Survival analysis of supply chain financial risk

2016 ◽  
Vol 17 (2) ◽  
pp. 130-151 ◽  
Author(s):  
Scott Dellana ◽  
David West
Keyword(s):  
Supply Chain ◽  
Survival Analysis ◽  
Financial Risk ◽  
Proportional Hazards ◽  
Risk Mitigation ◽  
Survival Function ◽  
Cox Proportional Hazards ◽  
Multiple Discriminant Analysis ◽  
Content Type ◽  
Simple Decision Rule

Purpose The purpose of this paper is to apply survival analysis, using Cox proportional hazards regression (CPHR), to the problem of predicting if and when supply chain (SC) customers or suppliers might file a petition for bankruptcy so that proactive steps may be taken to avoid a SC disruption. Design/methodology/approach CPHR is first compared to multiple discriminant analysis (MDA) and logistic regression (LR) to assess its suitability and accuracy to SC applications using three years of financial quarterly data for 69 non-bankrupt and 74 bankrupt organizations. A k-means clustering approach is then applied to the survival curves of all 143 organizations to explore heuristics for predicting the timing of bankruptcy petitions. Findings CPHR makes bankruptcy predictions at least as accurately as MDA and LR. The survival function also provides valuable information on when bankruptcy might occur. This information allows SC members to be prioritized into three groups: financially healthy companies of no immediate risk, companies with imminent risk of bankruptcy and companies with intermediate levels of risk that need monitoring. Originality/value The current paper proposes a new analytical approach to scanning and assessing the financial risk of SC members (suppliers or customers). Traditional models are able to predict if but not when a financial failure will occur. Lacking this information, it is impossible for SC managers to prioritize risk mitigation activities. A simple decision rule is developed to guide SC managers in setting these priorities.

scholarly journals Adapting the Default Weighted Survival Analysis Modelling Approach to Model IFRS 9 LGD

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 103
Author(s):  
Morne Joubert ◽  
Tanja Verster ◽  
Helgard Raubenheimer ◽  
Willem D. Schutte
Keyword(s):  
Survival Analysis ◽  
Financial Reporting ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Survival Curve ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Reporting Standard ◽  
International Financial Reporting Standard ◽  
Ifrs 9

Survival analysis is one of the techniques that could be used to predict loss given default (LGD) for regulatory capital (Basel) purposes. When using survival analysis to model LGD, a proposed methodology is the default weighted survival analysis (DWSA) method. This paper is aimed at adapting the DWSA method (used to model Basel LGD) to estimate the LGD for International Financial Reporting Standard (IFRS) 9 impairment requirements. The DWSA methodology allows for over recoveries, default weighting and negative cashflows. For IFRS 9, this methodology should be adapted, as the estimated LGD is a function of in the expected credit losses (ECL). Our proposed IFRS 9 LGD methodology makes use of survival analysis to estimate the LGD. The Cox proportional hazards model allows for a baseline survival curve to be adjusted to produce survival curves for different segments of the portfolio. The forward-looking LGD values are adjusted for different macro-economic scenarios and the ECL is calculated for each scenario. These ECL values are probability weighted to produce a final ECL estimate. We illustrate our proposed IFRS 9 LGD methodology and ECL estimation on a dataset from a retail portfolio of a South African bank.

scholarly journals Evaluation of Changes on World Stock Exchanges in Connection with the SARS-CoV-2 Pandemic. Survival Analysis Methods

Risks ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 121
Author(s):  
Beata Bieszk-Stolorz ◽  
Krzysztof Dmytrów
Keyword(s):  
Survival Analysis ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Stock Exchange ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Stock Exchanges ◽  
Analysis Methods ◽  
Hazards Model ◽  
Kaplan Meier

