Linear supply chain

The linear supply chain suffered from a lack of transparency that caused mismatches between production and demand and led to accumulation of inventory upstream, and loss of customers as well as revenues downstream. The disparity in scale was another problem HP, Solectron, and the resin supplier are large companies, while most injection molders and compounders are relatively small. Therefore, every HP order for monitor cases was split among many compounders, who bought resin in small volumes and, consequently, at relatively high prices from the resin maker. 

Positioning the EM techniques reviewed in this chapter into a research map (Figure 5) that has manufacturing level and decision timescale as its axes reveals almost intuitive results. The more focused (lower manufacturing level) an EM technique, the shorter the timescale on which decisions can be made adjusting machine setup parameters (turret level) can be done by one person in a few minutes, whereas reconfiguring a supply chain (enterprise level) will take a team of people months or even years. In the research map, this correlation seems linear, but since the x-axis is not continuous and the y-axis is not quantitatively scaled, a strict correlation is undefinable and inappropriate. Nonetheless, there are clear areas of the map that are not occupied by any of the reviewed EM techniques, and it is therefore suggested that there is a need for research to be undertaken to address these areas. 

The word chain in supply chain is misleading as it implies a linear structure. However, the structure of a supply chain is usually a network structure and only in... 

The optimization of value-added processes is a subject that scientists all over the world have been dealing with for more than 70 years. The first basic algorithms for so-called Linear Programming (LP) were developed at American and European universities already in the 1930s, for the first time allowing the planning and simulation of simple business processes. LP soon became the base of the first software systems and even today almost all Supply Chain Management (SCM) or... 

In addition to their use as stand-alone systems, LPs are often included within larger systems intended for decision support. In this role, the LP solver is usually hidden from the user, who sees only a set of critical problem input parameters and a set of suitably formatted solution reports. Many such systems are available for supply chain management—for example, planning raw material acquisitions and deliveries, production and inventories, and product distribution. In fact, the process industries—oil, chemicals, pharmaceuticals—have been among the earliest users. Almost every refinery in the developed world plans production using linear programming. 

Chemical-industry related literature addressing value chain management focuses on production and supply chain management as well as selectively procurement. Companies in the oil and chemical industries have been leaders for almost 50 years in the development and use of linear and mixed integer programming models to support decision-making at all levels of planning (Shapiro 2004). 

The constellations of actors in the supply chain vary between comparatively simple and linear stracture with few actors, and highly networked and complex stractures. Two basic types of iimovative system and innovation level complexity can be deduced and to which each of the 13 case studies of the SubChem project can be assigned. As an ideal type ... 

The production and supply side is analysed mainly using the MOREHyS model (Ball, 2006 Seydel, 2008). MOREHyS is a technology-based (bottom-up), mixed-integer, linear optimization model. The objective function used for the optimization, which is carried out sequentially, is yearly cost minimization for the whole country and the complete supply chain (production to dispensing) in each snapshot. 

To prepare the base model, we have considered a traditional supply chain with linear stmcrnre, which consists of five main levels Consumer, Shop Retailer, Retailer, Wholesaler and Factory. Figure 1 shows the graphical representation of the levels, indicating the materials flow, which occurs firom the top of the chain (Factory) to the lower levels (Consumer). Therefore, it is called downstream flow. The information flow is considered to be in the opposite way, which is called downstream flow. 

The linear programming tool is a first step in uncovering possible choices to operate a supply chain that may differ from the usual heuristics that do not account for the chain structure. Often the solution generated exposes opportunities that may not have been considered. At other times, the solution enables an understanding of the value of changes to a supply chain, such as addition of new supply sources or warehouses, that may further improve performance. 

This section thus suggests that careful choice of the products that use up internal capacity vs. those that can use externally available capacity should consider the marginal benefit per unit of the bottleneck internal capacity. Such an analysis gets complicated because the bottleneck resources are, in turn, defined by the mix of products that are made vs. outsourced. The use of tools such as linear programming enables this issue to be resolved by considering the entire problem simultaneously. Such tools enable the optimal choice of bottleneck resources that minimize supply chain costs. 

To illustrate these points, let s take a closer look at a specific industry—consumer products—and the dynamics of its nnderly-ing value network. The consnmer value chain is composed of many companies. It stretches from the consumer through a network of retailers, mannfactnrers, and suppliers. Other industries—transportation, third-party logistics firms, freight forwarders, and marketing agencies—play snpporting roles. It is not linear. Instead, it is a network of hnndreds of companies. Each company within the chain operates multiple supply chains. The industry has worked hard to be collaborative however, today few interactions are truly collaborative. They lack alignment and a win-win value proposition that can sustain the test of time. 

With product personalization, volume decreases and the predictability of supply declines. As a result, many companies are facing a new world of defining and managing supply processes for long tail supply chains. By definition, the further out the product portfolio is in the tail, the less efficient the supply chain process, ft is also less suitable for traditional linear optimization technlqnes found in the first generation of snpply chain planning technologies. 

Linear programming became the basis for mixed-integer linear programming (MILP) which nowadays involves several branches of chemical engineering (Biegler and Grossmann, 2004). One example is supply chain management process control... 

---