Supply chain nodes

In a fixed period of time, when the supply chain node enterprise provides the raw materials, intermediate products and firrished products, the customer demand has a limited growth, so there is a potential limit value K. 

Here is the product demand forecast in 2009 provided by a supply chain node enterprise. In order to calculate K value, we need to select historical demand data of this enterprise during 2000-2008. The actual data is divided into three groups, and each group has three data shown in Table 3.1. 

In fact, in the actual market environment, the supply chain node enterprises may face many kinds of real risks, under these circumstances, the decision-makers need to consider how to maximize supply chain benefits in terms of probabUily. Stochastic chance-constrained programming theory has a practical significance in dealing with such issues. Following is the brief introduction of stochastic chance-constrained programming theory. 

The distribution tier receives customer requirements and is responsible for delivering required products or services. It involves such general units as warehouses, distribution centers, and cross-docking points. These units are grouped into distribution sub-tiers. Alternatively, supply chain units in the distribution tier can be classified as wholesalers, retailers, and brokers. Third-party logistics providers present a special case for belonging to the distribution tier. In some situations, these can be represented by a single supply chain node. 

Chapter 7 incorporates disruption risk in a supply chain network design model. Supply chain network design decisions that determine the number and location of facilities and the selection of transportation modes have a significant impact on competitive performance. However, facilities and transportation links are susceptible to disruptions. In addition, they have different capacities to cope with those disruptions, which contribute to supply chain resilience. This chapter provides a framework to quantify the risk level of supply chain nodes and links. Then, a multiple objective optimization model is presented for designing a resilient supply chain network, with an emphasis on balancing the cost, responsiveness, and risk of the supply chain. 

There ate a number of supply chain nodes where a disruption (intentional or unintentional) could occur within the food supply chaim The supply chain for food is complex due to both the various hand-ofifs throughout the system and its global nature. In 2006, for example, it was estimated that US agricultnral exports wonld equal 68.7 billion, while agricultural imports would approach 64 billion (Economic Research Service 2006). This globalization will continue as worldwide economies rely on each other for agricultural and food productioa... 

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]. 

The review of Knolmayer supports the cross-node communication to ensure collaboration across the chain. Additionally, communication is not only one-directional but bi-directional as well as supply chain does not only cover material and information but also monetary flows (Knol-mayer/Mertens et al. 2000, p. 2). 

Fruit Industry Supply Chains (FISC) are interconnected networks conformed by production nodes (farms), processing plants (fruit packaging and concentrated juice plants), and storage facilities, along with clients and third party raw material and services suppliers. Although Supply Chain optimization is a mature field, very few contributions on FISC modeling with management purposes have appeared so far in the open literature. 

The pathways from local markets are much richer. The map shows that complete pathways run from open markets and the farmer to the terminal value. Good relations, via three functional consequences of these choices of outlet. Social contact. Contact with producers, and Advice or service. Contact with producers is a central node where complete pathways for shopping at open markets and direct from the farmer intersect. Thus Contact with producers can be seen to be integral to short organic supply chains (direct and local markets). But, whereas buying from the farmer connects to value only through Contact with producers, shopping in the open market connects to value via Relationship with staff - Advice, and an abstract attribute, Pleasant atmosphere that is linked to both Contact with producers and Social contact. Thus, three main paths can be... 

Now consider an inventory/distribution network, also known as a supply chain. Each node in the network represents a stocking location. Suppose a base-stock control policy is followed at each node. With the discussion above, we can adapt the standard decomposition approach in analyzing queueing networks to study this inventory network. 

Transportation is flow of goods between supply chain stakeholders. The flow can be between and through any echelon of the supply chain from warehouse to factory, from factory to customer etc. The transportation problem can be viewed as a network flow problem where the nodes represent stakeholders, edges represent the cost and amount of transportation between them basically. Consider the network in Fig. 4.1. 5 represents the amount of supply at node n. D is the amount of demand at node m. This network is a direct shipment network. 

Likewise, the Bullwhip Effect generated at each step can be defined as the ratio of the variance in orders sent to the upper node of the supply chain, and the variance in orders received from the bottom node of the supply chain. 

A serial supply chain consists of a number of entities that work sequentially to deliver product. In a serial supply chain, any given node s supply is affected by the decisions of upstream entities, and that node s demand is generated by downstream entities. Serial supply chains provide a simple supply chain structure, but it often implies use of a one-size- fits-all strategy that can generate significant costs if products and customer segments... 