The aim of our research was to compare the intensity of decline and then increase in the value of basic stock indices during the SARS-CoV-2 coronavirus pandemic in 2020. The survival analysis methods used to assess the risk of decline and chance of rise of the indices were: Kaplan–Meier estimator, logit model, and the Cox proportional hazards model. We observed the highest intensity of decline in the European stock exchanges, followed by the American and Asian plus Australian ones (after the fourth and eighth week since the peak). The highest risk of decline was in America, then in Europe, followed by Asia and Australia. The lowest risk was in Africa. The intensity of increase was the highest in the fourth and eleventh week since the minimal value had been reached. The highest odds of increase were in the American stock exchanges, followed by the European and Asian (including Australia and Oceania), and the lowest in the African ones. The odds and intensity of increase in the stock exchange indices varied from continent to continent. The increase was faster than the initial decline.

scholarly journals KMplot.com: a survival analysis tool tailored for medical research (Preprint)

2021 ◽  
Author(s):  
András Lánczky ◽  
Balázs Győrffy
Keyword(s):  
Survival Analysis ◽  
Medical Research ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Multiple Hypothesis Testing ◽  
Analysis Tool ◽  
Web Based ◽  
False Discovery ◽  
Using Data ◽  
Basic Medical

UNSTRUCTURED Survival analysis is a cornerstone of medical research enabling the assessment of clinical outcome for disease progression and treatment efficiency. Despite its central importance, neither commonly used spreadsheet software can handle it nor is there a web server for its computation. Here we introduce a web-based tool capable to perform uni- and multivariate Cox proportional hazards survival analysis using data generated by genomic, transcriptomic, proteomic, or metabolomics studies. We implemented different methods to establish cutoff values for trichotomization or for the dichotomization of continuous data. False discovery rate is computed to correct for multiple hypothesis testing. Multivariate analysis option enables comparing omics data with clinical variables. The registration-free web-service is available at https://kmplot.com/custom_data. The tool fills a gap and will be an invaluable help for basic medical and clinical research.

Incorporating the risk of knowledge loss in supply chain risk management

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abdelkader Daghfous ◽  
Abroon Qazi ◽  
M. Sajid Khan
Keyword(s):  
Risk Management ◽  
Supply Chain ◽  
Risk Mitigation ◽  
Relevant Literature ◽  
Future Research ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Knowledge Loss ◽  
Content Type ◽  
Knowledge Based

PurposeThe literature on supply chain risk management (SCRM) has investigated a multitude of supply chain risks. This paper aims to make a case for the importance of managing the risk of knowledge loss in the supply chain management (SCM) function and incorporating knowledge loss as a critical risk within the SCRM process.Design/methodology/approachThis paper adopts a knowledge-based view of the SCRM process and attempts to bring to light insights based on a synthesis of the relevant literature. The authors conducted a systematic literature review of peer-reviewed articles published between 1998 and 2019. Further, a case study was conducted to illustrate the significance of the risk of knowledge loss in the SCM function in terms of how it operates and why it has such a significant impact on performance.FindingsKnowledge loss is a relatively neglected type of supply chain risk that can be added to the existing typologies. This paper argues that knowledge loss in the SCM function has the propensity to significantly impact the performance of the focal firm, exacerbate other types of supply chain risk and impede risk mitigation efforts. We put forth several strategies that supply chain managers can adopt to mitigate the risk of knowledge loss in their function.Research limitations/implicationsThis paper generates an exploratory opening that could pave the way for a systematic theory of knowledge loss as a supply chain risk and future empirical research. The study culminates in a number of important insights and initiatives for supply chain managers to recognize and manage the risk of knowledge loss.Originality/valueThis paper argues for the importance of incorporating the risk of knowledge loss in SCRM research and practice. It also provides an examination of some promising angles for future research in SCRM from a knowledge-based perspective.

