/ Tinjaun Pustaka	1.    Marhretta, 1998: Dell, 2000) Dell Typically offer several lines of product, with each allowing at least dozens of features” from which customers can select when placing an order-different combinations of CPU, hard drive, memory, and other peripherals. In Dell’s Supply chain, multiple component are procures and kept in inventory at various assembly facilities, from which they are assembled into a wide variety of finished products in response to customers order. 2.    Vidal Fall Goetschalcia (2000) present a model that captures the effect of change in transportation Lead time and demand on the optimal configuration of the global supply chain net­work, assuming that the demand is deterministic 3.    Erengriic et all (1999) and Sarmiento and Nagi (1999) also point out that most of the existing supply chain design models do not consider measures of customer service such as response time in making loca­tion/allocation decisions, 4.    Close and Trod (2005). This is not surprising given the complexity of the model and the interplay of locational and queueing aspects of the problem 5.    Models for facility location with immobile servers, stochastic demand and congestion (such as location emergency medical Facilities, fire sta­tions, telecommunication network design, automated teller machines or internet mirror site location). For an exten­sive review; refer to Berman and Krass (2002). Due to the complexity of the underlying problem, most papers in this area make very strong assumptions: (i) either the number or capacity of the facilities or bulb) are assumed to be fixed (ii) the facilities are assumed to be identical; (iii) the demand arrival process is assumed it: be Poisson: and (iv) die service process is usually assumed to be exponential 6.    Most of the techniques proposed to date to solve these problems, with the exception of Elhcdhli (2006), are either approximate or heuristic based. 7.    Our work is also similar in spirit to models for capacity planning with congestion effects, for which only heuristic solution procedures have been reported; see Raja gopalan and Yu (2007) and refer­ences therein.
Metodologi	1.    An exact solution approach is presented that is based on the cutting plane method. 2.    A Lagrangean heuristic is proposed that exploits the echelon structure of the problem and uses solution methodology for the make-to-order problem.
Result and Discussion	1.    It is empirically shown that substantial reduction in response time can be achieved with minimal increase in total costs in the design of responsive supply chains. 2.    A supply chain configuration that considers congestion is proposed and its effect on the response time can be very different from the traditional configuration than ignores congestion.
Kesimpulan	Computational results and managerial insight are provided.