A Comparative Research Upon Various Techniques and Models of Supply Chain Network

Supply chain design (SCD) has been a very visible and influential topic in the field of production, operations, and supply chain management over the past two decades. SCD is a critical source of competitive advantage given that as much as 80% of total product cost may be fixed by these decisions (Harrison et al. 2005). One of the main issues in SCD is supply chain structuring in accordance with a given competitive strategy, supply chain strategy, coordination strategy, distribution strategy, product program, and financial plans (Chopra and Meindl, 2007) as well as with demand and supply uncertainty (Lee et al., 1997, Tsiakis et al., 2001, Santoso et al., 2004). It should be emphasized, that supply chains consist of different structures: business processes, technological, organizational, technical, topological, informational, and financial structures. All of these structures are interrelated and change in their dynamics. The literature on Supply Chain Management (SCM) indicates various multi-structural frameworks that received managerial attention when designing supply chains (Lambert and Cooper 2000; Bowersox et al. 2002). Nowadays, the complexity of the

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business environment is rapidly increasing. This is due to several factors such as the expansion of the market, a wide range of suppliers, increased competition and customers demands on the performance of a company, in particular, the waiting time, cost and quality of the product. Among these factors, if we consider the range of suppliers to the market, it is necessary to design an optimized supply chain model. The supply chain is a complex network from suppliers to customers, which involves people, technologies, activities, information and resources. Its design and management has the purpose of obtaining the best global performances under unions operating criteria. A typical supply chain is composed of the following elements: suppliers, manufacturing plants, warehouses, distribution centres (DCs), customers/final markets. The past decade has seen an increasing recognition of the importance of supply chain management. In industry, the rapid growth of supply chain software companies testifies to the significance that businesses place on the efficient management of their supply chains. Research in this area has become a key focus of the operations management academic community in recent years; a comprehensive review of this literature can be found in Tayur, Ganeshan and Magazine (1998). Supply chain design encompasses a very large number of decisions. Product development, in which product functions and features are determined, dictates certain supply chain features. The set of processing technologies chosen to deliver product functionality specifies some of the necessary supply chain activities. The entities that carry out the activities need to be selected; and may be internal or external to the firm. Willems (1999) studies the tradeoff between cost and lead time in the entity selection problem. Rules and contracts governing entity interaction need to be specified. Willems (1999) provides a brief review of the research on the material quality aspects of supplier manufacturer interactions. Tsay, Nahmias and Agrawal (1998) reviews the recent literature on supply chain contracts. Decisions need to be made on whether the supply chain will be a make-to-stock or make-to-order system, or some hybrid of the two. An entity providing a supply chain activity needs to determine how this activity will be delivered. Will it use a single resource (i.e. plant or machine) or multiple resources? What resource capacity is needed? This list of supply chain decisions is by no means exhaustive but it serves to highlight the complex nature of supply chain design. Supply chain design is often further complicated by decentralized decision-making. Extra complexity arises if the supply chain processes multiple products – aspects such as resource flexibility must be considered.

The supply chain in Figure 1 consists of five stages. Generally, multi-stage models for supply chain design and analysis can be divided into four categories, by modeling approach. In the cases included here, the modelling approach is driven by the nature of the inputs and the objective of the study. The four categories are: (1) deterministic analytical models, in which the variables are known and specified (2) stochastic analytical models, where at least one of the variables is unknown, and is assumed to follow a particular probability distribution, (3) economic models, and (4) simulation models. Cohen and Moon (1990) extend Cohen and Lee (1989) by developing a constrained optimization model, called PILOT, to investigate the effects of various parameters on supply chain cost, and consider the additional problem of determining which manufacturing facilities and distribution centers should be open. More specifically, the authors consider a supply chain consisting of raw material suppliers, manufacturing facilities, distribution centers, and retailers. This system produces final products and intermediate products, using various types of raw materials. Using this particular system, the PILOT model accepts as input various production and transportation costs, and consequently outputs:

• Which of the available manufacturing facilities and distribution centers should be open.

• Raw material and intermediate order quantities for vendors and manufacturing facilities.

• Production quantities by product by manufacturing facility.

• Product-specific shipping quantities from manufacturing facility to distribution center to customer.

The objective function of the PILOT model is a cost function, consisting of fixed and variable production and transportation costs, subject to supply, capacity, assignment, demand, and raw material requirement constraints. Based on the results of their example supply chain system, the authors conclude that there are a number of factors that may dominate supply chain costs under a variety of situations, and that transportation costs play a significant role in the overall costs of supply chain operations.

This project will look at determining optimal configuration

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strategies for new product supply chains. The goal is to develop a decision support tool that product managers can use during the product development process where the product’s design has been fixed, but the vendors, manufacturing technologies, and shipment options have not yet been determined. Our supply chain design framework considers three specific costs that are relevant when designing new supply chains: unit manufacturing cost, safety stock cost, and pipeline stock cost. The supply chain design problem minimizes the sum of these costs when creating a new supply chain. The problem is a design problem because there are several available sourcing options at each stage. Examples include multiple vendors available to supply a raw material and several manufacturing processes capable of assembling the finished product. These different options have different direct costs and production lead-times. Therefore, choices in one portion of the supply chain can affect the costs and responsiveness of the rest of the supply chain. The optimal configuration of the supply chain will choose one option per stage such that the costs of the resulting supply chain are minimized. Single-Stage Single-Option Model - In this section, we present a model for the inventory at a single stage, where there is only one option available at the stage. The single-stage model serves as the building block for modeling a multi-stage supply chain. Since we assume that there is only one option per stage, we can suppress the option-specific index and denote the production lead-time at stage i by Ti. We have already noted that each stage quotes and guarantees a service time Si by which stage i will deliver product to its immediate successor. For a serial supply chain, it must also be the case that stage i is being quoted a service time by its upstream supplier. That is, the inbound service time to stage i is the service time that stage i-1 quotes to stage i. By definition, this inbound service time is equal to Si-1. For the case where i = 1, we assume that S0 = 0; this corresponds to the case where there is an infinite supply of material available to the supply chain. Multi-Stage Multi-Option Serial Supply Chain Model – In this section uses as the building block in order to model the expected safety stock levels and pipeline stock levels across the serial supply chain. The consideration of multiple options at a stage does introduce some additional complexity to the formulation. In particular, we need to explicitly account for the fact that only one option will be selected at each stage.

