Demand Fulfillment

Short-term sales planning is sometimes supported by a demand fulfillment and available-to-promise (ATP) module. Its purpose is to match inventories and production orders with demands, if customers require reliable quotes with only short notice. 

Sales planning has to reflect the bidding process presented in subchapter 2.3. Fig. 20 illustrates the paradigm shift from demand fulfilment towards sales plan achievement, where demand forecast captures the role of a price-quantity bid that is cleared based on available supply and a certain sales flexibility. The sales planning mechanism is further described in the following detailed requirements. 

Kilger C, Schneeweiss L (2004) Demand Fulfilment and ATP. In Stadtler H, Kilger C (eds) Supply Chain Management and Advanced Planning. 3rd edn. Springer, Berlin et al., pp 179-196... 

Demand fulfilment Management of customer orders, e.g. tracking of orders along the production process, acceptance of new orders, setting of initial order due dates. "... 

Kilger, C. and Meyr, H. Demand fulfilment and atp. In Stadtler, H. and Kilger, C., editors. Supply Chain Management and Advanced Planning, pages 181-198. Springer, Berlin, 2008. 

Kilger, C., L. Schneeweiss (2000). Demand Fulfillment and ATP, in Stadtler, H., Kilger, C., eds. Supply Chain Management and Advanced Planning Concepts, Models, Software and Case Studies. Berlin, Germany Springer, 135-148. 

More recently, many supply chain management software companies such as i2 (www.i2.com) and Manhattan Associates (www.manh.com) have moved from the purely supply chain optimization solutions including demand fulfillment and transportation planning to offerings that provide many of the same capabilities as ERP vendors. Detailed discussion of supply chain operations and offerings may be found in chapter 17 and in Sections III and IV of this book. 

During the stage 3 analysis, additional levels of facility expansions were considered. The results provided production, distribution, and sales levels to maximize profits. However, the optimal demand fulfillment ratio was only 96%, highlighhng specific product-market combinations that were not profitable. 

The optimization models discussed in this chapter had a single objective— either to minimize supply chain costs or to maximize supply chain profitability, in case the product prices vary by location or customer. However, customer demand fulfillment and service are also important in designing a supply chain network. More recently, supply chain risk is emerging to be another important criterion (Supply chain risk is discussed in detail in Chapter 7). Hence, recent applications of optimization models have used multiple criteria optimization models for decision making. 

Customer service criterion has two sub-criteria Lead time and Demand fulfillment. Lead time is defined as the time between receipt of a customer sales order and its delivery at the customers facility (used as a proxy for speed). Demand fulfillment is the proportion of the customer order that is effectively delivered. 

In the multi-criteria model, the overall demand fulfillment ratio increased to 99% with some reduction in profits. A valuable outcome of this analysis was the trade-off evaluations for specific product-market combinations, where increases in sales reduced profitability. These insights led to valuable managerial implications for the evaluation of the firms competitive strategy. 

GP Gross profit LT Lead time DF Demand fulfillment RD Risk of disruption SF Strategic factor... 

Demand fulfillment at the customer zones (percent of demand met) The objective is to maximize the net profit over the planning horizon subject to the following constraints ... 

Chien C-F, Wu J-Z, Wu C-C (2013) A two-stage stochastic programming approach for new tape out allocation decisions for demand fulfillment planning in semiconductor manufacturing. Flex Serv Manuf J 25 286-309... 

Demand fulfillment, another APS feature, helps the organization respond to demand as it actually unfolds. This includes the ability to look forward and... 

The expectations of the poor are demanding fulfillment. Hopefully, some measure of their ambitions can be realized by peaceful means. 

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. 

Figure 4.9 highlights the integrative nature of S OP processes. Whereas in conventional businesses there is little integration between the demand creation side of the business (i.e. sales and marketing) with the demand fulfilment activity (i.e. logistics and operations), with the S OP philosophy there is a seamless alignment between the two. 

Note that with the revised CODP for assemble-to-order (in Fig. 6.2) customers must wait while their order is assembled. To minimize this wait time, the supply chain can be structured for a third option where the finished product is made to stock (MTS) as well as assembled to order (ATO). We call this make to stock and order (MTSO), where the size of inventory is smaller than that in MTS. While the make-to-stock items ensure demand fulfillment for customers who arrive early, MTSO facilitates fulfillment of late orders as well, on an ATO basis. As the demand is not known in advance, a policy that combines MTS of components with ATO can be optimal. To implement this, inventory is allocated to two buckets bucket 1 with finished products is used to fulfill the demand of early customers, and bucket 2 with the generic components is earmarked for the late customers. The company must decide how much of which products to stock in bucket 1 and what should be the size of inventory in bucket 2. 

The multiple and conflictive decision criteria used in the model will be explained in more detail in Section 3.3.2. The objective then becomes the minimization of the deviations from the specified criteria targets profit, demand fulfillment, lead time, disruption risk, and strategic factors. 

The customer service criterion has two sub-criteria lead time and demand fulfillment. Lead time is defined as the time from when a customer places an order until the moment it is delivered at the customer s facility (used as a proxy for speed of delivery). Demand fulfillment is the proportion of the customer s order that is effectively delivered. Under the lead time criterion, customers are classified as modem trade or traditional trade. Modern trade refers to large customers (e.g., super market chains), while traditional trade refers to small customers (e.g., "mom-and-pop" stores). 

Note GP gross profit, LT lead time, DF demand fulfillment, RD sourcing risk, SF strategic factor. 

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