Factors influencing sustainable maintenance in manufacturing industries

Abstract

PurposeThis paper intends to discover, analyze and construct a model that may be used to measure the interactions between major factors which are identified by expert opinion and literature review for sustainable maintenance specific to manufacturing industries using the total interpretive structural modeling (TISM) approach.Design/methodology/approachIn total, 12 factors were acknowledged from the literature review and the opinions of experts from manufacturing industries. Scheduled interviews with the employees were conducted by using the questionnaire which is developed from the identified 12 factors in order to find the interrelationships among these factors. The TISM approach is used for analyzing factors' interrelationships. The Matrice d'Impacts Croises Multiplication Appliques a un Classement (MICMAC) approach is used to identify the key factors which influence sustainable maintenance.FindingsThis paper found 12 factors that have ascendancy over the sustainable maintenance practices in the industry by reviewing the literature and consulting industry experts to realize the linkage between the factors. The results found that availability rate, adopting government policies, training and education are key factors that influence sustainable maintenance.Practical implicationsThe proposed model would be valuable for experts to understand the factors influencing sustainable maintenance in the industry. This model can be used by an organization's maintenance managers to implement sustainable maintenance practices in their plants.Originality/valueThis study analyzes the interrelationship between factors influencing sustainable maintenance in manufacturing industries, which is a new effort in this domain of practice.

How sustainability factors influence maintenance of water distribution systems feeding manufacturing industries

“…Liu et al (2022) consider the necessity of including the evaluation of environmental effectiveness within the whole evaluation of maintenance system effectiveness, mostly based on economic criteria. Suresh and Dharunanand (2021) propose a model capable to measure the interactions among relevant sustainability factors identified in literature, so as to offer sustainable maintenance specifics to manufacturing industries. The authors focus on twelve major factors and propose an approach based on the integration between the Total Interpretive Structural Modeling (TISM) and the Matrice d'Impacts Croises Multiplication Appliques a un Classement (MIC-MAC), identifying a reduced set of key factors.…”

“…The reduced set of key sustainability factors influencing maintenance identified by Suresh and Dharunanand (2021) will constitute part of our input data. As highlighted by the authors, these factors are highly interdependent with each other.…”

“…The general goal of our decision-making problem consists in prioritising maintenance interventions for the water network which feeds the industrial plants and facilities of a manufacturing company operating in the alimentary sector and located in the South of Italy (Carpitella et al, 2018). As evaluation criteria we assume the main relevant sustainability factors influencing maintenance management in manufacturing contexts, as resulting from the study led by Suresh and Dharunanand (2021). The same authors highlight as water is among the most important resources in the manufacturing sector, since it contributes to the quality of final products, and this consideration strengthens the purpose of our application.…”

“…Table 1 reports those sustainability factors considered as more influential, and whose interdependence may likely impact long-term maintenance in manufacturing sectors. These factors occupy the first positions of the MICMAC ranking, as it can be checked in (Suresh and Dharunanand, 2021).…”

Joint maintenance planning and production scheduling optimization model for green environment

“…Energy consumption increases as a machine deteriorates (Yan and Hua, 2010). Unexpected failures and downtimes necessitate unnecessary setups, significantly increasing energy consumption each time the machine turns on (Heilala et al, 2008;Suresh and Dharunanand, 2021). Therefore, an optimal maintenance plan can save significant energy for green scheduling (Takata et al, 2004).…”