Kasturi Muniapan, Rosmaini Ahmad, Muhammad Shahar Jusoh, Shaliza Azreen Mustafa and Tan Chan Sin
This paper proposes an assessment method for lean and sustainability (LS) practices for shop-floor workers, designed to evaluate their current practice culture.
Abstract
Purpose
This paper proposes an assessment method for lean and sustainability (LS) practices for shop-floor workers, designed to evaluate their current practice culture.
Design/methodology/approach
The method is developed in five phases: setting predefined indicators, constructing the assessment mechanism, implementing the assessment procedure, analyzing data and delivering results with recommendations. Validation is performed using two worker groups – line supervisors and operators – within the light-emitting diode (LED) manufacturing industry.
Findings
The results showed that workers’ familiarity and understanding of LS practices do not always correspond to their awareness levels. Key recommendations include prioritizing training for critical cases and adapting training approaches to fit the specific knowledge profiles identified.
Research limitations/implications
Firstly, the company should integrate the proposed assessment into an online platform that can automatically generate individual statistical results and priority levels. This reduces the burden of manual work and makes large-scale assessments more practical. Secondly, the study should expand to other shop-floor workers, such as technicians and engineers, to assess their knowledge profiles for future LS development initiatives.
Practical implications
The recommendations provide managers and training departments with guidelines to revise current training approaches. The methodology is validated, enabling the identification and mapping of each worker’s knowledge profile.
Originality/value
This study presents an original assessment method for evaluating the knowledge profiles of shop-floor workers regarding LS practices. To the best of the authors’ knowledge, no prior literature has reported on an assessment method targeting this specific group. The proposed approach supports the decision-making process for better LS practices in the company.
Details
Keywords
Adel Ali Ahmed Qaid, Rosmaini Ahmad, Shaliza Azreen Mustafa and Badiea Abdullah Mohammed
This study presents a systematic framework for maintenance strategy development of manufacturing process machinery. The framework is developed based on the reliability-centred…
Abstract
Purpose
This study presents a systematic framework for maintenance strategy development of manufacturing process machinery. The framework is developed based on the reliability-centred maintenance (RCM) approach to minimise the high downtime of a production line, thus increasing its reliability and availability. A case study of a production line from the ghee and soap manufacturing industry in Taiz, Yemen, is presented for framework validation purposes. The framework provides a systematic process to identify the critical system(s) and guide further investigation for functional significant items (FSIs) based on quantitative and qualitative analyses before recommending appropriate maintenance strategies and specific tasks.
Design/methodology/approach
The proposed framework integrates conventional RCM procedure with the fuzzy computational process to improve FSIs criticality estimation, which is the main part of failure mode effect criticality analysis (FMECA) applications. The framework consists of four main implementation stages: identification of the critical system(s), technical analysis, Fuzzy-FMECA application for FSIs criticality estimation and maintenance strategy selection. Each stage has its objective(s) and related scientific techniques that are applied to systematically guide the framework implementation.
Findings
The proposed framework validation is summarised as follows. The first stage results demonstrate that the seaming system (top and bottom systems) caused 50% of the total production line downtime, indicating it is a critical system that requires further analysis. The outcomes of the second stage provide significant technical information on the subject (seaming system), helping team members to identify and understand the structure and functional complexities of the seaming system. This stage also provides a better understanding of how the seaming system functions and how it can fail. In stage 3, the application of FMECA with the fuzzy computation integration process presents a systematic way to analyse the failure mode, effect and cause of items (components of the seaming system). This stage also includes items’ criticality estimation and ranking assessment. Finally, stage four guides team members in recommending the appropriate countermeasures (maintenance strategies and task selection) based on their priority level.
Originality/value
This paper proposes an original maintenance strategies development framework based on the RCM approach for production system equipment. Specifically, it considers a fuzzy computational process based on the Gaussian function in the third stage of the proposed framework. Adopting the fuzzy computational process improves the risk priority number (RPN) estimation, resulting in better criticality ranking determination. Another significant contribution is introducing an extended item criticality ranking assessment process to provide maximum levels of criticality item ranking. Finally, the proposed RCM framework also provides detailed guidance on maintenance strategy selection based on criticality levels, unique functionality and failure characteristics of each FSI.