Analysis of flexible decision strategies for sustainability-focused green product recovery system

Sachin Mangla, Jitendra Madaan, Tung Sun Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

121 Citations (Scopus)


Identifying and analysing key decision variables that an organisation can opt for in order to initiate resource recovery from return activities and improve overall performance is a challenge. Therefore, to assist organisations which are involved in or about to initiate an effective recovery process, this paper provides guidelines to establish flexible decision strategies using the interpretive structural modelling (ISM) approach. This study has identified interrelationships between green variables such as supplier commitment, cost, regulations, etc. and has categorised relevant enablers. Further, variables such as capacity utilisation, customer satisfaction, energy consumption reduction, etc. are recognised as outcomes or results. After statistically verifying these variables, it has been inferred that driving enablers aid in promoting performance variables, while the result variables represent outcomes achieved via the driving variables. Further, using MICMAC analysis, a graphical categorisation of the variables is done on the basis of the impact on performance. Later prioritisation of variables having high driving power and low dependence is conducted. Inputs in the proposed model are taken through a case study conducted in the paper industry. The proposed model can be quite helpful and can act as a generic tool to achieve the desired performance improvement of green product recovery systems.
Original languageEnglish
Pages (from-to)3428-3442
Number of pages15
JournalInternational Journal of Production Research
Issue number11
Publication statusPublished - 1 Jun 2013


  • green product recovery system (GPRS)
  • interpretive structural modelling
  • recovery process
  • sustainability

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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