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Operational excellence (OpEx) is a proven philosophy focusing on continuous improvement in processes and systems for superior performance and efficiency. It plays a crucial role in the energy sector, acting as a catalyst for safety, customer satisfaction, sustainability and competitiveness. This research aims to assess OpEx methodologies in Oman’s energy sector, examining methods, approaches, motivations and sustainability.

This study applies qualitative analysis methodology, involving interviews with 18 industry experts, from the energy sector in a sizeable energy country.

The analysis revealed a growing demand, particularly, in the oil and gas industry, driven by emerging business needs. Qualitative data analysis has identified 10 themes such as implemented methodologies, motivation drivers, deployment approaches, sustainability factors, benefits and challenges. Additionally, new themes emerged, including influencers to start OpEx, resource requirements, enablers for successful OpEx and systems.

This research was limited to Oman and the findings drawn from Omani energy companies may have limited applicability to energy companies in other regions. Therefore, if these findings were to be used, the validation of the findings in relation to other countries should be conducted, to ensure the validity of the context and outcome.

These findings contribute to understanding OpEx dynamics in the Omani energy sector, offering valuable insights for effective utilisation and organisational goal achievement. Furthermore, the study offers valuable insights on how to effectively employ OpEx initiatives in the energy sector to achieve their goals and create value. It addresses the lack of knowledge, offers a framework for successful OpEx implementation, bridges the theory-practice gap and provides insights for optimal utilisation.

To the best of the authors’ knowledge, this is the first empirical study on assessing OpEx methodologies in the energy sector, and therefore it serves as a foundation for many future studies. The study provides a theoretical foundation for the OpEx methodologies in terms of organisational readiness for successful OpEx implementation.

The purpose of this global study is to investigate the critical failure factors (CFFs) in the deployment of operational excellence (OPEX) programs as well as the key performance indicators (KPIs) that can be used to measure OPEX failures. The study also empirically analyzes various OPEX methodologies adopted by various organizations at a global level.

This global study utilized an online survey to collect data. The questionnaire was sent to 800 senior managers, resulting in 249 useful responses.

The study results suggest that Six Sigma is the most widely utilized across the OPEX methodologies, followed by Lean Six Sigma and Lean. Agile manufacturing is the least utilized OPEX methodology. The top four CFFs were poor project selection and prioritization, poor leadership, a lack of proper communication and resistance to change issues.

This study extends the current body of knowledge on OPEX by first delineating the CFFs for OPEX and identifying the differing effects of these CFFs across various organizational settings. Senior managers and OPEX professionals can use the findings to take remedial actions and improve the sustainability of OPEX initiatives in their respective organizations.

This study uniquely identifies critical factors leading to OPEX initiative failures, providing practical insights for industry professionals and academia and fostering a deeper understanding of potential pitfalls. The research highlights a distinctive focus on social and environmental performance metrics, urging a paradigm shift for sustained OPEX success and differentiating itself in addressing broader sustainability concerns. By recognizing the interconnectedness of 12 CFFs, the study offers a pioneering foundation for future research and the development of a comprehensive management theory on OPEX failures.

The purpose of this paper is to examine the relation between the utilization of the customization strategy and the availability of the online toolkit and its features with the commercial variables of businesses.

The sample used in this paper consists of 134 cases of corporate brands in the automobile industry; their sales correspond to 49.12 percent of vehicles produced in 2012. The logistic regression analysis was then applied to the sample.

This paper confirmed the relationship between the use of toolkits for customization and business variables, like vehicle sales.

The generated model allows the prediction of market conditions which recommended to provide the toolkit for customization, and if implemented, what combination of features it must have.

This study seeks to provide a conceptual framework for the classification and selection of Healthcare 4.0 (H4.0) digital technologies affecting healthcare processes.

