Supply Chain Network Modeling

Companies start by evaluating their current or base state. Analysts gather information surrounding  

Current and potential new locations of facilities, supply sources, and markets 

Current and future consumer demand for their products and services 

Revenue generated from products and services for the various locations 

companies consider a number of scenarios. By evaluating the current and possible new supply chain designs, management can answer questions such as  

---

In the present work, we focus on the operational planning and control of integrated production/distribution systems under product demand uncertainty. For the purposes of our study and the time scales of interest, a discrete time difference model is developed. The model is applicable to networks of arbitrary structure. To treat demand uncertainty within the deterministic supply chain network model, a receding horizon, model predictive control approach is suggested. The two-level control algorithm relies on a... 

Such a supply chain network easily adds up to tens of thousands of nodes and edges with which the product relations are described, whereby a node can represent raw material, an intermediary product or a final product. An edge represents the relationship between two products. As there are usually predecessor/successor relations, the relation network can be interpreted as a directed graph. The material flow is modelled in form of an edge, material factors and offset times are stored as attributes [3,10, 23, 25, 33]. 

Several simulation-based optimization models in the context of supply chain management can be found e.g. in the area of supply chain network optimization (Preusser et al. 2005) or to simulate rescheduling of production facing demand uncertainty or unplanned shut-downs (Tang/Grubbstrom 2002 Neuhaus/Giinther 2006). A basic approach of simulation-based optimization is presented by Preusser et al. 2005, p. 98 illustrated in fig. 24. 

Preusser M, Almeder C, Hartl RF, Klug M (2005) LP Modeling and Simulation of Supply Chain Networks, In Gunther HO, Mattfeld DC, Suhl L (eds) Supply Chain Management und Logistik Optimierung, Simulation, Decision Support. Physica, Heidelberg, pp 95-114... 

A. Nagurney, J. Dong, and D. Zhang, A Supply Chain Network Equilibrium Model, Transportation Research Part E, 38, 281-303 (2002). 

The main goal of the model was to understand the impact of capacity changes in the system on the supply chain. The model solution recommended changes in the network—a 20% reduction in the number of distribution centers, an 8% increase in the return on assets, and an improvement in the customer service offered, while decreasing inventory. An interesting component of the model was its ability to quantify the impact of managerial choices on the supply chain that were different from the optimal solution. 

The eSCM-I procedure uses the SCOR model for purposes of process standardization. This standardization step plays an essential role as a coordination mechanism to manage interdependencies within a supply chain network. 

Today industry is seeking new pathways to competitive positioning and ways to driving business models forward. Currently, many models exist, and new additions like the e-services built around e-supply chain networks are increasingly targeting meeting customer needs. These models still lack a customerization (one-on-one business-to-individual-customer relationship) approach, and consequentlyneed further enhancements. Service value networks offer a comprehensive pathway towards enhanced competitiveness. 

To put every scenario on a common basis, CGR Management Consultants and SFI developed a common cost model. Its purpose was to eliminate these differences when evaluating the underlying changes in location. The model, called SITELINK, used a structure that is adaptable for supply chain network optimization modeling. Figure 44.1 is a simplified stmcture of the SITELINK model. 

ATP Execution Scope A supply chain network consists of supply network, manufacturing network and distribution network. ATP execution scope refers to the extent of the supply chain elements included within an ATP model or system s domain of interest. Distribution ATP (d-ATP) considers a distribution network including distribution centers and retailers across a supply chain Make ATP (m-ATP) considers both the distribution network and the manufacturing network and Supply Chain ATP (sc-ATP) considers the entire supply chain including distribution network, manufacturing network and supplier network. 

Depending on where the push-pull boundary is positioned and how many echelons are considered in a supply chain network, different ATP strategies can be used by structuring push-based and pull-based ATP models properly. In general there will be a push component and a pull component, but the importance and complexity of the underlying models will vary by context. 

In a complex supply chain network, multiple business units including retailers, distribution centers and factories may be considered in an ATP search. These business units can be dispersed geographically. Hence, an intuitive method for ATP search is based on distance (or lead-time) between inventory location and customer order location. The search process may also be based on a pre-assigned location priority scheme, or on the echelon-level of the business units, e.g. retailer level, distribution center level, manufacturer level. Considering a complex supply chain with multiple distribution centers and factories, Jeong et al. (2002) proposed a global search model to find avail-... 

An important distinction, however, between Supply Chain Engineering (SCE) and Supply Chain Management (SCM) is the emphasis in SCE on the design of the supply chain network and the use of mathematical models and methods to determine the optimal strategies for managing the supply chain. 

This chapter focuses on location and distribution strategies for designing and operating the supply chain network. It begins with an introduction to the formulation of integer programming models, in particular the use of binary (0-1) variables in modeling real-world problems. 

Next we apply these integer programming models to different supply chain network optimization problems, including warehouse location, network design, and distribution problems. The topic of risk pooling or inventory consolidation is presented next. In this portion of the chapter. 

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. 

Integer programming (IP) models with binary variables have been successfully used in practice to design supply chain networks. Hence, we begin this chapter with a review of modeling with binary variables. We will then apply the IP models for location and distribution decisions in supply chain management. 

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. 

The stage 2 analysis included capacity expansions in production and distribution facilities already considered by the management. Close to a dozen new production lines were planned within a 2 year horizon. Although management had already decided on their locations, multiple ophons were allowed in the model to confirm their choices. The results showed that the majority of the chosen locations were optimal. Although the locations were the best to maximize profit, the supply chain network was capable of meeting only 85% of the projected demand. 

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

The optimization models discussed in this chapter had a single objective— either to minimize supply chain costs or to maximize supply chain profitability, in case the product prices vary by location or customer. However, customer demand fulfillment and service are also important in designing a supply chain network. More recently, supply chain risk is emerging to be another important criterion (Supply chain risk is discussed in detail in Chapter 7). Hence, recent applications of optimization models have used multiple criteria optimization models for decision making. 

---