In this section, we describe the basics of the SCD multi-structural treatment. One of the main supply chain features is the multiple structure design and changeability of structural parameters because of objective and subjective factors at different stages of the supply chain life cycle. In other words, SCD structure dynamics are constantly encountered in practice. Why is the SCD multi-structural treatment so important? First, SCD design decisions are dispersed over different structures. Secondly, the structures and decisions at different stages of supply chain execution change in their dynamics. Output results of one operation are interlinked with other operations (the output of one model is at the same time the input of another model). This necessitates structure dynamics considerations. In the case of disruptions, changes in one structure will cause changes in other relevant structures. Structure dynamics considerations may allow establishing feedback between supply chain design and operations.

In this study, the multi-objective bees algorithm has been used to solve a resource options selection problem for the supply chain design of a bulldozer, taken from. The supply chain design problem is a general problem that concerns the optimal choice of resource options across a supply chain network in order to minimize the total cost and the lead-time simultaneously for a product or a family of products. In this study, the given supply chain is composed of N activities including the sourcing/supplying of each of the components, the assembling of each of the sub-assemblies and final products, and the delivering of each product to its destination market. Each activity can be performed by a different number of resource options(Ni), and each resource option has its own cost and processing lead-time. The total supply chain cost can be calculated by Equation A supply chain can be represented by nodes connected by links representing supply-demand relationships between activities. The activity at a particular node cannot start until all inputs to the node are available, until all preceding activities are completed. For this reason, the cumulative lead-time at a node, expressed by Equation, is the sum of the processing lead-time of the node and the maximum delivery lead-time of all input components:

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Manufacturers often require multiple components when producing a product, with the various components being sourced from different suppliers. In such situations, the manufacturer requires more than one supplier to invest in capacity. In this section, the supply chain is assumed to comprise one manufacturer and N suppliers, as opposed to a single supplier as in previous sections. The suppliers do not produce the same component but rather each produces a distinct component. The manufacturer assembles these components into the end product. As in previous sections, demand for the end product is uncertain. The manufacturer and suppliers must each invest in capacity before demand is realized. After demand is realized, the manufacturer purchases components from each of the suppliers, assembles them into the end product, and then sells them. I analyze two different games in this section, the first in which the wholesale prices are exogenous, and the second in which they are under the control of the manufacturer. In each game all parties are assumed to know all the other parties costs. All parties know the end product demand distribution also.

One of challenges of SCD consists of the number of interrelated structures. Supply chain execution is accomplished by permanent changes of the internal network properties and external environment. We focus on strategic-level issues of the supply chain design and consider it a problem of multi-structural network synthesis. The results show multi-structural and inter-disciplinary treatment of supply chain design allows comprehensive and realistic design problem formulation and solution. The proposed multi-structural treatment also allows establishing links to comprehensive uncertainty analysis and especially to supply chain execution and reconfiguration. The findings suggest how to implement a simultaneous multi structural supply chain synthesis as well as how to encapsulate the structure dynamics into supply chain design and planning. This study introduces an approach to enhance the existing frameworks of the supply chain design by means of simultaneous multi-structural network synthesis considering structure dynamics. In this study, a multi-objective bee’s algorithm-based supply chain design model has been proposed and applied on a supply chain problem. This problem deals with the resource options’ selection for a multi-product and multi-delivery supply chain in order to minimize two objective functions simultaneously, namely, total cost and total lead-time of the network. Several tests have been conducted to find the optimum parameters for the bees algorithm. Subsequently, Pareto front lines have been computed with different weight combinations. The results showed the efficiency of the proposed model. The Pareto solutions of the proposed model have been compared with those obtained by an ant colony optimization. This showed that the bees algorithm is a more powerful tool for finding a better Pareto solution for supply chain problems. A supply chain is defined as a set of relationships among suppliers, manufacturers, distributors, and retailers that facilitates the transformation of raw materials into final products. Although the supply chain is comprised of a number of business components, the chain itself is viewed as a single entity. Traditionally, practitioners and researchers have limited their analyses and scope to individual stages within the larger chain, but have recently identified a need for a more integrated approach to manufacturing system design. Consequently, the supply chain framework has emerged as an important component of this new, integrated approach. There are also several interesting extensions to the part selection problem. First, it will be very useful to extend the model to incorporate an entire product family. The single product assumption is very limiting because it does not capture the role of downgrading in the product development process. When downgrading is present, all but possibly the lowest ranked part will still be available in the next period.

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 Pinto-Varela T, Barbosa-Pøsvoa FD AP, Novais AQ (2011) Bi-objective optimization approach to the design and planning of supply chains: economic versus environmental performances. Comp Chem Eng 35:1454-1468.