By examining the literature review, a set of processes of health services based on two axes of interaction and service customization and the axis of labor intensity of the service process matrix was divided into four categories: service factory, mass service, service shop and professional services. Then, using a combination of grey decision-making trial and evaluation laboratory (DEMATEL) and grey weighted aggregates sum product assessment (WASPAS) methods, a framework was presented to compute the impact of each of the H4.0 digital technologies on sub-criteria of the two main axes. Finally, based on the degree of the impact of each technology on the main axes, the technology affecting the four processes was segmented.

Findings show that the customer participation in the service process (C₁), ways to provide customer service (C₆) as well as the speed of service delivery (L₄) are the most important in the classification of digital technologies affecting healthcare processes.

Various other indicators from the behavioral, cultural, political, social and economic fields can be examined and used as a basis for evaluating H4.0 digital technologies.

The proposed framework can help managers select H4.0 digital technologies to prioritize, review and analyze appropriate technologies to improve and support different processes, prioritize appropriate technologies and review and analyze.

So far, no study has examined the link between digital technologies and various service processes. Therefore, this reinforces the originality and value of the present study.

Customer value is the key to successful innovation management. This task is considered complex and abstract. For this reason, several models have been proposed to that end, among them, Lean approaches. Lean's background has been applied to value identification, providing many benefits. This paper aims at analyzing the practices employed to identify customer value through Lean approaches.

Through a systematic literature review, the main Lean practices for customer value identification were categorized based on five main streams: (1) customer development, (2) customer value, (3) lean product, (4) lean start-up, and (5) lean innovation. These practices were organized into a framework that addresses value identification based on ideation, value prospection, requirements, construction and measurement, and value delivery.

In total, 33 practices were found to support capturing the value from the customer's perception. Additionally, a discussion is provided on the complementarity and differences between practices.

A framework is proposed to integrate the five streams and the corresponding practices for value identification. The results provide mechanisms that can benefit practitioners to better understand the value demanded by customers during innovation management. In fact, practitioners can use the proposed framework to identify value to customers in a holistic way. Academically, the results gather research on customer value and innovation management to contribute with a novel artifact for customer value identification in operations management using Lean approaches. Finally, a future research agenda on value identification is proposed.

The purpose of this study is to provide a robust model to bridge the influence of national culture (NC) on lean production (LP), identifying relationships among their elements and investigating the degree of influence.

The culturally sensitive lean production model (CSLPM) is used as a framework to develop a survey questionnaire that captures the information to analyze using partial least squares structural equation modeling (PLS-SEM). A case study to validate the model is presented from a subsidiary with operations in Mexico (MX) and the USA.

The CSLPM provides a robust framework for the measurement of the interaction between LP and NC. Multi-group analysis facilitated the comparison among the different groups (MX and USA), highlighting the differences of culture and the corresponding LP implementation within the same company.

The instrument was validated only within two countries and one organization. A higher sample of respondents, countries and organizations could validate the relationships established and the replication of the model.

The CSLPM can be used for assessment of the evolution of organizations, highlighting strengths and weaknesses of a particular culture when implementing LP, providing focal points for research and training efforts.

The CSLPM is a robust instrument to measure expectations for LP practices according to their company’s geographical context. The incorporation of higher order variables and mixed directionality within the variables presented a novel approach to PLS-SEM.

This paper aims at identifying the main contributions of Industry 4.0 (I4.0) technologies to the enhancement of knowledge management (KM).

A scoping review based on two stages was carried out. In the first stage, a numerical and descriptive analysis was conducted. In the second stage, the latent content of those papers was explored through a qualitative analysis, in which papers were assessed regarding the outcome of the implementation of ten I4.0 technologies on 14 KM abilities and 6 KM processes.

Six technologies (e.g. Internet of Things (IoT), big data, cloud computing, simulation, visualisation and industrial robot) were claimed to have a significant contribution on KM, especially for creation, organisation, dissemination and application processes. Overall, results indicated that technologies can significantly contribute to KM, although the extent of such contribution varies across technologies, learning levels, KM processes and abilities.

