Forecast/forecasting demand

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... 

The single refinery was then solved considering risk in terms of variations in the price of imported crude oil, prices of final products and forecasted demand to provide a... 

The objective function of a supply network design model can either minimize costs or maximize profits. In practice the production function is often required to assume that all forecasted demands have to be met. In this constellation cost minimization and profit maximization lead to identical results and consequently cost minimization models are used. From an economic perspective this simplification can be justified in cases where a high share of fixed costs allows the assumption that any product sale con-... 

Demand can be estimated using forecasting. Forecasting is a necessary prerequisite for many of the methods and procedures used in operations management (Lewis, 1981, p. 241). Forecasting demand for goods and services requires the use of information, mathematical functions, and statistical analyses. 

Forecast the crude slate that would likely be used to meet the forecast demand. This is determined by an analysis of domestic reserves, historical production trends, and estimates of new production. The shortfall between domestic production and refinery crude runs is met by imported crudes which are selected on the basis of production/export capabilities, logistical factors, and historical trends. 

President s Materials Policy Commission, Resources for Freedom, 5 vol., June, 1952. Study of raw material requirements of the United States, 1950-1975. Major sections on chemicals, metals, minerals, fuels, agriculture, forest resources, water, and ocean resources. Forecasts demand for major chemicals and raw materials up to 1975. 

Agility will be a new paradigm of manufacturing. Increasingly, products will not be made on the basis of forecast demand, but made to order. Personalized or customized products will be available at the right time and at a mass production price. 

Figure 13.2 shows domestic vanadium production and consumption 1960 to 1968, and the domestic maximum and minimum forecast demand for vanadium from 1968 to the year 2000, as estimated by Griffith. Cumulative requirements at the maximum demand rate from 1968 to 2000 are 520,000 short tons of vanadium. 

When standby or future capacity is required for transformers it is necessary to rate the secondary cables or busbars correctly at the design stage of the project. Likewise the secondary circuit breakers and switchgear busbars need to be appropriately rated for the future demand. The decision to over-rate the primary cables or lines may be made at the beginning of the project or later when demand increases. Again this is a matter of economics and forecasting demand. 

Is there sufficient safety stock to meet the forecasted demand ... 

In this chapter we have provided a quick review of four possible approaches to forecast demand and its use in planning. The constant demand model allows for a quick analysis of the effect of ordering costs in a system. The models of demand as a distribution permit details of lead time and demand uncertainty to be included. The modeling of demands as a mixture of distributions enables us to consider the role of information acquired over time. Finally, the exponential smoothing model shows how demand forecast updating can create large swings upstream in a supply chain. 

Negotiation Agent. Negotiation Agent will be activated by the user, when it is considered appropriate by the latter, from the interface of the developed tool. When it is active, it will allow the management of forecasting demand in the supply chain in a coordinated way through collaboration between Shop Retailer and Retailer, on the one hand, and Wholesaler and Factory, on the other. 

Table 8 is an extension of Table 3 but adding a column with the results when considering the ARIMA models to forecast demand in the first level of the supply chain (BW5). Furthermore, we show the reduction achieved in each case. 

Grocery retailers have to carry store inventory to satisfy retail demand over the supplier lead time to ensure high in stock levels. Thus, retailers have to forecast demand at price levels offered. As discussed in the chapter on coordination, in an uncoordinated supply chain, double marginalization results in a lower retail service level than is optimal for the supply chain as a whole. 

Often the difficulty of forecasting demand during a retail promotion means that the retailer may order too much or too little. In addition, if the retail pricing information is not communicated to the manufacturer, then a forecasting rule that adjusts orders based on shipments will result in the manufacturer carrying high inventories between retail promotions. Sharing planned retail promotion information decreases these costs and thus improves supply chain profits. 

Surveys by major retailers surest that service levels at various stores are around 70%. Thus one out of three customers does not find the item in stock at a store that carries it. Note that apparel SKUs refer to color, style, and size availability, which requires demand estimates of specific sizes at a location. From a supply perspective, if manufacturer deliveries are of a fixed assortment of product across sizes, then it is clear that any deviation from the average size mix can cause stockouts. Thus, stockout reductions may require coordination between a fiexible manufacture of varying sizes (driven by observed store sales) and careful retailer monitoring of inventory levels by size level demand and availability. The additional complication is the impact of fit preferences across customers, ranging from slim fit to loose, baggy fit choices. Such trend effects may add additional complexity to the problem of forecasting demand at the SKU level. 

Thus the forecasted demand distribution, eight months in advance, is Demand Probability... 

Consider the quick response (QR) approach. Under this scheme, the manufacturer has to receive the order only four months in advance. The retailer can collect data regarding sales of similar products at points closer to the upcoming season before placing an order. Intuitively, the ability to order closer to the season increases the possibility that more recent trend information or economic conditions can be used to better forecast demand. For this example, assume that data regarding demand for similar product enables the retailer to further refine the demand distribution estimates. What will be the impact of QRoii the retailer (For now, assume manufacturer prices remain the same.)... 

The model determines the type and sizes of facilities to be added/removed and when so that the present worth of all capacity changes is minimized while meeting forecasted demand. Examples of such classical and static models can be found in Manne (1967) and Freidenfelds (1981), while a good review on classical capacity expansion models can be found in Luss (1982). 

To compensate, Dell shifts demand at the point of sale. For example, if you went online to the Dell website to purchase a Dell Inspiron 15 laptop, you may find a sales promotion pop-up that says, For today and today only you can purchase a Dell Inspiron 17 laptop with a bigger screen, more processing capacity, bigger hard drive with expanded memory, as well as additional software for a reduced price. Consider a likely scenario of under-forecasted demand for key components for the Dell Inspiron 15 laptop. To align the supply chain, Dell actively shifts demand by offering a sales promotion to move a customer from the Inspiron 15 laptop to the Inspiron 17 laptop. Its goal was not to lose a sale or delay a shipment, but increase profitability. 

Our existing solutions did a poor job of forecasting demand around promotions, explained the director of demand and supply chain planning. 

Batch production is also useful for seasonal items, products for which it is difficult to forecast demand, for a trial mn for production, or products that have a high-profit margin. 

Push-based strategy in which production and distribution decisions are based on long-term forecasted demand. In this case, it takes much longer to the company to react to the changing marketplace. As the strategy relies on forecasts, it is most of the time difficult to match supply and demand. 

---