Incentives to improve the service level in a random yield supply chain: The role of bonus contracts

Yin, Zhe; Ma, Shihua

doi:10.1016/j.ejor.2015.02.006

Cited by 31 publications

(6 citation statements)

References 35 publications

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“…Li et al. (2013), Yin and Ma (2015), and Pal and Sana (2018), and studies referenced therein, explore supply chain coordination issues under the random yields environment. Recent research on random yield in a multi‐period setting focuses on information learning (see, e.g., Tomlin, 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Purchasing Policy for Fresh Products from Multiple Supply Sources with Considerations of Random Delivery Times, Risk, and Information

Cai

Zhang

2020

Decision Sciences

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We consider a problem in which a distributor purchases fresh products from multiple supply sources for subsequent sale at a wholesale market. Due to uncontrollable factors such as traffic congestion or bad weather conditions, the time taken for the product to be delivered from each production origin is uncertain. As the wholesale market is open for trading only in a fixed‐time window, any fresh products arriving earlier face the risk of decay, and any arriving later cannot be sold. The market demand for the product is random and follows a general distribution. We formulate the basic problem as a multi‐source selection model with random yield, taking into account the trade‐off between purchasing cost and other costs such as deterioration loss arising from delivery uncertainty. We derive the optimal order quantities from different origins and propose algorithms to search for them. We also investigate the effects of delivery uncertainty on service level and transport mode selection. Finally, we examine two extensions. One considers a capacity limit in each production origin, and the other incorporates information updating. For the first extension, we propose algorithms to compute the optimal solution based on its structural properties. For the second problem, we derive the optimal purchasing policy according to updated information such as details of the arrivals of previous orders.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Purchasing Policy for Fresh Products from Multiple Supply Sources with Considerations of Random Delivery Times, Risk, and Information

Cai

Zhang

2020

Decision Sciences

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“…Case studies also help to generalize, as the results of the findings using various cases can facilitate some form of replication (Noor, 2008). Besides, conducting other case studies can also diminish the errors and bias of a study through case study procedures and a case database development (Yin andMa, 2015 in Takahashi andAraujo, 2019).…”

Section: Case Studymentioning

confidence: 99%

Benefits and Challenges of Dmaic Methodology Implementation in Service Companies: An Exploratory Study

Subagyo¹,

Saraswati²,

Trilaksono³

et al. 2020

JAM

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The DMAIC (Define, Measurement Analyze, Improve, Control) methodology of Six Sigma has been widely recognized as a breakthrough process improvement strategy for application among human resources that offers an impressive reduction in defects or errors in many business processes. Even though Six Sigma’s DMAIC was predominantly applied in the manufacturing industry, recent studies have revealed that it has been implemented effectively in the service industry as well. This article aims to explore the benefits and the fundamental challenges of DMAIC implementation as a student’s course project in five Indonesian service companies. An exploratory study was conducted to provide a clear understanding of DMAIC method implementation in service companies. A similar challenge, the lack of openness of the company, was found across cases. Several enhancements in company performance were identified, including improved customer satisfaction, reduction in delivery errors, and shorter waiting time. This research also highlighted the usage of some non-statistical tools and techniques that effectively contribute to each phase of the DMAIC method. Lastly, all five companies reported that these improvement projects offer positive benefits, including increased revenue and cost reduction.

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“…Operational performance of the ACTGSC were defined in terms of customer value and business performance (Wieland and Wallenburg, 2012). Product availability, quality consistency, on-time delivery and order fulfilment correctness were identified as the key indicators of customer value as they were highly valued by customers (Gaudenzi and Borghesi, 2006;Yin and Ma, 2015). The four indicators were also validated by data mining 709 consumer comments on buying Australian table grapes from two popular Chinese online sellers.…”

Section: Modelling Objectivementioning

confidence: 99%

“…The upstream firm's "supply discontinuity" does not have a significant influence on the downstream firm's "customer value risk" but does result in increased "profit slippage" for the downstream firm. As such, Australian table grape suppliers should make efforts to ensure supply continuity which would keep customers satisfied (Yin and Ma, 2015). As natural disaster is the root cause of supply discontinuity, Australian suppliers should operate geographically diversified farms or work with third-party growers to mitigate natural disasters.…”

Section: Managerial Implicationsmentioning

confidence: 99%

An Ontology-based Bayesian network modelling for supply chain risk propagation

Cao

Bryceson

Hine

2019

IMDS

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Purpose Supply chain risks (SCRs) do not work in isolation and have impact both on each member of a chain and the performance of the entire supply chain. The purpose of this paper is to quantitatively assess the impact of dynamic risk propagation within and between integrated firms in global fresh produce supply chains. Design/methodology/approach A risk propagation ontology-based Bayesian network (BN) model was developed to measure dynamic SCR propagation. The proposed model was applied to a two-tier Australia-China table grape supply chain (ACTGSC) featured with an upstream Australian integrated grower and exporter and a downstream Chinese integrated importer and online retailer. Findings An ontology-based BN can be generated to accurately represent the risk domain of interest using the knowledge and inference capabilities inherent in a risk propagation ontology. In addition, the analyses revealed that supply discontinuity, product inconsistency and/or delivery delay originating in the upstream firm can propagate to increase the downstream firm’s customer value risk and business performance risk. Research limitations/implications The work was conducted in an Australian-China table grape supply chain, so results are only product chain-specific in nature. Additionally, only two state values were considered for all nodes in the model, and finally, while the proposed methodology does provide a large-scale risk network map, it may not be appropriate for a large supply chain network as it only follows the process flow of a single supply chain. Practical implications This study supports the backward-looking traceability of risk root causes through the ACTGSC and the forward-looking prediction of risk propagation to key risk performance measures. Social implications The methodology used in this paper provides an evidence-based decision-making capability as part of a system-wide risk management approach and fosters collaborative SCR management, which can yield numerous societal benefits. Originality/value The proposed methodology addresses the challenges in using a knowledge-based approach to develop a BN model, particularly with a large-scale model and integrates risk and performance for a holistic risk propagation assessment. The combination of modelling approaches to address the issue is unique.

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Incentives to improve the service level in a random yield supply chain: The role of bonus contracts

Cited by 31 publications

References 35 publications

Optimal Purchasing Policy for Fresh Products from Multiple Supply Sources with Considerations of Random Delivery Times, Risk, and Information

Optimal Purchasing Policy for Fresh Products from Multiple Supply Sources with Considerations of Random Delivery Times, Risk, and Information

Benefits and Challenges of Dmaic Methodology Implementation in Service Companies: An Exploratory Study

An Ontology-based Bayesian network modelling for supply chain risk propagation

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