Development of a supply chain risk mitigation index for distillery

Raghuram, P.; Sandeep, P.; Sreedharan, V. Raja; Saikouk, Tarik

doi:10.1108/tqm-01-2020-0008

Cited by 13 publications

(30 citation statements)

References 74 publications

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“…The results as shown in Tables 3 and 4 illustrate that there is a direct impact of SC AC on SCA, while there is no effect from SC risk mitigation. The finding of SC AC on SCA is consistent with some previous studies (Golgeci and Kuivalainen, 2019; Alkalha et al , 2019; Xie et al , 2018); conversely, the finding of SC risk mitigation on SCA was inconsistent with a study conducted by Raghuram et al (2020).…”

Section: Discussion Of Results and Theoretical Contributionsupporting

confidence: 70%

Enabling supply chain efficacy through SC risk mitigation and absorptive capacity: an empirical investigation in manufacturing firms in the Middle East region – a moderated-mediated model

AL‐Shboul

Alsmairat

2023

SCM

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Purpose This study aims to contribute to the supply chain management (SCM) literature differently. It offers insightful information about the main enablers that affect supply chain efficacy (SCE). Therefore, this study examines the significant roles and the relationships between SC absorptive capacity, SC risk mitigation, supply chain agility (SCA) and supply chain integration (SCI) among manufacturing firms (MFs) in the Middle East region. Design/methodology/approach This paper performed a quantitative survey-based study to analyze the substantial roles of SC absorptive capacity, SC risk mitigation, SCA and SCI on SCE. Thus, the authors conducted an online survey through 260 MFs that are listed in the Chamber of the industries of Jordan, Egypt and Turkey that only responded by email. The main respondents were chief executive managers, operations managers, managers and logistics employees from both mid and top levels. The conceptual model was tested by using a hypothesis-testing deductive approach. The findings are based on covariance-based analysis and structural equation modeling (SEM) using partial least squares (PLS)-SEM software. Findings The PLS-SEM clearly shows a significant relationship between SC absorptive capacity, SCA and SCI on SCE, while surprisingly still, SC risk mitigation does not significantly affect SCE. Further, the outcomes of this study indicate that there is a significant effect of SCA as a mediating factor in the relationship between SC absorptive capacity and SCE directly and indirectly, as well as a moderating effect of SCI in the relation, whereas there is a nonsignificant effect by SC risk mitigation. Finally, there is a significant effect of SCI in the relationship between SC absorptive capacity and SC risk mitigation on SCE as a moderating factor. Originality/value This study introduces a theoretical insight and empirically presents that both SCA and SCI are proper logistics characteristics for mediating and moderating the impact of SC absorptive capacity on SCE. Such findings of this study can provide insightful implications for managers at different levels in MFs, stakeholders and policymakers regarding the importance of using the three mentioned enablers on SCE in MFs, in the Middle East firms, in particular, and in developing countries, in general.

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Section: Discussion Of Results and Theoretical Contributionsupporting

confidence: 70%

Enabling supply chain efficacy through SC risk mitigation and absorptive capacity: an empirical investigation in manufacturing firms in the Middle East region – a moderated-mediated model

AL‐Shboul

Alsmairat

2023

SCM

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“…As a result, these Process areas are relevant to and valuable in the manufacturing industry. For instance, in the case studies by Raghuram et al (2020) and Thomas et al (2021), the CMMI approaches were employed in the process appraisal of manufacturing industries. Among the different process areas of CMMI, OPF is selected for the investigation considering the process enhancement for the growth of digitalisation in the manufacturing sector.…”

Section: Methodsmentioning

confidence: 99%

“…CMMI is a successful tool kit used by software industries to guide process appraisal most effectively, and it is considered one of the influential, long-standing and often-studied standards to schedule performance index (Staple and Niazi, 2008). CMMI has been profoundly generalised over the years, and various investigations have tested the suitability of CMMI in the manufacturing sector, which include the studies conducted by Thomas et al (2021), Raghuram et al (2020), De Carolis et al (2017, Kundu and Manohar (2012) and Li and Lin (2011). Though ample research and development are progressing in assessing the CMMI level of software organisations, none of the studies has extended it to manufacturing industries to assess the digital manufacturing maturity level.…”

