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Output from the Irish Dairy Industry has grown rapidly since the abolition of quotas in 2015, with processors investing heavily in capacity expansion to deal with the extra milk volumes. Further capacity gains may be achieved by extending the processing season into the winter, a key enabler for which being the reduction of duration of the winter maintenance overhaul period. This paper aims to investigate if Lean Six Sigma tools and techniques can be used to enhance operational maintenance performance, thereby releasing additional processing capacity.

Combining the Six-Sigma Define, Measure, Analyse, Improve, Control (DMAIC) methodology and the structured approach of Turnaround Maintenance (TAM) widely used in process industries creates a novel hybrid model that promises substantial improvement in maintenance overhaul execution. This paper presents a case study applying the DMAIC/TAM model to Ireland’s largest dairy processing site to optimise the annual maintenance shutdown. The objective was to deliver a 30% reduction in the duration of the overhaul, enabling an extension of the processing season.

Application of the DMAIC/TAM hybrid resulted in process enhancements, employee engagement and a clear roadmap for the operations team. Project goals were delivered, and original objectives exceeded, resulting in €8.9m additional value to the business and a reduction of 36% in the duration of the overhaul.

The results demonstrate that the model provides a structure that promotes systematic working and a continuous improvement focus that can have substantial benefits for wider industry. Opportunities for further model refinement were identified and will enhance performance in subsequent overhauls.

To the best of the authors’ knowledge, this is the first time that the structure and tools of DMAIC and TAM have been combined into a hybrid methodology and applied in an Irish industrial setting.

The purpose of this study was to improve the overall equipment effectiveness (OEE) of the production process of the shredder operation of ABC company, an industrial waste management company which supplies pre-processed industrial waste as alternative fuel to a cement plant.

This case study investigated all possible availability and performance losses that caused the shredder system’s OEE and various problem-solving techniques, such as root cause analysis and Pareto analysis, were used to find the root cause of the reduced OEE.

After analysing this case study, three significant loss factors were identified from all the availability and performance losses, which caused the shredder system’s OEE losses. Practical solutions were found for the effect of those loss factors to improve the machine’s OEE and productivity.

This case study has been concentrated on only analysing of losses and improvement of OEE in the production process and not about cost analysis between loss and improvements.

This paper shows how to improve the OEE of a production process through various problem-solving techniques by identifying its losses and how to achieve the best solutions for those losses in a practical manner.

This study investigates the integration of Industry 4.0 (I4.0) technologies with condition-based maintenance (CBM) in upstream oil and gas (O&G) operations, focussing on developing countries like Nigeria. The research identifies barriers to this integration and suggests solutions, intending to provide practical insights for improving operational efficiency in the O&G sector.

The study commenced with an exhaustive review of extant literature to identify existing barriers to I4.0 implementation and contextualise the study. Subsequent to this foundational step, primary data are gathered through the administration of carefully constructed questionnaires targeted at professionals specialised in maintenance within the upstream O&G sector. A semi-structured interview was also conducted to elicit more nuanced, contextual insights from these professionals. Analytically, the collected data were subjected to descriptive statistical methods for summarisation and interpretation with a measurement model to define the relationships between observed variables and latent construct. Moreover, the Relative Importance Index was utilised to systematically prioritise and rank the key barriers to I4.0 integration to CBM within the upstream O&G upstream sector.

The most ranked obstacles in integrating I4.0 technologies to the CBM strategy in the O&G industry are lack of budget and finance, limited engineering and technological resources, lack of support from executives and leaders of the organisations and lack of competence. Even though the journey of digitalisation has commenced in the O&G industry, there are limited studies in this area.

The study serves as both an academic cornerstone and a practical guide for the operational integration of I4.0 technologies within Nigeria's O&G upstream sector. Specifically, it provides an exhaustive analysis of the obstacles impeding effective incorporation into CBM practices. Additionally, the study contributes actionable insights for industry stakeholders to enhance overall performance and achieve key performance indices (KPIs).

