Price forecasting model

The second model which must be parameterized is the time-series forecast model. This is equivalent to determining the location (placement) of the demand curve for each customer segment for each product. The placement of the demand curve is of critical importance, since it represents the potential size of the market and whether or not supply constraints will bind. If demand from a market segment is large relative to supply, then the optimal prices are adjusted to reflect the opportunity cost associated with limited supply. 

There are over 400 products which makes it complicated and difficult to plan and manage the production and inventory of both raw materials and finished products. It is also difficult to forecast product demand as the products have different demand patterns. The demand of some products are highly stochastic while others are steady. Some products have seasonal demand patterns. And the demand of some products are related to the demand pattern of other products. To solve this problem, we have to design different forecast models for each products. Since the demands for over 400 products must be met, the plant must carefully plan and manage its production and raw materials supply. The charactristics of raw materials used in TCM also result in the difficulty in planning raw material procurement. Most of the raw materials of TCM production are natural, so the price often varies in different seasons. This also offers the procurement department opportunities to gain extra profit from properly planning its procurement and inventory. 

An overview is provided of the North American PE Foam Market. Historical market growth rate and market dynamics are presented as well as a forecast to 2007. An analysis is also presented of the forces that impact PE foam demand and pricing based upon Michael Porter s well-known five-market forces model for analysing industries and markets. Application of this model will provide some insight into dynamics that should be considered in creating a robust business plan. 3 refs. 

Risk is modeled in terms of variance in both prices of imported cmde oil CrCosta and petroleum products Pry/, represented by first stage variables, and forecasted demand DRef, yr, represented by the recourse variables. The variability in the prices represents the solution robustness in which the model solution will remain close to optimal for all scenarios. On the other hand, variability ofthe recourse term represents the model robustness in which the model solution will almost be feasible for all scenarios. This technique gives rise to a multiobjective optimization problem in which... 

Targets being discussed in the USA vary. Boxer-Lieberman-Warner and Waxman-Markey have respective 2020 targets of 19% and 17% below 2005 levels, and the Obama Administration has proposed a 2020 target of 14% below 2005 levels. Modelling of an earlier version of the Boxer-Lieberman-Warner proposal projected a price range of 37-51 in 2020. The ElA forecast a price of 30 per ton in its core case for 2020. Modelling of the US ERA of an earlier version of Waxman-Markey forecast a price of 17-22 in 2020. 

In many cases the values of the data coefficients are obtained by statistical estimation procedures on past figures, as in the case of sales forecasts, price estimates, and cost data. These estimates, in general, may not be very accurate. If we can identify which of the parameters affect the objective value most, then we can obtain better estimates of these parameters. This will increase the reliability of our model and the solution. 

Determining the Value of an Embedded Option The value of an embedded option is found through the binomial tree model. The first step is to forecast the value of the underlying security in which the price S of a security can move, respectively, in the upstate and downstate with a probability of p and 1 p. The change in price occurs in discrete time interval At and will depend on the level of volatility assumed. An option written on the asset, with maturity T will move in discrete steps as the movements of the share prices. The process can be carried on for any number of time intervals (Figure 9.6). 

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