Forecasts aggregate demand

Elasticity analysis algorithm as pragmatic approach to determine average price elasticity of aggregated demand forecasts that analyzes underlying customer price-quantity demand. 

Auctions to procure delivery services are not new, but they have typically been run lane by lane. The Home Depot, for example, used to publish the zip codes and aggregated demand forecasts for all their needed OD-pairs but not information on demand or growth patterns. Based on this limited information, carriers had to submit individual bids for OD-pairs they were willing to serve. Not knowing the demand distribution for such a given lane, or whether they would win lanes that would match with the lane under consideration, bidders would bid conservatively. ... 

Aggregate demand forecast Ft for each Period t in a planning horizon that extends over T... 

However, customers want variability. They do not always want or need the same items. In the case of variability, demand planners can provide an aggregate demand forecast. For example, consider the Lindt Chocolate Company that wants to forecast the number of 75 piece truffle bags needed to meet demand. The demand forecasts tend to be more accurate when forecasting the total number of 75 piece truffle bags (Table 9.1) rather than trying to forecast the number of bags of each flavor milk chocolate, dark chocolate, and caramel (Table 9.2). ... 

Revenue management is focused on demand forecasting - aggregated and disaggregated - demand distribution models or arrival processes to... 

The rolling planning process starts with a first phase ( ) typically in the first half of the period. The regions update the regional demand forecast by price and quantity that is aggregated to a global demand. In analogy, offers... 

Aggregate forecasts are more accurate—Demand forecasts for the entire product family are more accurate compared to those made for each member of the product family. 

As stated at the beginning of the chapter, the success of a manufacturing supply chain depends on its ability to accurately forecast customer demands and plan the production process to meet those demands in time. So far in this chapter, we discussed the forecasting principles and several qualitative and quantitative methods for demand forecasting. Based on the demand forecasts, aggregate production planning is done to plan the production. 

Demand forecasting forms the basis of production planning. Facilities planning are based on long-term demand forecasts. They determine where to locate plants, warehouses, DCs, etc., and what products to make where. Mathematical models and methods for facilities planning will be discussed in Chapter 5. In this chapter, we discussed methods for aggregate planning decisions which are usually made a few months in advance. 

Aggregate planning manages supply (capacity and inventory) to handle demand variability. Hence it is a reactive process given a demand forecast. Typically, manufacturing departments are responsible for aggregate planning. 

XYZ company is preparing its aggregate production plan for 2012. The monthly demand forecasts are given in Table 2.23. 

Part of the reason that so many forecasts have so little accuracy is that they try to achieve the impossible, i.e. to forecast at the individual item level (SKU) too far ahead. Clearly every business needs to plan ahead in order to ensure that they have access to enough capacity and materials. Flowever, wherever possible these plans should be made on the basis of high-level aggregate volume forecasts at the product family level. As we get closer to the point of demand fulfilment then we can start to think about product mix requirements. 

All the data are exactly the same as in our previous discussion of Red Tomato, except for the demand forecast. Assume that the same overall demand (16,000 units) is distributed over the six months in such a way that the seasonal fluctuation of demand is higher, as shown in Table 8-5. Obtain the optimal aggregate plan in this case. 

There are no limits on subcontracting, inventories, and stockouts. All stockouts are back-logged and supplied from the following month s production. Inventory costs are incurred on the ending inventory in each month. The companies goal is to obtain the optimal aggregate plan that leaves at least 500 units of inventory at the end of June (i.e., no stockouts at the end of June and at least 500 units in inventory). The base demand forecast is shown in cells J5 J10 of Figure 9-1. 

Regularly, decisions about the production have to be taken. These decisions can be taken at different levels in a hierarchical way. First we can do the capacity load planning and decide whether we will produce in a certain period or not. For this decision the demand forecasts at an aggregated level will be the most important element. For the periods in which we have decided to produce, we have to decide which type(s) will be produced. This decision will be called the type planning. The type that will be chosen may be the type that we have been woridng on most recently, especially if no set-up is needed in that case. It can also be the type for which the orders are the most urgent, possibly measured according to the demand forecasts. If we have chosen the type that will be produced, we have to select the orders to be... 

Demand is forecasted with price and quantity in a first step and aggregated to a total demand volume with an average price. 

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