Proposal of a method for selecting suppliers considering risk management

Modeling of factors affecting supplier selection on start-ups during frequent pandemic episodes like COVID-19

Purpose

In developing countries such as India, start-ups play an essential role in “industrial output,” “Gross Domestic Product ” and “employment creation.” Evidence suggests that pandemics have risen over the last century due to rising global travel and assimilation, urbanization, alterations in land use, and significantly larger exploitation of the natural environment. These trends are likely to continue and intensify. These pandemic episodes affect businesses, especially start-ups. Supplier selection is among the vital critical elements that start-ups must include in start-ups' strategy procedures during the pandemic episodes. This study's focus is to “identify,” “analyze,” and “categorize” the factors affecting supplier selection in start-ups during frequent pandemic episodes like coronavirus disease 2019 (COVID-19).

Design/methodology/approach

Through “literature review” and “experts' opinion” from various start-ups in India, ten affecting factors were identified. Total Interpretative Structural Modeling (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) were employed to analyze the interrelationship among the factors affecting the supplier selection on start-ups during frequent pandemic episodes, and these factors were ranked as “autonomous,” “independent,” “linkage,” and “dependent” factors.

Findings

The findings show that “performance history,” “service levels,” “technical capability,” and “financial stability” are the most critical factors affecting the supplier selection on start-ups during frequent pandemic episodes. The next importance should be safety and environmental concern” and “quality.”

Research limitations/implications

The factors affecting supplier selection on start-ups during frequent pandemic episodes are the current focus of this study. This study is mainly performed on Indian start-ups and can be extended to other countries.

Practical implications

The start-ups can rely on this study to clearly understand the factors affecting the supplier selection on start-ups during frequent pandemic episodes.

Originality/value

There is no research regarding factors affecting supplier selection on start-ups during the COVID-19 emergencies. This research gap is filled by analyzing aspects linked to supplier selection in start-ups. This gap inspired the present study, which employs the “Total Interpretive Structural Modeling (TISM)” technique to uncover supplier selection determinants and investigate hierarchical interconnections among factors influencing/affecting supplier selection in start-ups during frequent pandemic episodes.

Worst Expected Best method for assessment of probabilistic network expected value at risk: application in supply chain risk management

Purpose

The purpose of this paper is to develop and operationalize a process for prioritizing supply chain risks that is capable of capturing the value at risk (VaR), the maximum loss expected at a given confidence level for a specified timeframe associated with risks within a network setting.

Design/methodology/approach

The proposed “Worst Expected Best” method is theoretically grounded in the framework of Bayesian Belief Networks (BBNs), which is considered an effective technique for modeling interdependency across uncertain variables. An algorithm is developed to operationalize the proposed method, which is demonstrated using a simulation model.

Findings

Point estimate-based methods used for aggregating the network expected loss for a given supply chain risk network are unable to project the realistic risk exposure associated with a supply chain. The proposed method helps in establishing the expected network-wide loss for a given confidence level. The vulnerability and resilience-based risk prioritization schemes for the model considered in this paper have a very weak correlation.

Originality/value

This paper introduces a new “Worst Expected Best” method to the literature on supply chain risk management that helps in assessing the probabilistic network expected VaR for a given supply chain risk network. Further, new risk metrics are proposed to prioritize risks relative to a specific VaR that reflects the decision-maker's risk appetite.

Impact of supply risk management on firm performance: a case of the Indian electronics industry

Purpose

The purpose of this research is to study and examine the influence of systematic supply risk management (SRM) on the buyer's firm performance, mainly focussing on the Indian electronics industry.

Design/methodology/approach

The study has framed a set of hypotheses on the risk management model. A thorough literature review and experts' opinion were considered in framing constructs and hypothesis for the model. We adopted self-administration questionnaires mainly focusing on the Indian electronics industry. The derived hypothesis is tested using partial least squares (PLS) method from 140 survey data pertaining to small, medium and large scale industries.

Findings

Study justify that constructs with high loadings for risk identification, risk assessment, risk reduction, and risk monitoring supports all hypothesized relation to better risk management. The model captures superior risk identification, risk control and risk monitoring for overall firm performance, but fails to justify with organization supply risk assessment process on overall firm performance.

Research limitations/implications

The study mainly focused on SRM process on firm performance. Study mainly focused on single survey responses and expert's perceptions on SRM practices in Indian electronics industry.

Practical implications

Research empirically justifies the effects of SRM process on organization performance. Furthermore, effective SRM practices assist decision makers framing corrective strategies to mitigate risk occurrences and their negative impact.

Originality/value

This empirical work provides a deep understanding of SRM process on the Indian electronics industry and their perception towards firm performance. Moreover, this is one of the few empirical studies addressing SRM practices in the Indian electronics industry.

Product recall strategy in the supply chain: utility and culture

Purpose

Product recalls have the potential to damage firm and consumer quality reputation. While globalization has brought about various economic benefits, expanding supply chain networks have also made it more difficult for downstream organizations to manage product recall strategy. This study aims to examine the role of culture on a manufacturer's initiation of a recall and the severity of the remedy chosen for the product recall.

Design/methodology/approach

Utilizing the culture-specific argument, this study uses an exploratory approach to assess how cultural variables impact recall strategy utilizing a large-scale data analysis with a cross-sectional time-series panel of 898 firms.

Findings

The results provide support for the expected utility hypothesis that the more severe the consequence, the more likely a manufacturer will decide to recall the product. Moreover, the more likely the manufacturer will provide greater returns to the consumer. However, these relationships are impacted to differing degrees by the manufacturer's cultural origin.

Originality/value

These results provide evidence to researchers about how culture impacts the expected utility hypothesis in the decision theory. The study examines how deeply embedded cultural variables impact the relationship between the foreseeable consequence of the product recall and the recall facilitator and remedy.