Guest editorial: Bridging the research-practice gaps in supply chain management: lessons from COVID-19

Wamba, Samuel Fosso; Dubey, Rameshwar; Bryde, David; Foropon, Cyril; Gupta, Michèle

doi:10.1108/ijlm-10-2022-600

Cited by 2 publications

(3 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, 2021). Unexpected crises like the pandemic give managers the chance to analyze their SCM and identify the causes of its disruptions (Fosso Wamba et al. , 2022).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Indeed, the pandemic crisis has brought attention to resilience issues (Das et al, 2022), with scholars calling for new research on resilience theory in the SCM context (Veile, 2022). AI includes the corpus of knowledge allowing machines to behave in intelligent human-like manners (Bawack et al, 2021); hence, AI can generate positive impacts on the agility, resilience and performance of a supply chain (Dubey et al, 2022). It is therefore imperative to incorporate the artificial knowledge management concept within the study of resilience in SCM (Leoni et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Digitalization and artificial knowledge for accountability in SCM: a systematic literature review

Vaio

Latif

Gunarathne

et al. 2023

JEIM

Self Cite

View full text Add to dashboard Cite

PurposeIn this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.Design/methodology/approachUsing the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.FindingsThe results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.Research limitations/implicationsThe study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.Practical implicationsThis research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.Originality/valueThis study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.

show abstract

“…, 2021). Unexpected crises like the pandemic give managers the chance to analyze their SCM and identify the causes of its disruptions (Fosso Wamba et al. , 2022).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digitalization and artificial knowledge for accountability in SCM: a systematic literature review

Vaio

Latif

Gunarathne

et al. 2023

JEIM

Self Cite

View full text Add to dashboard Cite

show abstract

“…The resilience theory sheds light on how manufacturing organisations in SA can effectively respond to and recover from the impact of the COVID-19 pandemic and other similar crises, enabling stakeholders to explore strategies, mechanisms, and processes that enhance resilience and build more robust systems and organisations. In analysing supply chain management (SCM) and understanding the causes of disruptions, managers can develop effective strategies to maintain operational continuity, as emphasised by [22]. According to [23], embracing the principles of resilience theory leads to the establishment of a resilient manufacturing sector, where companies can rapidly predict, adapt to, respond to, and recover from unforeseen disruptions.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Evaluating and Predicting the Long-Term Impact of the COVID-19 Pandemic on Manufacturing Sales within South Africa

Makoni

Chikobvu

2023

Sustainability

View full text Add to dashboard Cite

Manufacturing sales forecasting is crucial for business survival in the competitive and volatile modern market. The COVID-19 pandemic has had a significant negative impact on the demand and revenue of firms globally due to disruptions in supply chains. However, the effect of the pandemic on manufacturing sales in South Africa (SA) has not been quantified. The progress of the country’s manufacturing sector’s recovery after the pandemic remains unknown or unquantified. This paper uses a Box–Jenkins approach to time series analysis to produce long-term forecasts/projections of potential manufacturing sales, thereby quantifying the effects of the pandemic shock when the projections are compared with actual manufacturing sales. The Box–Jenkins approach is chosen because of its credibility and ability to produce accurate forecasts. Long-term projections enable organisations to plan ahead and make informed decisions, develop successful recovery plans, and navigate through similar economic shocks in the future, thereby ensuring long-term business survival and sustainability of the manufacturing sector. The SARIMA (0,1,1)(0,1,1)12 model best fits the SA manufacturing sales data according to the Akaike information criterion (AIC) and Bayesian information criterion (BIC), as well as the root mean square error (RMSE) and the mean absolute error (MAE). The results indicate that SA’s manufacturing sector was negatively impacted by the COVID-19 pandemic from about April 2020, but by November 2020 manufacturing sales had recovered to levels similar to projected levels had the COVID-19 pandemic not occurred. Long-term forecasts indicate that SA manufacturing sales will continue to increase. The manufacturing sector continues to grow, leading to increased employment opportunities and a boost to the gross domestic product (GDP).

show abstract

Guest editorial: Bridging the research-practice gaps in supply chain management: lessons from COVID-19

Cited by 2 publications

References 63 publications

Digitalization and artificial knowledge for accountability in SCM: a systematic literature review

Digitalization and artificial knowledge for accountability in SCM: a systematic literature review

Evaluating and Predicting the Long-Term Impact of the COVID-19 Pandemic on Manufacturing Sales within South Africa

Contact Info

Product

Resources

About