Demand Fulfilment and ATP

Kilger, Christoph; Meyr, Herbert

doi:10.1007/978-3-540-74512-9_10

Cited by 19 publications

(7 citation statements)

References 8 publications

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“…Especially, AATP is also used when the supply strategy resources are constrained and cannot meet market demands in either the short or long term (i2 technologies 2000). Kilger and Schneeweiss (2002) said the adequate allocation of ATP to distributors from manufacturer can be exploited to increase the revenue and profitability of the company (Kilger and Schneeweiss 2002). Jeong et al (2002) showed the ATP system for TFT LCD manufacturing in the supply chain).…”

Section: Allocation Policiesmentioning

confidence: 98%

“…For the high-level order fulfilment, ATP (available to promise), allocation policies, and AATP (allocated available to promise) are important components (Kilger and Schneeweiss 2002). First of all, ATP is defined as the uncommitted portion of a company's inventory and planned production, maintained by the master production schedule and material plan (i2 technologies 2000).…”

Section: Allocation Policiesmentioning

confidence: 99%

“…Order fulfilment is the planning process that determines how the actual customer order is fulfilled-order promising, allocation of product, and so on (Kilger and Schneeweiss 2002). To respond to future demands, buyers forecast the demand in the next period.…”

Section: Introductionmentioning

confidence: 99%

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Production quantity allocation for order fulfilment in the supply chain: a neural network based approach

Lee

Jung

Eum

et al. 2006

Production Planning & Control

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In the current global business environment, it is very important to know how to allocate products from the producer to buyers (or distributors). If products are not appropriately distributed due to absence of an effective allocation policy, the producer and buyers cannot expect to increase customer satisfaction and financial profit. Sometimes some buyers can order more than the actual demand due to inappropriately forecasting customer orders. This is the big obstacle to the effective allocation of products. If the producer can become aware of buyers' actual demands, it is possible to realise high-level order fulfilment through the effective allocation of products. In this study, new allocation policies are proposed considering buyers' demands. The back propagation algorithm, one of the learning algorithms in neural network theory, is used to recognise actual demands from the previous buyers' orders. After excluding surplus demands included in buyers' demands, products are allocated to buyers according to one of the existing allocation policies depending on the company's decision. In the numerical examples, new allocation policies reducing buyers' surplus demands outperform previous allocation policies with respect to average amount of backorder.

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Section: Allocation Policiesmentioning

confidence: 98%

Section: Allocation Policiesmentioning

confidence: 99%

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Production quantity allocation for order fulfilment in the supply chain: a neural network based approach

Lee

Jung

Eum

et al. 2006

Production Planning & Control

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“…However, since we are dealing with a problem on supply chain level, we primarily review publications from the field of demand fulfilment. For a general introduction to demand fulfilment, the interested reader is referred to Fleischmann and Geier (2012), Kilger and Meyr (2015), Pibernik (2005), Ball, Chen, and Zhao (2004) or Quante, Meyr, and Fleischmann (2009). Framinan and Leisten (2010) mention five flexibilities that can be exploited when it is not possible to fulfil orders according to the original request.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Chen, Zhao, and Ball 2002). Kilger and Meyr (2015) point out that customer retention and increase of market share strongly depend on the speed and reliability of the order promising process of a company. Furthermore, according to Oracle and Capgemini (2013), promising reliable delivery dates to the customer becomes more challenging.…”

Section: Introductionmentioning

confidence: 99%

Increasing accuracy and robustness of order promises

Seitz

Grunow

2016

International Journal of Production Research

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Accurate order promising is a key requirement for customer satisfaction. Nevertheless, practitioners struggle with the reliability of the delivery dates they promise to customers. Consequently, the costs of demand fulfilment soar due to intensified communication, emergency processes in logistics and acquisition of costly external production resources. We identify and formalise product and process flexibilities in supply chains that can be exploited in production planning. Product flexibility is the possibility to produce several kinds of products from one predecessor product. Process flexibility is the possibility to use one production process to manufacture several products. In order to increase the accuracy and robustness of delivery dates, we develop an order promising methodology able to deal with demand mix uncertainty and heterogeneous customer order lead times. The approach anticipates changes in production plans made possible by product and process flexibilities. In a numerical study based on a case from the semiconductor industry, we demonstrate that our method increases the accuracy and robustness of order promises. For the studied case, we find that the consideration of process flexibility is more important for the generation of accurate and robust order promises than the consideration of product flexibility.

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