Value of Reverse Factoring in Multi-Stage Supply Chains

2012 ◽  
Author(s):  
Fehmi Tanrisever ◽  
Hande Cetinay ◽  
Matthew Reindorp ◽  
Jan C. Fransoo
Keyword(s):  
Supply Chains ◽  
Multi Stage

Mean Sojourn Time in Multi Stage Fork-Join Network

2015 ◽  
Vol 6 (3) ◽  
pp. 80-99 ◽  
Author(s):  
Yonit Barron
Keyword(s):  
Supply Chains ◽  
Sojourn Time ◽  
Computer Processing ◽  
Single Stage ◽  
Assembly Systems ◽  
Parallel Tasks ◽  
Multi Stage ◽  
Stage System ◽  
Major Factors ◽  
Performance Behavior

Fork-Join queue networks (F-J) have received increasing attention during the last Decade, due to their ability to model parallel and distributed computer processing, supply chains and assembly systems. However, most research is focused on a single stage processing, and only scant work exists on F-J with two or more stages. In this paper, the author investigates (through simulation) the performance behavior of a multi-stage system; in particular, the performance of a synchronized system is compared to an unsynchronized system regarding three major factors: (1) the number of parallel tasks; (2) the number of serial stages and (3) the utilization.

Investigating the impact of inventory inaccuracy on the bullwhip effect in RFID-enabled supply chains using colored petri nets

2019 ◽  
Vol 14 (2) ◽  
pp. 360-384 ◽  
Author(s):  
Maria Drakaki ◽  
Panagiotis Tzionas
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Petri Nets ◽  
Information Sharing ◽  
Bullwhip Effect ◽  
Colored Petri Nets ◽  
Content Type ◽  
Multi Stage ◽  
The Impact ◽  
Serial Supply Chains

PurposeInformation distortion results in demand variance amplification in upstream supply chain members, known as the bullwhip effect, and inventory inaccuracy in the inventory records. As inventory inaccuracy contributes to the bullwhip effect, the purpose of this paper is to investigate the impact of inventory inaccuracy on the bullwhip effect in radio-frequency identification (RFID)-enabled supply chains and, in this context, to evaluate supply chain performance because of the RFID technology.Design/methodology/approachA simulation modeling method based on hierarchical timed colored petri nets is presented to model inventory management in multi-stage serial supply chains subject to inventory inaccuracy for various traditional and information sharing configurations in the presence and absence of RFID. Validation of the method is done by comparing results obtained for the bullwhip effect with published literature results.FindingsThe bullwhip effect is increased in RFID-enabled multi-stage serial supply chains subject to inventory inaccuracy. The information sharing supply chain is more sensitive to the impact of inventory inaccuracy.Research limitations/implicationsInformation sharing involves collaboration in market demand and inventory inaccuracy, whereas RFID is implemented by all echelons. To obtain the full benefits of RFID adoption and collaboration, different collaboration strategies should be investigated.Originality/valueColored petri nets simulation modeling of the inventory management process is a novel approach to study supply chain dynamics. In the context of inventory errors, information on RFID impact on the dynamic behavior of multi-stage serial supply chains is provided.

Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem

2009 ◽  
Vol 2 (4) ◽  
pp. 24-42 ◽  
Author(s):  
R. Dhanalakshmi ◽  
P. Parthiban ◽  
K. Ganesh ◽  
T. Arunkumar
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Raw Materials ◽  
Optimization Techniques ◽  
Network Design Problem ◽  
Production Plant ◽  
Two Stage ◽  
Cost Structures ◽  
Multi Stage ◽  
Product Costs

In many multi-stage manufacturing supply chains, transportation related costs are a significant portion of final product costs. It is often crucial for successful decision making approaches in multi-stage manufacturing supply chains to explicitly account for non-linear transportation costs. In this article, we have explored this problem by considering a Two-Stage Production-Transportation (TSPT). A two-stage supply chain that faces a deterministic stream of external demands for a single product is considered. A finite supply of raw materials, and finite production at stage one has been assumed. Items are manufactured at stage one and transported to stage two, where the storage capacity of the warehouses is limited. Packaging is completed at stage two (that is, value is added to each item, but no new items are created), and the finished goods inventories are stored which is used to meet the final demand of customers. During each period, the optimized production levels in stage one, as well as transportation levels between stage one and stage two and routing structure from the production plant to warehouses and then to customers, must be determined. The authors consider “different cost structures,” for both manufacturing and transportation. This TSPT model with capacity constraint at both stages is optimized using Genetic Algorithms (GA) and the results obtained are compared with the results of other optimization techniques of complete enumeration, LINDO, and CPLEX.

scholarly journals Analysis of Asymmetric Quantity Commitment in Decentralized Supply Chains

