Development of risk based dynamic backorder replenishment planning framework using Bayesian Belief Network

Shin, KwangSup; Shin, Yong-Woo; Kwon, Jihye; Kang, Suk‐Ho

doi:10.1016/j.cie.2011.11.015

Cited by 21 publications

(10 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where Q(t) is the satisfied OD demands at time t. ρ t ij indicates whether or not the demand q ij from supply node i to demand node j is satisfied. Since we assume that all supplies are delivered from the supply nodes to the demand nodes by the fastest path, the minimum travel time z t ij between each OD pair at time t is solved by the Dijkstra method [33,34] formulated in Equations 3and (4).…”

Section: Problem Statementmentioning

confidence: 99%

Resilience-Based Restoration Model for Supply Chain Networks

Mao

Lou²,

Cao

et al. 2020

Mathematics

View full text Add to dashboard Cite

An optimal restoration strategy for supply chain networks can efficiently schedule the repair activities under resource limits. However, a wide range of previous studies solve this problem from the perspective of cost-effectiveness instead of a resilient manner. This research formulates the problem as a network maximum-resilience decision. We develop two metrics to measure the resilience of the supply chain networks, i.e., the resilience of cumulative performance loss and the resilience of restoration rapidity. Then, we propose a bi-objective nonlinear programming model, which aims to maximize the network resilience under the budget and manpower constraints. A modified simulated annealing algorithm is employed to solve the model. Finally, a testing supply chain network is utilized to illustrate the effectiveness of the proposed method framework. The results show that the optimal restoration schedule generated by the proposed model is a tradeoff between the cumulative performance loss and the restoration rapidity. Additionally, the sensitivity analysis of parameters indicates that decision-maker’s preference, tolerance factor of delivery time, number of work crews, and availability of budget all have significant impacts on the restoration schedule.

show abstract

Section: Problem Statementmentioning

confidence: 99%

Resilience-Based Restoration Model for Supply Chain Networks

Mao

Lou²,

Cao

et al. 2020

Mathematics

View full text Add to dashboard Cite

show abstract

“…To minimize the supply chain and inventory control costs, a risk-based dynamic backorder replenishment planning framework was proposed by Shin et al [25] applying the Bayesian Belief Network. A similar framework was prescribed by Acar and Gardner [26], using optimization and simulation techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques

2020

View full text Add to dashboard Cite

When a customer orders a product, which is not available in the store or temporary out of stock, and the customer decides to wait until the product is available and promised to be shipped, then this scenario is called backorder of that specific product [1, 2]. If backorders are not handled promptly, they will have high impacts on the respective company's revenue, share market price, customers' trust, and may end up losing the customer

show abstract

“…Kao et al (2005) propose diagnostic reasoning using dynamic Bayesian networks for cause-effect relationship in industrial supply chains. Bayesian network is used to devise a framework of backorder replenishment planning; based on shifts of risk within the supply chain (Shin et al, 2012). Bayesian network and total cost of ownership is used to solve the problem in selecting a facility location for manufacturing (Dogan, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of operating effectiveness of a cold chain model using Bayesian networks

Sharma

Pai

2015

Business Process Management Journal

View full text Add to dashboard Cite

Purpose – Cold chain has become an integral part of the supply chain domain. The purpose of this paper is to consider all the significant factors in a single study. This will result into a better model to study the effectiveness of a cold chain because there has been absence of such an integrated study. Design/methodology/approach – The basis of the factors is justified by performing extensive literature review. Inter relations are drawn based on critical analysis of each factor and its implications on cold chain. Bayesian Network is used to develop the model. Findings – The end result is an established model, depicting the interdependencies of the factors. The model ultimately provides effectiveness of a given cold chain when the corresponding values of factors are put in. Practical implications – The findings will be helpful for government and non-government bodies to analyse the effectiveness of a cold chain. This can be used to increase the performance of different stages in the cold chain. From a business perspective, an investor can analyse the cold chains of various geographies in order to make an investment decision. Originality/value – The value lies in developing and introducing new factors which were not considered in the related literature previously. To identify the inter relations among the factors in order to build a causal model is another contribution of the present paper. This would assist in decision-making process with respect to any given cold chain. It can be applied to any cold chain as proposed model is not specific to a particular country or material.

show abstract

Development of risk based dynamic backorder replenishment planning framework using Bayesian Belief Network

Cited by 21 publications

References 47 publications

Resilience-Based Restoration Model for Supply Chain Networks

Resilience-Based Restoration Model for Supply Chain Networks

Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques

Analysis of operating effectiveness of a cold chain model using Bayesian networks

Contact Info

Product

Resources

About