Modeling the enablers of sourcing risks faced by startups in COVID-19 era

Purpose Startups across all sectors are affected by the COVID-19 pandemic and are facing a formidable challenge in terms of marketing and operations. Most of the startups have experienced a downturn in demand and supply due to COVID-19 led disturbances in sourcing networks. This paper aims to identify, analyze and categorize the significant risks influencing sourcing operations in startups during the COVID-19 era in India, using the total interpretive structural modeling (TISM) approach. Design/methodology/approach Eight enablers were identified through literature review and expert opinions from various startups in India. This study adopted the TISM approach to analyze the inter-relationships between the enablers. Multiplication Applied to Classification (MICMAC) analysis was used to rank the sourcing risk enablers and classify them as autonomous, independent, linkage and dependent enablers. Findings The results indicate “insufficient fund” as the most critical enabler. Network issues and employee flexibility risk were among the other critical enablers that have a high driving power. Supplier risk, quality risk and demand risk were found to have highly dependent on other enablers for implementation. Research limitations/implications This study mainly focuses on the sourcing risks in startup operations in India. This study can be extended to many other countries. Practical implications This study will help startup industry managers and practitioners understand the interactions of enablers and identify critical enablers to mitigate risks in startup sourcing operations in the COVID-19 era. Originality/value The present study identifies the sourcing risk enablers in the COVID-19 era. It is the first attempt to analyze the interrelationship among sourcing risks in startups using the TISM approach.

Strategies to mitigate the impact of COVID-19 on supply chain disruptions: a multiple case analysis of buyers and distributors

PurposeThis paper explores the steps/countermeasures taken by buying and distributing firms to address supply chain disruptions caused by COVID-19.Design/methodology/approachThis study employs a multiple case study methodology and conducts 36 semi-structured interviews with senior managers of nine different firms producing, procuring or distributing products from China and other highly impacted South Asian regions (Pakistan, Sri Lanka, India).FindingsResults reveal that buying firms are moving to agile production, focusing on tier-1 supplier risk, enhancing inbound material visibility and temporarily closing production facilities to respond to the challenges posed by COVID-19. Furthermore, distribution centres are modifying their inventory policies, evaluating alternative outbound routes and sources of supply to manage disruptions caused to their business operations amid COVID-19 outbreak.Practical implicationsSupply chain firms can use the countermeasures provided in this study to mitigate the impact of COVID-19 and make the best out of this pandemic.Originality/valueThis study contributes to the supply chain literature by exploring the countermeasures taken by firms to mitigate the impact of COVID-19. In particular, this study explores such countermeasures from the perspective of two different entities (buyers and distributors) along the supply chain. Firms can use the countermeasures highlighted in this study to mitigate the impact of COVID-19 on the supply chain.

RISK ANALYSIS IN JAKARTA’S WASTE COOKING OIL TO BIODIESEL GREEN SUPPLY CHAIN USING GROUP AHP APPROACH

The Jakarta regulation for waste cooking oil (WCO) shows the desired WCO to Biodiesel supply chain through the DKI Jakarta Governor Regulation Number 167 the Year 2016. Still, the implementation of said regulation proved inefficient. The study aims to analyze the risks in the supply chain because the WCO to Biodiesel supply chain is vulnerable to different risks than the typical supply chain and the green supply chain. The method used in this research is the group analytical hierarchy process (G-AHP) approach to create a consensus model between actors of the supply chain. Deep interviews were conducted with six experts to identify the risks and the normal scale was used to quantify their preference. Then, the PriEst software assisted the risk weight calculation, AHP matrix validation, and consensus modelling. The findings show the supply chain is vulnerable to 23 risks, categorized into six risk categories. The three risks that cause the most uncertainties in the supply chain are supply chain design risk, key supplier risk, and financial source risk. Technology risks and asset failure risks are the least concern because most WCO conversion is not done in Indonesia. These findings would be useful for the government to focus its effort on the most critical risks.