Existing studies in I4.0 fall short in exploring how embodied knowledge is articulated in practice, especially regarding opportunities to use new technological alternatives in favour of knowledge and learning development. This study complements the existing literature on I4.0 by identifying which technologies can contribute to KM. Also, a framework was proposed to examine the contribution of I4.0 technologies to KM at individual, team, and organisational levels.

This paper aims to understand the patterns of lean production implementation, and the relationship between three context factors (i.e. firm size, positions within the supply chain and time length of the lean initiative) and the adoption of lean production practices in firms of the automotive supply chain in Brazil.

The authors collected data from 65 companies of the automotive supply chain in Brazil. For data analysis, first a cluster analysis was performed to identify common characteristics in the companies’ context factors when considering patterns of lean implementation. Then, multivariate analysis of variance was used to investigate the differences between the context factors and the degree of use of lean practices.

High lean adopters had better performance than low lean adopters in terms of lead time, inventory and turnover. Firms at the first and second tier of the automotive supply chain were “leaner” than firms at the third tier. Large-sized firms were more likely to have a higher degree of use of lean practices than medium and smaller ones. Some, but not all, lean practices followed these patterns. Results also showed that some lean practices were most commonly adopted at the beginning of the lean journey, whereas others took more time to mature.

This paper demonstrated how lean practices were implemented at different positions within the supply chain, and the patterns of implementation often followed. It also considers lean in the context of developing countries such as Brazil.

The purpose of this paper is to examine the moderating role of Industry 4.0 technologies on the relationship between lean production (LP) and operational performance improvement within Brazil, a developing economy context.

One representative from each of the 147 studied manufacturing companies filled in a survey on three internally related lean practice bundles and two Industry 4.0 technology bundles, with safety, delivery, quality, productivity and inventory as performance indicators. As this study was grounded on the contingency theory, multivariate data analyses were performed, controlling for four contingencies.

Industry 4.0 moderates the effect of LP practices on operational performance improvement, but in different directions. Process-related technologies negatively moderate the effect of low setup practices on performance, whereas product/service-related technologies positively moderate the effect of flow practices on performance.

With the advent of Industry 4.0, companies have been channelling their efforts to achieve superior performance by advancing levels of automation and interconnectivity. Eventually, widespread and proven manufacturing approaches, like LP, will integrate such technologies which may, in turn, impair or favour operational performance. Contrary to previous studies, the contingencies appeared to have a less extensive effect. The authors point to various options for further study across different socio-economic contexts. This study evidenced that purely technological adoption will not lead to distinguished results. LP practices help in the installation of organisational habits and mindsets that favour systemic process improvements, supporting the design and control of manufacturers’ operations management towards the fourth industrial revolution era.

The purpose of this paper is to classify the main barriers related to the implementation of lean supply chain management (LSCM) and prioritize its main practices. A case study approach was conducted to obtain values for the intensity of the relationship between LSCM practices and barriers to mitigate or even anticipate difficulties in its implementation.

The case study in a company in southern Brazil that is in lean implementation is used for conducting this research. The methodology was structured in three steps, namely, classification of the main barriers through risk analysis, selection of LSCM practices appropriate to the company context and ranking and prioritization of the main practices of LSCM related to the barriers to lean implementation.

As a result, with the combination of two approaches, failure mode and effect analysis (FMEA) and analytical hierarchical process (AHP), it was possible to obtain values for the intensity of the relationship between LSCM practices and barriers. From the 12 barriers, 5 reached a high-risk degree that can be mitigated, and among the 18 practices found, 7 have the potential to be implemented.

This research allows direct efforts to continuously improve the supply chain to mitigate or even anticipate difficulties in its implementation. The proposed methodology, combining FMEA and AHP is easy to apply and understand, allowing managers and professionals to replicate it in the context of their supply chain and verify results similar and comparable to those obtained here.