Benchmarking of internal supply chain management: factors analysis and ranking using ISM approach and MICMAC analysis

2019 ◽  
Vol 27 (4) ◽  
pp. 394
Author(s):  
N.A. Kailash ◽  
Rajeev Kumar Saha ◽  
Sanjeev Goyal
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Chain Management ◽  
Micmac Analysis ◽  
Factors Analysis ◽  
Management Factors

Strategic supply chain management factors influencing agribusiness innovation utilization

2014 ◽  
Vol 25 (3) ◽  
pp. 487-521 ◽  
Author(s):  
Maree Storer ◽  
Paul Hyland ◽  
Mario Ferrer ◽  
Ricardo Santa ◽  
Andrew Griffiths
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Chain Management ◽  
Factors Influencing ◽  
Management Factors

Modelling the SCM implementation barriers

2015 ◽  
Vol 10 (2) ◽  
pp. 158-178 ◽  
Author(s):  
S. J. Gorane ◽  
Ravi Kant
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Model Development ◽  
Integrated Approach ◽  
Business Managers ◽  
Implementation Barriers ◽  
Chain Management ◽  
Content Type ◽  
Micmac Analysis ◽  
Fuzzy Micmac

Purpose – The purpose of this paper is to develop the relationships among the identified supply chain management barriers (SCMBs) and understand mutual influences of these SCMBs on supply chain implementation. Further, this paper seeks to identify driving and dependent SCMBs using an interpretive structural modelling (ISM) and fuzzy MICMAC (Matrix of Cross-Impact Multiplications Applied to Classification) analysis. Design/methodology/approach – The methodology used in the paper is the ISM with a view to evolving mutual relationships among SCMBs. The identified SCMBs have been classified further, based on their driving and dependence power using fuzzy MICMAC analysis. Findings – This paper has identified 15 key SCMBs which hinder the successful supply chain management (SCM) implementation in an organization and has developed the relationships among the SCMBs using the ISM methodology. Further, this paper analyses the driving and dependent SCMBs using fuzzy MICMAC analysis. The integrated approach is developed here, as the ISM model provides only binary relationship among SCMBs. The fuzzy MICMAC analysis is adopted here, as it is useful in specific analysis related to driving and the dependence power of SCMBs. Research limitations/implications – The weightage for the ISM model development and fuzzy MICMAC is obtained through the judgement of academics and industry experts. Further, validation of the model is necessary through questionnaire survey. Practical implications – The identification of SCMBs, ISM model development and fuzzy MICMAC analysis provide academics and managers a macro picture of the challenges posed by the SCM implementation in an organization. Originality/value – The results will be useful for business managers to understand the SCMBs and overcome these SCMBs during the SCM implementation in an organization.

Modeling the enablers of green supply chain management

2017 ◽  
Vol 24 (2) ◽  
pp. 536-568 ◽  
Author(s):  
Rakesh Kumar Malviya ◽  
Ravi Kant
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Action Plan ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Interpretive Structural Modeling ◽  
Chain Management ◽  
Content Type ◽  
Micmac Analysis ◽  
Fuzzy Micmac

Purpose The purpose of this paper is to identify and develop the relationships among the green supply chain management enablers (GSCMEs), to understand mutual influences of these GSCMEs on green supply chain management (GSCM) implementation, and to find out the driving and the dependence power of GSCMEs. Design/methodology/approach This paper has identified 35 GSCMEs on the basis of literature review and the opinions of experts from academia and industry. A nationwide questionnaire-based survey has been conducted to rank these identified GSCMEs. The outcomes of the survey and interpretive structural modeling (ISM) methodology have been applied to evolve mutual relationships among GSCMEs, which helps to reveal the direct and indirect effects of each GSCMEs. The results of the ISM are used as an input to the fuzzy Matriced’ Impacts Croisés Multiplication Appliquéeá un Classement (MICMAC) analysis, to identify the driving and the dependence power of GSCMEs. Findings Out of 35 GSCMEs 29 GSCMEs (mean⩾3.00) have been considered for analysis through a nationwide questionnaire-based survey on Indian automobile organizations. The integrated approach is developed, since the ISM model provides only binary relationship among GSCMEs, while fuzzy MICMAC analysis provides precise analysis related to driving and the dependence power of GSCMEs. Research limitations/implications The weightage for ISM model development and fuzzy MICMAC are obtained through the judgment of few industry experts. It is the only subjective judgment and any biasing by the person who is judging might influence the final result. Practical implications The study provides important guidelines for both practitioners, as well as the academicians. The practitioners need to focus on these GSCMEs more carefully during GSCM implementation. GSCM managers may strategically plan its long-term growth to meet GSCM action plan. While the academicians may be encouraged to categorize different issues, which are significant in addressing these GSCMEs. Originality/value Arrangement of GSCMEs in a hierarchy, the categorization into the driver and dependent categories, and fuzzy MICMAC are an exclusive effort in the area of GSCM implementation.

Medikamentenlogistik: Kooperation: Swisslog Healthcare und SPZ

2017 ◽  
Vol 22 (04) ◽  
pp. 78-78
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Chain Management

Swisslog, ein führender Anbieter von Lösungen für Medikamenten- und Supply-Chain-Management im Gesundheitswesen, hat vom angesehenen Schweizer Paraplegiker-Zentrum in Nottwil (SPZ) den Großauftrag für die Lieferung und Installation seiner modernsten Technologie zur stationären und ambulanten Medikamentenversorgung erhalten.

Sign in / Sign up

Export Citation Format

Share Document