Section: Introductionmentioning

confidence: 99%

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CMMI based fuzzy logic approach to assess the digital manufacturing maturity level of manufacturing industries

Thomas

Saleeshya

2023

TQM

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PurposeThis research study aims to introduce a maturity model based on capability maturity model integration (CMMI) that can assess the digital manufacturing maturity level of manufacturing companies.Design/methodology/approachA CMMI model for the manufacturing industry is designed to assess the digitalisation level of manufacturing industries. The model is developed exclusively for the process area “organisational process focus” (OPF), and the digitalisation level is quantified using fuzzy logic by employing a case study approach.FindingsThe CMMI is successfully employed to assess the digitalisation level of a manufacturing organisation using the fuzzy logic approach. The triangular fuzzy number of the Fuzzy CMMI Measure Index (FCMI) is obtained as (6.08, 7.33, 8.52). The transformation of FCMI into linguistic terms discloses the digitalisation level of the manufacturing organisation as “Capability Maturity Level 4” (CML 4).Originality/valueThe authors tested the suitability of CMMI in the manufacturing sector. The operational concept introduced in this research sets forth a unique framework to quantify the digitalisation level of manufacturing industries.

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“…These process areas are applicable and can appropriately be interpreted in the manufacturing sector. For instance, in the case study conducted by Raghuram et al (2020), CMMI’s continuous representation model is used in the supply chain risk management of an alcohol and beverage distilling company. Moreover, CMMI is also used as a complementary model to existing methods such as agile manufacturing (Alegria and Bastarrica, 2006), lean manufacturing (Kundu and Manohar, 2012) and six-sigma (Siviy et al , 2007) to fasten the process of development or to optimize the process.…”

Section: Introductionmentioning

confidence: 99%

Assessment of CMMI level of manufacturing industry using fuzzy logic approach: a case study

Thomas

Saleeshya

Suresh

2021

JM2

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Purpose The purpose of this study is to develop a CMMI model for the manufacturing industry and to find an appropriate method to assess the CMMI level. The CMMI level indicates how well the processes are planned, performed, measured and controlled, thus revealing the performance level of an organization. Design/methodology/approach Among the various process areas of CMMI, “organizational process focus” (OPF) is selected for the study. The CMMI model for the process area OPF is designed based on the CMMI enablers, criteria and attributes. Based on this multilevel model, a case study approach is adopted and fuzzy logic is used to measure the CMMI level of an organization. The fuzzy performance importance index (FPII) and the ranking score are used to further analyze the attributes. Findings The proposed model has been successfully used to measure the CMMI level of the manufacturing industry in south India. The triangular fuzzy number of the fuzzy CMMI measure index (FCMI) is obtained as (2.077, 3.534, 5.000). The transformation of FCMI back into linguistic terms discloses the current CMMI level of the industry as “Capability Maturity Level 2” (CML 2). Originality/value The authors tested the suitability of an inter-disciplinary approach known as CMMI for the process appraisal in the manufacturing sector. The investigation sets forth a unique framework to quantify the performance of practices followed in a manufacturing organization and thereby help the industry to realize the present strength and weakness in terms of process assets.

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Development of a supply chain risk mitigation index for distillery

Cited by 13 publications

References 74 publications

Enabling supply chain efficacy through SC risk mitigation and absorptive capacity: an empirical investigation in manufacturing firms in the Middle East region – a moderated-mediated model

Enabling supply chain efficacy through SC risk mitigation and absorptive capacity: an empirical investigation in manufacturing firms in the Middle East region – a moderated-mediated model

CMMI based fuzzy logic approach to assess the digital manufacturing maturity level of manufacturing industries

Assessment of CMMI level of manufacturing industry using fuzzy logic approach: a case study

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