Consider a set of independent entities (nodes) in a serial supply chain, shown in Figure 2.4. Node 1 is closest to the customer, and Node 1 is supplied by Node 2, Node 2 is supplied by Node 3, and so on. Now suppose that Node 1 faces a demand of fi every period. Suppose each node faces a lead time L to get product from its supplier immediately upstream. Finally, suppose that each node carries a pipeline inventory (sum of all physical inventory, plus orders or material in transit) of (T 4- S) X DemandForecast, where S is the safety stock factor at that location. Thus, if every node passed along the demand forecast it faced, each node would have a pipeline inventory of (Z 4- 5) X DemandForecast. 

Each of the three problem contexts described earlier has the following basic structure A set of sources of supply has to be linked by a supply system to a set of demand locations. Figure 2.3 shows the supply and demand nodes for Delco (three supply nodes and thirty demand nodes). Merloni had five supply nodes and seventeen demand nodes. Letin had five supply nodes and twenty-five demand nodes. Notice that in the Delco case, the flows from supply to demand nodes consist of components used to assemble cars. In the Merloni and Letin cases, the flows consist of finished goods from assembly plants to locations closer to customers. But the abstraction of these problems has the same structure. What are possible ways to create a supply chain from these supply locations to the demand locations ... 

Variability increases with the number of nodes. The more nodes that a value chain has in a design, the greater the variability, the more working capital required and the higher requirements for automation and supply chain visibility. Waste (unessential activities and costs) increases as the number of nodes increases. Whenever possible, look for opportunities to disintermediate the supply chain and reduce the number of nodes. 

Waste increases with the length of the supply chain. The longer the supply chain, the more difficult it is to manage. Variability is amplified with each node. Long supply chains are the best fit for products with high volumes and low demand and supply... 

Like a tug-of-war, within the supply chain, there are push and pull relationships between trading partners at each node. For leaders, this is a conscious design element in the determination of supply chain strategy. 

Within these value chains, change happens frequently. Relationships come and go. The value chain is characterized by the number of nodes in the network, the number and type of constraints, the variability of demand and supply, the rhythms and cycles of decisions, the products and shipments, and the latency of information. Five years ago, the design of supply chains and value networks was an ad hoc process. Today, over 35 percent of companies have planning teams to rationalize the design and refine the network for current conditions. In our interviews, we learned that these teams are growing in both size and importance. 

Issue 2 Tackling demand distortion and waste. The number of nodes in the snpply chain increases variability. Data latency and distortion happen in each step, or node, of the supply chain. In the building of value networks, the greatest potential value and usually... 

Despite investments in technology and connectivity, companies within the consumer value chain have not reduced the bullwhip effect (distortion of the demand signal at each node) within their value network. This distortion coupled with the length of the supply chain (20 to 30 weeks) increases costs and waste for all parties. This is shown in Figure 2.6. 

This was a public admission of demand network failure. With the creation of Coca-Cola Enterprises in 1986, Coca-Cola was the darling of Wall Street. The company divested assets to improve its return on assets and financial fundamentals. What was not obvious then—and is all too clear now—was that when a company sheds assets, it must redesign to sense and shape demand to drive market performance. The more extensive the supply chain and the more third-party nodes, the greater the challenge and the more critical it is to sense demand and service a network. Form needs to follow function. Alignment and strategy are essential. 

The problem of resource contention at the intermediate nodes in the supply chain as a result of multiple flows has been deflned, and the solution in terms of relative quality parameters has been explored. 

A technique called a node tree, borrowed from a systems analysis technique called IDEFg, is useful. It is described in Section 24.3.2. IDEFq is widely used in analyzing activities in processes. An alternative, or even supplemental approach, is to use the SCOR model developed by the Supply-Chain Council. Section 23.1 describes the SCOR model. 

Given the supply-chain context of this book, we will consider only the management of independent-demand items—i.e., those items that move between firms in the supply chain. Throughout this book, we focus on issues related to node-to-node relationships in the supply chain, consistent with the framework developed in Chapter 1 that defines a supply chain as a network of nodes. Dependent demand involves "within-node" effects and is outside the scope of this book, but is discussed extensively in books on production/operations planning and control systems (e.g., Vollmann et al., 2005 or Chapman, 2006, which also contains an excellent discussion on hybrid systems that combine appropriate elements of MRP and kanban control). Note, however, that the classification of an item as an independent-demand item or a dependent-demand item is not an absolute characterization. Rather, it only makes sense in context. For example, to the company that assembles the cell phones, the keypad is clearly a dependent-demand item, provided that its only demand is derived from the production schedule for cell phones (i.e., not from sales of keypads as stand-alone items). To the firm that produces the keypads and sells them to various cell phone manufacturers, however, the keypad is an... 

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