Uncertainty prices/market demands/product

Changes in demand, breakthroughs by competing companies, fluctuations in the price or availability of raw materials and faulty original estimates can all cause a product to fail. Such market performance dramatizes the uncertainty of marketing new products. Indeed, experience has shown that about half of new commercial ventures fail. 

Uncertainty in market demand introduces randomness in constraints for production requirements of intermediates and saleable products, as given by Equation (6.4). The sampling methodology employed for scenario construction is similar to the case of price uncertainty in Approach 1, involving the generation of representative scenarios of demand uncertainty for N number of products with the associated probabilities that indicate their comparative frequency of occurrence. 

Table 6.2 Scenario formulation for uncertainty in prices, market demands, and product yields.

This book analyses uncertainty of market demand and price of the finished products and builds the model mainly based on stochastic programming theory [5-9]. In the following the stochastic programming theory will be presented and discussed. 

In this chapter, all parameters were assumed to be deterministic. However, the current situation of fluctuating petroleum crude oil prices and demands is an indication that markets and industries everywhere are impacted by uncertainties. For example, source and availability of crude oils as the raw material prices of feedstock, chemicals, and commodities production costs and future market demand for finished products will have a direct impact on final decisions. Thus, acknowledging the shortcomings of deterministic models, the next Chapters will consider uncertainties in the design problem. 

A 5% standard deviation from the mean value of market demand for the saleable products in the LP model is assumed to be reasonable based on statistical analyses of the available historical data. To be consistent, the three scenarios assumed for price uncertainty with their corresponding probabilities are similarly applied to describe uncertainty in the product demands, as shown in Table 6.2, alongside the corresponding penalty costs incurred due to the unit production shortfalls or surpluses for these products. To ensure that the original information structure associated with the decision process sequence is respected, three new constraints to model the scenarios generated are added to the stochastic model. Altogether, this adds up to 3 x 5 = 15 new constraints in place of the five constraints in the deterministic model. 

Table 7.6 shows the solution of the refineries network using the SAA scheme with N = 2000 and N = 20000 where the proposed model required 790CPUs to converge to the optimal solution. In addition to the master production plan devised for each refinery, the solution proposed the amounts of each intermediate stream to be exchanged between the different processes in the refineries. The formulation considered the uncertainty in the imported crude oil prices, petroleum product prices and demand. The three refineries collaborate to satisfy a given local market demand where the model provides the production and blending level targets for the individual sites. The annual production cost across the facilities was found to be 6 650 868. 

This chapter addresses the planning, design and optimization of a network of petrochemical processes under uncertainty and robust considerations. Similar to the previous chapter, robustness is analyzed based on both model robustness and solution robustness. Parameter uncertainty includes process yield, raw material and product prices, and lower product market demand. The expected value of perfect information (EVPI) and the value of the stochastic solution (VSS) are also investigated to illustrate numerically the value of including the randomness of the different model parameters. 

The results of the model considered in this Chapter under uncertainty and with risk consideration, as one can intuitively anticipate, yielded different petrochemical network configurations and plant capacities when compared to the deterministic model results. The concepts of EVPI and VSS were introduced and numerically illustrated. The stochastic model provided good results as the objective function value was not too far from the results obtained using the wait-and-see approach. Furthermore, the results in this Chapter showed that the final petrochemical network was more sensitive to variations in product prices than to variation in market demand and process yields when the values of 0i and 02 were selected to maintain the final petrochemical structure. 

An important part of external uncertainty is caused by customer demands. However, in basic chemical industry almost all final chemicals are produced to stock and, thus, are decoupled from direct market demands. Hence, variations of the customer demand do not affect the upstream production network immediately. Due to the exceptionally high re-start and changeover costs for plants, demand variations are mostly reflected by stock increases or sales price declines. The adaptation of production capacities by means... 

They also find that the cost of learning is a consequence of censored information and shared with the consumer in the form of a higher selling price when demand uncertainty is additive. They also apply the results to three motivating examples a market research problem in which a product is introduced in a test market prior to a widespread launch a global newsvendor problem in which a seasonal product is sold in two different countries with nonoverlapping selling seasons and a minimum quantity commitment problem in which procurement resources for multiple purchases may be pooled. 

Other kind of uncertainties need to be investigated. Up to date, most of SC models considering uncertainty are focused on product demand and variability in prices. However, to what extent the uncertainty associated with suppliers performance, processes output, and financial markets affects business performance has not been studied enough. 

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