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Congestion-cognizant joint planning ...

Purdue University., Industrial Engineering.

Congestion-cognizant joint planning models at the production-marketing interface.

紀錄類型:	書目-電子資源 : 單行本
正題名/作者:	Congestion-cognizant joint planning models at the production-marketing interface./
作者:	Upasani, Abhijit Anand.
面頁冊數:	164 p.
附註:	Source: Dissertation Abstracts International, Volume: 68-12, Section: B, page: 8312.
Contained By:	Dissertation Abstracts International68-12B.
標題:	Engineering, Industrial. -
電子資源:	Download PDF (下載PDF全文)
ISBN:	9780549357438

Congestion-cognizant joint planning models at the production-marketing interface.
Upasani, Abhijit Anand.

Congestion-cognizant joint planning models at the production-marketing interface. - 164 p.

Source: Dissertation Abstracts International, Volume: 68-12, Section: B, page: 8312.

Thesis (Ph.D.)--Purdue University, 2007.

In many industries the pricing of a product over time can be used to manage demand for the product. Lead time (or promised delivery time) is often a significant factor in price negotiations. However, the production planning literature has largely treated pricing decisions as exogenous while focusing on the allocation of production capacity between products over time. On the other hand, the marketing literature has generally ignored the effects of capacity constraints and focused on the effects of pricing.

ISBN: 9780549357438Subjects--Topical Terms:

170926
Engineering, Industrial.

Congestion-cognizant joint planning models at the production-marketing interface.
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245 1 0 $a Congestion-cognizant joint planning models at the production-marketing interface.
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500 $a Source: Dissertation Abstracts International, Volume: 68-12, Section: B, page: 8312.
500 $a Adviser: Reha M. Uzsoy.
502 $a Thesis (Ph.D.)--Purdue University, 2007.
520 $a In many industries the pricing of a product over time can be used to manage demand for the product. Lead time (or promised delivery time) is often a significant factor in price negotiations. However, the production planning literature has largely treated pricing decisions as exogenous while focusing on the allocation of production capacity between products over time. On the other hand, the marketing literature has generally ignored the effects of capacity constraints and focused on the effects of pricing.
520 $a In this research, we begin by reviewing the existing literature on integrated production -- marketing research, focusing on those models that consider lead times and capacity. We suggest a number of directions for future research that take advantage of recent developments in production planning models, as well as explicit modeling of feedback loops governing key parameters, which suggest a broader view of the problem.
520 $a Next, we develop an integrated single product model for dynamic pricing and lead time quotation, where lead times are congestion dependent. We achieve this by means of a non-linear clearing function that relates the throughput of the production system to its work-in-process inventory. This allows us to capture interactions between pricing, sales and lead times that have generally not been considered in the past. We present numerical examples to demonstrate these interactions, as well as analytical results showing that the proposed model behaves quite differently from conventional models that ignore congestion.
520 $a Finally, we extend the above model to include multiple products, where the products compete for capacity but face independent demand streams. We model this competition by means of a clearing function that allocates capacity to products based on WIP levels and demand requirements. Analytical and numerical results show that our model has three levers for managing demand: price, load-dependent lead time and WIP mix. In comparison, existing models from literature have only one lever: price.
520 $a Our models combine several aspects of the problem that were considered separately in the past, and are more aware of the delivery capabilities of the production system thus enabling more predictable revenues and better customer service.
590 $a School code: 0183.
650 4 $a Engineering, Industrial. $3 170926
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710 2 $a Purdue University. $b Industrial Engineering. $3 1000006303
773 0 $t Dissertation Abstracts International $g 68-12B.
790 1 0 $a Uzsoy, Reha M., $e advisor
790 1 0 $a Rardin, Ronald L. $e committee member
790 1 0 $a Preckel, Paul V. $e committee member
790 1 0 $a Richard, Jean-Philippe P. $e committee member
790 $a 0183
791 $a Ph.D.
792 $a 2007
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