Supply chain information sharing: challenges and risk mitigation strategies

2016 ◽  
Vol 27 (8) ◽  
pp. 1102-1126 ◽  
Author(s):  
Thi Thanh Huong Tran ◽  
Paul Childerhouse ◽  
Eric Deakins
Keyword(s):  
Supply Chain ◽  
New Zealand ◽  
Information Sharing ◽  
Data Exchange ◽  
Risk Mitigation ◽  
Exchange Process ◽  
Small Sample ◽  
Mitigation Strategies ◽  
Structured Interviews ◽  
Content Type

Purpose The purpose of this paper is to investigate how managers perceive risks associated with sharing information with trading partners, and how they attempt to mitigate them. Design/methodology/approach In this exploratory New Zealand study, qualitative research was conducted involving semi-structured interviews with boundary spanning managers who are responsible for inter-organizational interfaces. Multiple case studies in different industries are used to highlight managers’ perceptions of risks in data exchange process throughout the supply network, and their underlying reasoning. Findings Managers perceive several types of risks when exchanging information across external supply chain interfaces, and adopt different approaches to handling them. The research also reinforces the vital role played by interpersonal relationships and trust as key enablers of inter-organizational cooperation. Research limitations/implications The findings are based on a small sample of 11 case companies based in a single New Zealand province, thereby potentially restricting generalizability. Future work could usefully extend the sample size in order to investigate the correlations between firm sizes, levels of trust, and degrees of data integration within particular industry sectors. Practical implications The findings will help managers understand and evaluate different types of risks in the data exchange process, and enable them to make better decisions that enhance information sharing and supply chain performance. Originality/value Perceived information sharing risks are peculiar to the individual actors, and as such need to be mitigated through changes to their socially constructed perceptions. This work extends the literature on understanding the various dimensions of inter-organizational information sharing.

Risk assessment and operational approaches to managing risk in global supply chains

2014 ◽  
Vol 25 (6) ◽  
pp. 873-890 ◽  
Author(s):  
Sameer Kumar ◽  
Katie J. Himes ◽  
Collin P. Kritzer
Keyword(s):  
Supply Chain ◽  
Design Methodology ◽  
Risk Mitigation ◽  
Global Supply Chains ◽  
Design Strategies ◽  
Content Type ◽  
Risk Areas ◽  
Managing Risk ◽  
Supply Chain Risks ◽  
A Company

Purpose – The purpose of this paper is to provide the organization with a process for assessing risk associated with their supply chain and a framework from which they can build their strategy to manage risk. Design/methodology/approach – The proposed process is based on a compilation of research and interactions with supply chain managers in various industries, and these sources provide a specific process to identify how critical the risk is, when to act upon it, and how to manage it. An adapted risk mitigation framework organizes strategies according to the likelihood of disruption and consequences. Included is an industry example used to demonstrate the framework. Findings – The variability and uncertainty associated with supply chain risks make disruption difficult to predict. Furthermore, getting information from suppliers about the amount of risk associated with their operation in an attempt to scope one's own risk can be a challenge. Management must consider the amount of risk the organization is going to accept and how much to invest to mitigate it. Originality/value – To manage the risk associated with supply chain disruption, an organization must deploy a strategy for assessing it. Once risk areas have been identified, the organization must design strategies which will mitigate the risk. The depth and degree to which risk is mitigated depends upon how risk-averse a company is and what they are willing to invest in this activity.

scholarly journals Risk of depressive disorders after tobacco smoking cessation: a retrospective cohort study in Fukuoka, Japan

BMJ Open ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. e025124 ◽  
Author(s):  
Takako Fujita ◽  
Akira Babazono ◽  
Yumi Harano ◽  
Peng Jiang
Keyword(s):  
Smoking Cessation ◽  
Cohort Study ◽  
Survival Analysis ◽  
Retrospective Cohort ◽  
Depressive Disorders ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Proportional Hazards Models ◽  
Cox Proportional Hazards Models ◽  
Significant Difference

