Assessing supply chain risk for apparel production in low cost countries using newsfeed analysis

Abstract

Purpose With the growth of unstructured data, opportunities to generate insights into supply chain risks in low cost countries (LCCs) are emerging. Sourcing risk has primarily focused on short-term mitigation. This paper aims to offer an approach that uses newsfeed data to assess regional supply base risk in LCC’s for the apparel sector, which managers can use to plan for future risk on a long-term planning horizon. Design/methodology/approach This paper demonstrates that the bulk of supplier risk assessments focus on short-term responses to disruptions in developed countries, revealing a gap in assessments of long-term risks for supply base expansion in LCCs. This paper develops an approach for predicting and planning for long-term supply base risk in LCC’s to address this shortfall. A machine-based learning algorithm is developed that uses the analysis of competing hypotheses heuristic to convert data from multiple news feeds into numerical risk scores and visual maps of supply chain risk. This paper demonstrates the approach by converting large amounts of unstructured data into two measures, risk impact and risk probability, leading to visualization of country-level supply base risks for a global apparel company. Findings This paper produced probability and impact scores for 23 distinct supply base risks across 10 countries in the apparel sector. The results suggest that the most significant long-term risks of supply disruption for apparel in LCC’s are human resource regulatory risks, workplace issues, inflation costs, safety violations and social welfare violations. The results suggest that apparel brands seeking suppliers in the regions of Cambodia, India, Bangladesh, Brazil and Vietnam should be aware of the significant risks in these regions that may require mitigative action. Originality/value This approach establishes a novel approach for objectively projecting future global sourcing risk, and yields visually mapped outcomes that can be applied in forecasting and planning for future risks when considering sourcing locations in LCC’s.

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

“… I11 Supply chain coordination SC coordination describes collective efforts to reach goals, which is “the act of managing dependencies between entities and the joint effort of entities working together towards mutually defined goals.” Li et al (2018) ; Zhao et al (2020) ; Hosseini-Motlagh et al (2020) ; Heydari et al (2019) ; Hosseini-Motlagh et al (2019) ; Oliveira & Handfield (2019) ; Esmaeili-Najafabadi et al (2019) ; Liu et al (2017) ; Wang et al (2017) ; Li et al (2017) ; Danusantoso & Moses (2016) ; Xiao & Chen (2016) ; Ali & Nakade (2016) ; Zheng et al (2015) ; Mortazavi et al (2015) ; Watanabe & Kusukawa (2015) ; Chen et al (2020) ; Chakraborty et al (2020) ; Zhao et al (2020) . I12 Supply chain disruptions SC disruptions are undesired and unexpected events; examples include natural disasters, industrial accidents, technological shifts, and political events Braunscheidel and Suresh (2009) ; Kumaran et al (2020) ; Parast (2020) ; Paul & Chowdhury (2020) ; Messina et al (2020) ; Nguyen et al (2020) ; Zhu et al (2020) ; Handfield et al (2020) ; Govindan et al (2020b) ; Kumar & Anbanandam (2020) ; Salmi et al (2020) ; Azadegan et al (2020) ; Birkel & Hartmann (2020) ; Sheu & Kuo (2020) ; Wamba & Queiroz (2020) ; Jahani et al (2020) ; Polyviou et al (2019) ; Queiroz et al (2019) ; Ni et al (2019) . <...>…”

“… I20 Supply chain vulnerability SC vulnerability is the susceptibility or exposure to a disruptive event in the supply chain Blackhurst et al (2018) ; Azadegan et al (2020) ; Viljoen & Joubert (2018) ; Nakatani et al (2018) ; Konig & Spinler (2016) ; Chowdhury & Quaddus (2016) ; Chen et al (2015 b); Ethirajan et al (2020) . I21 Sustainability Sustainability in the SC is defined as managing the supply chain functions aligned with the social, environmental, and economic sustainability requirements of the stakeholders to reduce sustainability risks in supply chain and improve market performance Seuring & Müller (2008) ; Sharifi et al (2020) ; Chatterjee & Layton (2020) ; Chen et al (2020) ; Handfield et al (2020) ; Zhu & Krikke (2020) ; Kamble et al (2020) ; Niu et al (2020) ; Quayson et al (2020) ; Ivanov (2020) ; Shareef et al (2020) ; He et al (2020) ; Nayak & Dhaigude (2019) ; Maiyar & Thakkar (2020) . I22 Uncertainty “Uncertainty” refers to a state that cannot be directly expressed by a certain amount of information, which describes a situation that cannot be ascertained, or where there are different possibilities.…”

The Impact of the Coronavirus Pandemic on Supply Chains and Their Sustainability: A Text Mining Approach

“…Text mining allows detailed insights on the impact of the coronavirus pandemic on SC. While similar tools start to emerge in related literature (e.g., Handfield et al, 2020), this is still a rarely applied method and has not yet been used in the COVID-19 SC-related literature. So the review paper by Queiroz et al (2020) does not provide evidence of empirical material being analyzed.…”

“…During this so-called "information age, " an era suffering from information overload, as big amounts of data are collected on a daily basis, the need to analyze this data arose. Slowly developing into the "data age, " data mining is seen as an attractive method to tackle this problem, where analyzing news feeds emerges as a promising research approach (e.g., Handfield et al, 2020).…”

“…347f.). As a consequence of greater uncertainties in supply and demand, globalization of markets, and shorter product and technology life cycles, managing risks has become more challenging (Rao and Goldsby, 2009, p. 98;Handfield et al, 2020).…”