Supply Chain Network Design

Supply chain network design must consider several factors. Some obvious elements are the number and locations of suppliers, manufacturing and assembly plants, distribution centers, and warehouses. Yet, there is still more to consider. 

Supply chain professionals need to consider the customer in the design. For example, it is important to know who the customers are and their buying behaviors. Ultimately, businesses are in business to satisfy the customers. It is imperative to find out what is important to them. In supply chain design, the answers to the following questions are crucial. 

How long are customers willing to wait for products and services  

What service level are customers willing to accept  

Of course, the cost to deliver a product or service is important. What cost is the organization willing to incur to deliver a product or service at the service level customers will accept What are they willing to pay for the level of service they desire If a network is designed to achieve high service levels, this could increase costs to a level where the revenue collected will not cover them. 

Since warehouses can be built of different capacities and cost, we shall consider a multi-state supply chain network design problem, which determines not only the locations of the warehouses but their right capacities to meet the customer demand. 

At each of the potential sites, the company can build a warehouse in three different sizes—small, medium, or large. The investment cost and capacities at the two warehouse sites are given in Table 5.6. The company can build a warehouse at either locations or both. However, it cannot build more than one warehouse at each location. In other words, the company cannot build both a large and a mediiun size warehouse at the same location. The customer demands must be met and are given in Table 5.7. The unit costs of shipping XYZ s product from the factory to the warehouse sites and from each warehouse site to the customer are given in Table 5.8. 

Note that the construction costs are lower at site 2, but its distribution costs are higher. The company has a total investment limit of 30,000 for construction. The objective is to minimize the total cost of investment and transportation such that all customers demands are met. 

Similarly we define additional binary variables for 5 and for building medium and large warehouses at site i. 

we have 16 decision variables (6 binary and 10 continuous) 

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Depending on the type of analysis, a DSS may require collecting information from various parts of the company. For example, supply chain network design requires both static and dynamic information from different parts of the company. The static data include plant production rates, locations of the plants, warehouses, and customers as well as warehousing costs and transportation costs, and the dynamic data involve forecasts, orders, and current deliveries. This type of information will not usually be found in one database or one department in a company. 

There are many aspects which effects customer service. Some of them are directiy influenced by the structure of the distribution network [3] response time, product variety, product availability, customer experience, time to market, order visibility, and retumability. These factors, as explained below, affect supply chain network design decisions. 

Watson M, Lewis S, Cacioppi P, Jayaraman J (2012) Supply chain network design applying optimization and tmalytics to the global supply chain. FT Press... 

Consider the earlier example, but include the possibility of closing plants and warehouses given fixed costs associated with each facility. During supply chain network design, such facility decisions will have to be made... 

Facitationof Supply chain Network design Defnitionof Inventory Definition of best Establishment Evaluation of new... 

Next we present some basic results in continuous location models and how they relate to supply chain network design. We conclude the chapter by discussing several real-world applications of integer programming models used successfully in supply chain network design and distribuhon problems. 

Next, we discuss an important aspect of supply chain network design, called Risk Pooling. Risk pooling refers to the use of a more consolidated distribution network with fewer facilities, each serving a large allocation of customer demand. A consolidated distribution system reduces supply chain costs—inventory holding cost (IHC), order costs, and facilities cost. However, customer service suffers, as time to fulfill customer demand increases. We will study the tradeoff between supply chain cost and customer service under risk pooling. 

Constraints similar to Equation 5.1 arise in supply chain network design, where a company has the option to build warehouses of different capacities. In that case, x, is the quantity of product x stored at that location, xx is the square footage occupied by one unit of product x and by by b are the three potential warehouse capacities. Using binary variables, one for each b value, we can represent Equation 5.1 as a linear constraint. Define 5i, 82, 83 as the binary variables such that when 8j = 1, the RHS value is by Then Equation 5.1 can be written as. 

By including the cost of building a warehouse at location j as K, we will minimize the total cost of building warehouses such that every customer region can be supplied by at least one warehouse. We will illustrate this with Example 5.3 in the next section. In addition to the warehouse location problem. Section 5.2 will also include other examples in supply chain network design and distribution problems using binary variables for modeling. 

Chopra and Meindl (2010) suggest that supply chain network design encompasses four phases Phase 1— Supply Chain strategy. Phase 2— Regional facility strategy. Phase 3—Desirable sites. Phase 4— Location choices. They also suggest a number of factors that enter into these decisions. We discussed models and methods for Phases 3 and 4 until now. We shall now address the first two phases. 

There are several published results of real-world applications using IP models for supply chain network design. We discuss briefly a few of the applications in practice. For interested readers, the cited references will provide more details on the case studies. 

Additional details on the MILP model are available in Chapter 8 as an illustrative case study for global supply chain network design. 

Portillo, R. C. 2009. Resilient global supply chain network design optimization. PhD dissertation. The Pennsylvania State University, University Park, PA. 

We will use RvaR( ) and RmitC ) this section to denote the combined risk values. Note that the term "supplier" could be easily replaced by "geographic region" in the context of supply chain network design optimization, where risk can be explicitly included as part of the criteria functions for locating plants and warehouses. 

Supply chain network design (location of plants and warehouses)... 

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Akbari AA, Karimi B (2015) A new robust optimization approach for integrated multi-echelon, multi-product, multi-period supply chain network design under process uncertainty. Int J Adv Manuf Technol 79 229-244... 

Mohammadi Bidhandi H, Yusuff RM, Megat Ahmad MMH, Abu Bakar MR (2009) Development of a new approach for deterministic supply chain network design. Eur J Oper Res 198 121-128 Moncayo-Martmez LA, Zhang DZ (2011) Multi-objective ant colony optimisation a metaheuristic approach to supply chain design. Int J Prod Econ 131 407-420... 

Pan F, Nagi R (2013) Multi-echelon supply chain network design in agile manufacturing. Omega 41 969-983... 

Ramezani M, Kimiagari AM, Karimi B (2014) Closed-loop supply chain network design a financial approach. Appl Math Model 38 4099-4119 Ratha PC (2014) Towards concurrent product and supply chain designing a review of concepts and practices. Int J Procure Manag 7 391... 

Tiwari A, Chang PC, Tiwari MK (2012) A highly optimised tolerance-based approach for multistage, multi-product supply chain network design. Int J Prod Res 50 5430-5444 Ulkii S, Schmidt GM (2011) Matching product architecture and supply chain configuration. Prod Oper Manag 20(1) 16-31... 

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