2021 ◽  
Vol 12 (2) ◽  
pp. 83-102
Author(s):  
Zhaoqiong Qin ◽  
Wen-Chyuan Chiang ◽  
Robert Russell
Keyword(s):  
Supply Chains ◽  
Price Competition ◽  
Just In Time ◽  
Lead Times ◽  
Backward Induction ◽  
Multi Stage ◽  
Stage Game ◽  
Time Systems

Quantity commitment chosen by firms in competition has been demonstrated by previous studies to mitigate price competition. This study demonstrates that asymmetric quantity commitment can always arise when one firm (e-tailer) shortens lead times or adopts just-in-time systems to circumvent quantity commitment while another firm (retailer) does not. To study the asymmetric quantity commitment in decentralization, a multi-stage game is analyzed, and backward induction is adopted. The authors find that the retailer always adopts the quantity commitment in the decentralization to achieve a higher profit.

Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem

2011 ◽  
pp. 275-289
Author(s):  
R. Dhanalakshmi ◽  
P. Parthiban ◽  
K. Ganesh ◽  
T. Arunkumar
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Raw Materials ◽  
Optimization Techniques ◽  
Network Design Problem ◽  
Production Plant ◽  
Two Stage ◽  
Cost Structures ◽  
Multi Stage ◽  
Product Costs

In many multi-stage manufacturing supply chains, transportation related costs are a significant portion of final product costs. It is often crucial for successful decision making approaches in multi-stage manufacturing supply chains to explicitly account for non-linear transportation costs. In this article, we have explored this problem by considering a Two-Stage Production-Transportation (TSPT). A two-stage supply chain that faces a deterministic stream of external demands for a single product is considered. A finite supply of raw materials, and finite production at stage one has been assumed. Items are manufactured at stage one and transported to stage two, where the storage capacity of the warehouses is limited. Packaging is completed at stage two (that is, value is added to each item, but no new items are created), and the finished goods inventories are stored which is used to meet the final demand of customers. During each period, the optimized production levels in stage one, as well as transportation levels between stage one and stage two and routing structure from the production plant to warehouses and then to customers, must be determined. The authors consider “different cost structures,” for both manufacturing and transportation. This TSPT model with capacity constraint at both stages is optimized using Genetic Algorithms (GA) and the results obtained are compared with the results of other optimization techniques of complete enumeration, LINDO, and CPLEX.

Multi-Stage Efficiency Tools for Goal Setting and Monitoring in Supply Chains

2005 ◽  
pp. 28-49 ◽  
Author(s):  
Marvin D. Troutt ◽  
Paul J. Ambrose ◽  
Chi Kin Chan
Keyword(s):  
Supply Chains ◽  
Continuous Improvement ◽  
Programming Models ◽  
Data Envelopment ◽  
Research Needs ◽  
Multi Stage ◽  
Upstream Process ◽  
Rate Optimization ◽  
Theoretical Results ◽  
Stage Efficiency

This chapter discusses the extension and potential application of some recent theoretical results on efficiency monitoring and throughput rate optimization for serial processes. In particular, we consider the relevance and adaptation of these results for use in monitoring and continuous improvement uses in supply chains or networks, with particular emphasis on the importance to e-business. Linear programming models based on ideas from Data Envelopment Analysis have been developed for maximizing the throughput of serial input-output processes in which one or more outputs of an upstream process become inputs to a successor process. We consider their adaptation to supply chain monitoring. We also propose some additional research needs in this area.

Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem

2011 ◽  
pp. 273-289
Author(s):  
R. Dhanalakshmi ◽  
P. Parthiban ◽  
K. Ganesh ◽  
T. Arunkumar
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Raw Materials ◽  
Optimization Techniques ◽  
Network Design Problem ◽  
Production Plant ◽  
Supply Chain Network ◽  
Cost Structures ◽  
Multi Stage ◽  
Product Costs

In many multi-stage manufacturing supply chains, transportation related costs are a significant portion of final product costs. It is often crucial for successful decision making approaches in multi-stage manufacturing supply chains to explicitly account for non-linear transportation costs. In this article, we have explored this problem by considering a Two-Stage Production-Transportation (TSPT). A twostage supply chain that faces a deterministic stream of external demands for a single product is considered. A finite supply of raw materials, and finite production at stage one has been assumed. Items are manufactured at stage one and transported to stage two, where the storage capacity of the warehouses is limited. Packaging is completed at stage two (that is, value is added to each item, but no new items are created), and the finished goods inventories are stored which is used to meet the final demand of customers. During each period, the optimized production levels in stage one, as well as transportation levels between stage one and stage two and routing structure from the production plant to warehouses and then to customers, must be determined. The authors consider “different cost structures,” for both manufacturing and transportation. This TSPT model with capacity constraint at both stages is optimized using Genetic Algorithms (GA) and the results obtained are compared with the results of other optimization techniques of complete enumeration, LINDO, and CPLEX.

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