India liberalized its economy in 1991, which resulted in intense global competition, quality-conscious and demanding customers. Additionally, significant technological advancements lead to enhancements in products and processes. These forced Indian organizations to adopt innovative business strategies in the past 30 years. Meanwhile, the Lean Six Sigma methodology has significantly grown with vast applicability during the past 30 years. Thus, the purpose of this study is to develop the learning on Lean Six Sigma methodology in the Indian context through investigation of literature.

A three-stage systematic literature review approach was adopted to investigate the literature during the present study. In total, 187 articles published in 62 journals/conference proceedings from 2005 to 2022 (18 years) were shortlisted. The first part of the article summarizes the significant milestones towards the quality journey in the Indian context, along with the evolution of the Lean Six Sigma methodology. The second part examines the shortlisted papers on Lean Six Sigma frameworks, their applicability in industrial sectors, performance metrics, outcomes realized, publication trends, authorship patterns and leading researchers from the Indian perspective.

Lean Six Sigma has emerged as a highly acclaimed and structured business improvement strategy worldwide. The Indian economy has seen remarkable growth in the past decade and is one of the fastest-growing economies in the 21st century. Lean Six Sigma implementation in India has significantly increased from 2014 onward. The study revealed that researchers have proposed several different frameworks for Lean Six Sigma implementation, the majority of which are conceptual. Furthermore, the balanced applicability of Lean Six Sigma in manufacturing and service sectors was observed with the highest implementation in the health-care sector. Additionally, the widely adopted tools, techniques along with performance metrics exploring case studies were reported along with a summary of eminent and leading researchers in the Indian context.

This study is confined to reviewed papers as per the research criteria with a significant focus on the Indian context and might have missed some papers due to the adopted papers selection strategy.

The present study is one of the initial attempts to investigate the literature published on Lean Six Sigma in the Indian context, including perspective on the Indian quality movement. Therefore, the present study will provide an understanding of Lean Six Sigma methodology in the Indian context to graduating students in engineering and management and entry-level executives. The analysis and findings on Lean Six Sigma frameworks, research approach, publications details, etc., will be helpful to potential research scholars and academia. Additionally, analysis of case studies on Lean Six Sigma implementation by Indian industries will assist the managers and professionals in decision making.

The purpose of this study is to explore the relationship between Lean manufacturing and Industry 4.0 for small and medium size of enterprise in Japan and Brazil.

The authors conducted a quantitative survey (20 companies in Japan and 30 companies in Brazil) combined with a qualitative interview (2 companies in Japan and 15 companies in Brazil).

According to the quantitative study, 90% of them practice Lean manufacturing and 40% of them practice Industry 4.0. In the qualitative study in Brazil, four managers responded that the Lean manufacturing is a prerequisite for Industry 4.0 since any production process with waste cannot be productive, even with sophisticated digitalization technology.

The authors explored further the relationship between “defensive Digital Transformation (DX),” which is based mainly on Lean manufacturing, and “offensive DX,” which relates to customer value creation through Industry 4.0. This study clarifies the relationship and plays as a roadmap to develop better the manufacturing from current status to the vision of Industry 4.0.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

This paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.

The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.

The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.

This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.

Lean management tools are becoming increasingly applied in different types of organizations around the world. These tools have shown their significant contribution to improving business performance. In this vein, the purpose of this paper is to examine the influence of lean management on both occupational safety and operational excellence in Tunisian companies.

A survey was conducted among Tunisian companies, and it resulted in the collection of 62 responses that were analyzed using the software SPSS. In addition, a conceptual model linking the practices of the three basic concepts was designed to highlight the hypotheses of the research. Subsequently, factor analysis and structural equation method analysis were conducted to assess the validation of the assumptions.

The results obtained have shown that lean management has a significant impact on occupational safety. Similarly, occupational safety has a significant impact on operational excellence. However, lean management does not have a significant impact on operational excellence.

This work highlighted the involvement of small and medium-sized enterprise’s managers from emerging economies in the studied concepts’ practices. Likewise, it testified to the impacts of lean management on occupational safety and operational excellence in the Tunisian context.