Ashraf Labib

Prof. Ashraf Labib

orcid

Research Focus & Metrics

Academic Career Length

29Years (1998-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 May 2011 in Expert Systems with Applications
Verified
Journal Article
Closed access
Alessio IshizakaAlessio Ishizaka
Alessio Ishizaka
Ashraf LabibAshraf Labib
Ashraf Labib

26 October 2009 in OR Insight
Verified
Journal Article
Closed access
Alessio IshizakaAlessio Ishizaka
Alessio Ishizaka
Ashraf LabibAshraf Labib
Ashraf Labib
OR Insight publishes accessible articles on the practical application of operational research. It aims to be an interesting and stimulating publication that appeals not only to OR practitioners and consultants, but also to managers and non-specialists. OR Insight is an official journal of the OR Society

13 April 2020 in The Bottom Line
Verified
Journal Article
Closed access
MSMerlin Stone
Merlin Stone
EAEleni Aravopoulou
Eleni Aravopoulou
YEYuksel Ekinci
Yuksel Ekinci
GEGeraint Evans
Geraint Evans
MHMatt Hobbs
Matt Hobbs
LMLiz Machtynger
Liz Machtynger
Purpose: The purpose of this paper is to review literature about the applications of artificial intelligence (AI) in strategic situations and identify the research that is needed in the area of applying AI to strategic marketing decisions.Design/methodology/approach: The approach was to carry out a literature review and to consult with marketing experts who were invited to contribute to the paper.Findings: There is little research into applying AI to strategic marketing decision-making. This research is needed, as the frontier of AI application to decision-making is moving in many management areas from operational to strategic. Given the competitive nature of such decisions and the insights from applying AI to defence and similar areas, it is time to focus on applying AI to strategic marketing decisions.Research limitations/implications: The application of AI to strategic marketing decision-making is known to be taking place, but as it is commercially sensitive, data is not available to the authors.Practical implications: There are strong implications for all businesses, particularly large businesses in competitive industries, where failure to deploy AI in the face of competition from firms, who have deployed AI to improve their decision-making could be dangerous.Social implications: The public sector is a very important marketing decision maker. Although in most cases it does not operate competitively, it must make decisions about making different services available to different citizens and identify the risks of not providing services to certain citizens; so, this paper is relevant to the public sector.Originality/value: To the best of the authors’ knowledge, this is one of the first papers to probe deployment of AI in strategic marketing decision-making.

1 June 2011 in Expert Systems with Applications
Verified
Journal Article
Closed access
Alessio IshizakaAlessio Ishizaka
Alessio Ishizaka
Ashraf LabibAshraf Labib
Ashraf Labib

1 April 2018 in European Journal of Operational Research
Verified
Journal Article
Closed access
QHQiwei Hu
Qiwei Hu
JBJohn E. Boylan
John E. Boylan
HCHuijing Chen
Huijing Chen
Ashraf LabibAshraf Labib
Ashraf Labib

1 September 2004 in Journal of Quality in Maintenance Engineering
Verified
Journal Article
Closed access
PDF file
Ashraf LabibAshraf Labib
Ashraf Labib
In this paper, an investigation of the characteristics of computerised maintenance management systems (CMMSs) is carried out to highlight the need for them in industry and identify their current deficiencies. A proposed model provides a decision analysis capability that is often missing in existing CMMSs. The proposed model employs a hybrid of intelligent approaches. This hybrid system is analogous to the Holonic concept. The distinction between these two features is important. The rules function automatically. Practical implications. The main practical implication of this paper is the proposal of an intelligent model that can be linked to CMMSs to add value to data collected in the form of provision of decision support capabilities. A further implication is to identify the need for information to aid maintenance, followed by the provision of reasons for current deficiencies in existing off‐the‐shelf CMMSs.