Pricing decisions, components

We now describe key components of pricing decisions that affect supply chain performance. 

These models, however, can be useful if estimates are to be made of operating costs. By assigning fixed and variable costs to each activity, average and marginal unit costs at each stage of the process can be easily calculated, which will assist in decisions regarding pricing policies or whether to buy in components and materials or make them on site. ATPLAN is an example of a network-based model of this type. 

Consideration of the expected value of profit alone as the objective function, which is characteristic of the classical stochastic linear programs introduced by Dantzig (1955) and Beale (1955), is obviously inappropriate for moderate and high-risk decisions under uncertainty since most decision makers are risk averse in facing important decisions. The expected value objective ignores both the risk attribute of the decision maker and the distribution of the objective values. Hence, variance of each of the random price coefficients can be adopted as a viable risk measure of the objective function, which is the second major component of the MV approach adopted in Risk Model I. 

There were two reasons why Colonius and I were favored to be the first to hit on this very simple reaction in 1930. First, it was just then that lithium was first used for certain technical applications (e.g., as an alloy component for bearing metals), and therefore became easily accessible at a tolerable price. Second, the experiments described under II above had induced us to study the kinetics of certain lithium alkyl reactions. From these we had found that ethyllithium and alkyl halides, especially chlorides, essentially do not react with each other. Therefore the course of our decisive experiments had been predictable (38). This impetus had been necessary to overcome the prejudice about the Wurtz synthesis originating from the textbooks. 

Companies should consider whether to make components or the final product in-house or purchase them from outside vendors. Price is usually the deciding factor, but other factors ctm affect the final decision. Some of these factors are can production demand requirements be fulfilled, can qimlity expectations be met, and can delivery schedules be met Likewise, it might be better to use a supplier because they may have been producing similar parts for years and have the expertise to produce better parts than the company making the estimate. It is always wise to develop these estimates for comparison. 

Lastly, the pricing of remanufactured products has become a subject of debate. Traditionally, remanufactured products have been priced at a lower value than new products due to the reused components. For instance, Siemens remanufactured imaging systems price runs about two-thirds that of a new unit, which are usually priced between 160,000 and 170,000. However, it is still in discussion whether remanufactured products should receive a lower valuation. A diversity of variables such as certification of the final product, number of parts replaced or upgraded, and demand for remanufactured components must be taken into account in this decision. 

Apart from price and availability considerations, the composition of an oil is only one of the factors that enter into decisions to choose one oilseed over another in world markets. Two other important considerations are the proportion of meal in the seed and the quality of the meal. For example, soybeans contain only 18% oil hence a much higher proportion of their total value lies in the high protein meal than in the oil. Conversely, rapeseed peanuts, sunflower, and cottonseed contain 30-40% oil, therefore oil is the more valuable component. Then the quality of the meal becomes a very critical factor in determining the commercial value of one oilseed versus another. Sunflower seed, for example, produces an inferior meal, which is high in fiber and low in protein (23%) meals from cottonseed and peanut are of medium quality. 

The important message is that supply chain cost under uncertain demand has two components. The first is the traditional cost associated with physical distribution. But distribution cost does not fully capture the economic impact of supply chain design decisions. One should also consider the effect of price markdowns and lost profit opportunities. Later chapters, particularly Chapter 28, are dedicated to adding flexibility to the supply chain s design. 

Similarly, Federgruen and Heching [51] study a problem where price and inventory decisions are coordinated under linear production cost, stochastic demand and backlogging of excess demand. They assume revenue is concave (e.g., as under a linear demand curve with an additive stochastic component), and inventory holding cost is convex. All parameters are allowed to vary over time, and price changes may be bi-directional over a finite or infinite horizon. 

Many research problems that address pricing and production decisions with fixed production set-up cost fall within the area of the Economic Order Quantity (EOQ) model). The general EOQ model inventory model has been studied frequently in inventory literature (see [164] for a review). The problem consists of multiple periods in a fixed time horizon, with a stationary deterministic function in each period ordering or production costs have a fixed and variable component. Since demand is deterministic, the optimal policy will leave zero inventory at the end of each time cycle, so each period may be considered... 

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