Building and evaluating ESET: A tool for assessing the support given by an enterprise system to supply chain management

Peiris, K. Dharini Amitha; Jung, Jin; Gallupe, R. Brent

doi:10.1016/j.dss.2015.05.004

Cited by 15 publications

(2 citation statements)

References 69 publications

(177 reference statements)

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“…It does not, however, coordinate di↵erent views of this network for helping decision makers understand its topological nature and provide additional insight into the problem context. Similar real-world DSS applications for SCM [51] have been developed across various industries including pharmaceuticals [55] and energy [39].…”

Section: Decision Support Systems For Supply Chain Managementmentioning

confidence: 99%

Visual analytics for supply network management: System design and evaluation

Park

Bellamy

Basole

2016

Decision Support Systems

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Section: Decision Support Systems For Supply Chain Managementmentioning

confidence: 99%

Visual analytics for supply network management: System design and evaluation

Park

Bellamy

Basole

2016

Decision Support Systems

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“…Some studies (e.g., Peiris et al, 2015;Raut et al, 2019;Sarkar et al, 2022;Shukla & Tiwari, 2017;Wu & Huang, 2018) factor in sustainability in supply chain management. Peiris et al (2015) develop a decision-making tool to investigate the reliability of enterprise information systems in organizations. Verification of sustainability in manufacturing is also an important concern for organizations.…”

Section: Category IImentioning

confidence: 99%

A Journey of Business Analytics in Improving Supply Chain Performance: A Systematic Review of Literature

Chatterjee,

Chatterjee

2023

Management and Labour Studies

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The influence of business analytics on supply chain performance has become notable over the last decade. The purpose of this study is to identify and analyze the effect of endogenous variables of business analytics, if any, on supply chain performance. We have conducted a systematic literature review (SLR) in four sequential stages, that is, collecting existing literature from relevant databases, finding endogenous variable(s) in this context, classifying papers based on how the endogenous variable(s) under consideration affect the impact of business analytics on supply chain performance and, finally, segmenting the literature depending on different types of supply chain performance. The study considers research papers published during the period 2008–2023. Based on the analysis of the literature, it is found that ‘market competition’ is an important endogenous variable in this context. This variable can impact the relationship between business analytics and supply chain performance in different ways. It can serve as a moderating variable, mediating variable or main variable. In this study, we have shown how directional relationships among business analytics, market competition and supply chain performance have changed over time. The future scope of the proposed study could be to identify other endogenous variables at the time of verifying the influence of business analytics on supply chain performance.

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Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions

2020

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In this digital era, data is new oil and artificial intelligence (AI) is new electricity, which is needed in different elements of operations management (OM) such as manufacturing, product development, services and supply chain. This study explores the feasibility of AI utilization within an organization on six factors such as job-fit, complexity, long-term consequences, affect towards use, social factors and facilitating conditions for different elements of OM by mining the collective intelligence of experts on Twitter and through academic literature. The study provides guidelines for managers for AI applications in different components of OM and concludes by presenting the limitations of the study along with future research directions.

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Building and evaluating ESET: A tool for assessing the support given by an enterprise system to supply chain management

Cited by 15 publications

References 69 publications

Visual analytics for supply network management: System design and evaluation

Visual analytics for supply network management: System design and evaluation

A Journey of Business Analytics in Improving Supply Chain Performance: A Systematic Review of Literature

Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions

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