Enablers for digital supply chain transformation in the service industry

Arwa Mohamed Alzarooni, Sharfuddin Ahmed Khan, Angappa Gunasekaran, Muhammad Shujaat Mubarik

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Businesses across the globe are adopting digital supply chain (DSC) management parameters to attain supply chain resilience, flexibility and efficiency together. Interestingly, in some cases, businesses claim that the adoption of DSC has proven to be counter-productive. We argue that such firms faced issues because of their poor/lack of readiness to adopt DSC. The readiness for DSC includes identification of DSC enablers and development of a strategy to capitalize on them. Against this backdrop, the objective of the present study is to identify the enablers of DSC from the literature, validate them with help of experts and explore the contextual relationship between them. The list of enablers highlighted in this study can be used in the future, as a guideline to evaluating the service organisations’ readiness to adopt the DSC. The study adopts a threefold approach. In the first step, DSC enablers are identified from the literature. In the second step, the Interpretive structural model is developed using the expert opinion of 17 professionals from different service sector organisations in the United Arab Emirates, selected using the defined criterion. In the third step, decision-making trial and evaluation laboratory is employed to prioritise and find interrelationships among identified enablers. The results revealed that “smart warehousing” is the most influential enabler with high driving power and weak dependence power. Similarly, “Intelligence” and “Real-time” are operative enablers in the transformation process of DSC and have strong driving power and dependence power. The findings of this study can help organisations and decision-makers to focus on specific DSC transformation enablers, to transform their traditional supply chain to DSC.

Original languageEnglish (US)
JournalAnnals of Operations Research
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • Management Science and Operations Research


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