ObjectiveWe sought to examine the effect of smoking cessation on subsequent development of depressive disorders.DesignThis was a retrospective cohort study.MethodsWe used administrative claim and health check data from fiscal years 2010 to 2014, obtained from the largest health insurance association in Fukuoka, Japan. Study participants were between 30 and 69 years old. The end-point outcome was incidence of depressive disorders. Survival analysis and Cox proportional hazards models were conducted. The evaluated potential confounders were sex, age, standard monthly income and psychiatric medical history.ResultsThe final number of participants was 87 255, with 7841 in the smoking cessation group and 79 414 in the smoking group. The result of survival analysis showed no significant difference in depressive disorders between the two groups. The results of Cox proportional hazards models showed no significant difference by multivariate analysis between participants, including users of smoking cessation medication (HR 1.04, 95% Cl 0.89 to 1.22) and excluding medication use (HR 0.97, 95% Cl 0.82 to 1.15).ConclusionsThe present study showed that there were no significant differences with respect to having depressive disorders between smoking cessation and smoking groups. We also showed that smoking cessation was not related to incidence of depressive disorders among participants, including and excluding users of smoking cessation medication, after adjusting for potential confounders. Although the results have some limitations because of the nature of the study design, our findings will provide helpful information to smokers, health professionals and policy makers for improving smoking cessation.

scholarly journals Reverse logistics risk management: identification, clustering and risk mitigation strategies

2020 ◽  
Vol 58 (7) ◽  
pp. 1449-1474 ◽  
Author(s):  
Hamidreza Panjehfouladgaran ◽  
Stanley Frederick W.T. Lim
Keyword(s):  
Risk Factors ◽  
Risk Management ◽  
Supply Chain ◽  
Reverse Logistics ◽  
Risk Mitigation ◽  
Mitigation Strategies ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Content Type ◽  
Risk Mitigation Strategies

PurposeReverse logistics (RL), an inseparable aspect of supply chain management, returns used products to recovery processes with the aim of reducing waste generation. Enterprises, however, seem reluctant to apply RL due to various types of risks which are perceived as posing an economic threat to businesses. This paper draws on a synthesis of supply chain and risk management literature to identify and cluster RL risk factors and to recommend risk mitigation strategies for reducing the negative impact of risks on RL implementation.Design/methodology/approachThe authors identify and cluster risk factors in RL by using risk management theory. Experts in RL and supply chain risk management validated the risk factors via a questionnaire. An unsupervised data mining method, self-organising map, is utilised to cluster RL risk factors into homogeneous categories.FindingsA total of 41 risk factors in the context of RL were identified and clustered into three different groups: strategic, tactical and operational. Risk mitigation strategies are recommended to mitigate the RL risk factors by drawing on supply chain risk management approaches.Originality/valueThis paper studies risks in RL and recommends risk management strategies to control and mitigate risk factors to implement RL successfully.

A contextual approach to supply chain risk mitigation

2015 ◽  
Vol 26 (3) ◽  
pp. 642-656 ◽  
Author(s):  
Woojung Chang ◽  
Alexander E. Ellinger ◽  
Jennifer Blackhurst
Keyword(s):  
Supply Chain ◽  
Risk Mitigation ◽  
Business Students ◽  
Mitigation Strategies ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Content Type ◽  
Risk Mitigation Strategies ◽  
And Performance ◽  
Research Propositions

Purpose – As global supply networks proliferate, the strategic significance of supply chain risk management (SCRM) – defined as the identification, evaluation, and management of supply chain-related risks to reduce overall supply chain vulnerability – also increases. Yet, despite consistent evidence that firm performance is enhanced by appropriate fit between strategy and context, extant SCRM research focusses more on identifying sources of supply chain risk, types of SCRM strategy, and performance implications associated with SCRM than on the relative efficacy of alternative primary supply chain risk mitigation strategies in different risk contexts. Drawing on contingency theory, a conceptual framework is proposed that aligns well-established aspects of SCRM to present a rubric for matching primary alternative supply chain risk mitigation strategies (redundancy and flexibility) with particular risk contexts (severity and probability of risk occurrence). The paper aims to discuss these issues. Design/methodology/approach – Conceptual paper. Findings – The proposed framework addresses supply chain managers’ need for a basic rubric to help them choose and implement risk mitigation approaches. The framework may also prove helpful for introducing business students to the fundamentals of SCRM. Originality/value – The framework and associated research propositions provide a theoretically grounded basis for managing the firm’s portfolio of potential supply chain risks by applying appropriate primary risk mitigation strategies based on the specific context of each risk rather than taking a “one size fits all” approach to risk mitigation. An agenda for progressing research on contingency-based approaches to SCRM is also presented.

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