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In practical workshop production process, there are many production emergencies, e.g. new manufacturing tasks, facilities failure and tasks change. On one hand, it results in poor timeliness and reliability of real-time production data collection, acquisition and transmission; on the other hand, it increases the difficulty of real-time data tracking and monitoring. This paper aims to develop a novel RFID-based tracking and monitoring approach of real-time data in production workshop (TM_rfid) to solve them.

At first, a three-layer model of real-time data based on RFID has been constructed, which contains RFID-based integrated acquisition center; “RFID & Cloud-service-rules”-based calculation and analysis center; and “RFID & Ontology-knowledge-base”-based monitoring and scheduling center. Then, a targeted analysis and evaluation method of TM_rfid with feasibility, quality and performance has been proposed. Finally, a prototype platform of a textile machinery manufacturing enterprise has been built to verify the effective of TM_rfid.

The effectiveness of TM_rfid is verified by applying two groups of actual experimental data from the case enterprise, the results show that TM_rfid can promote the efficiency, reliability and feasibility of tacking and monitoring of real-time data in production workshop.

RFID-based tracking and monitoring approach of real-time data in production workshop has been developed to solve the data information transmission and sharing problem. Three analysis and evaluation approaches have been introduced to solve the un-standardized evaluation problem of RFID application. A prototype platform of a textile machinery manufacturing enterprise has been constructed.

The purpose of this paper is to examine the relationship between customer order lead time (COLT) and the price sensitivity of an electrical equipment manufacturer company. In consequence, it examines two research questions in terms of COLT, price and profitability level and to ensure the validity and practical justification of these research questions.

In this research the authors have used a case study approach where three business measures, namely COLT, price and the profitability level of a case company were investigated and analyzed critically. These measures were implemented through four different customer segments with two production lines of the case company. Data were collected from the company's order delivery database from the period 2006 to 2008. In addition, different experimental data were collected through interviewing and reviewing the results of the data analysis with the unit managers.

In this paper the authors have observed the correlation between the price, profit and COLT with all four customer segments in both the production lines of the case company. From the case data, the authors concluded that the customer did not pay more when the COLT is shorter than with the average time. It is also noticeable that the profit margin is higher for the case company to handle COLT with shorter lead time than the average order delivery lead time.

More case examples might be helpful to motivate the managers to accept the research outcomes.

The concept of the company's COLT in relation to the price and profitability level supports organizational managers in their decision‐making process in terms of productivity level and the company's growth. It will motivate the managers to make tradeoffs among various developmental measures.

This paper implemented a unique approach for measuring the significant level of price and profitability level over COLT. From the outcomes of this study, it is observed that the price correlated positively with the COLT and has a direct and significant impact on it. When the price is increased the COLT is also increased. It is also noticed that the products of the case company which offered shorter lead times were on average also more profitable, even though there were no significant differences in average pricing between the customer segments.

With production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive maintenance and maintenance reporting to increase maintenance operation efficiency, operational data may also be used to improve maintenance management. Research on the value of data-driven decision support to foster increased internal integration of maintenance with related functions is less explored. This paper explores the potential for further development of solutions for cross-functional responsibilities that maintenance shares with production and logistics through data-driven approaches.

Fifteen maintenance experts were interviewed in semi-structured interviews. The interview questions were derived based on topics identified through a structured literature analysis of 126 papers.

The main findings show that data-driven decision-making can support maintenance, asset, production and material planning to coordinate and collaborate on cross-functional responsibilities. While solutions for maintenance planning and scheduling have been explored for various operational conditions, collaborative solutions for maintenance, production and logistics offer the potential for further development. Enablers for data-driven collaboration are the internal synchronization and central definition of goals, harmonization of information systems and information visualization for decision-making.

This paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.

The purpose of this paper is to present a structured framework whose objectives are to identify, analyse and eliminate fashion-luxury supply chains inefficiencies.

A Lean Manufacturing tool, the 5-Whys Analysis, has been used to find out the root causes associated with the problem identified from a data analysis of production orders of a fashion-luxury company. A case study, which explains the methodology and illustrates the capability of the tool, is provided.

This tool can be considered a suitable instrument to identify the causal factors of inefficiencies within luxury supply chains, suggesting potential countermeasures able to eliminate the problems previously highlighted. In addition, enabling technologies that deal with Industry 4.0 are associated with the root causes to enable further improvement of the supply chain.

The effectiveness and practicality of the tool are illustrated using an industrial case study concerning an international Italian signature in the world of fashion-luxury footwear sector.

This framework provides practitioners with an operative tool useful to highlight where the major inefficiencies of fashion-luxury supply chains take place and, at the same time, individuates both the root causes of inefficiencies and the corresponding corrective actions, even considering Industry 4.0 enabling technologies.

This is the first data-driven pilot study that is focused on the micro-scaled but burgeoning Bruneian screen industry. The scope of the study includes employment modes, frequency of productions, payment codes of practice and exhibition platforms, based on the data collected by both online surveys and semi-structured, face-to-face interviews between January to June 2021. The aim of this paper is to investigate the possible creation of a temporarily closed system of interaction between the local screen industry and other Bruneian businesses. It is hypothesised that the COVID-19 pandemic and subsequent action taken by the Bruneian Government to restrict international travel and border crossings resulted in a new and rare interchange of production-based activity. The study targets a wide variety of parties, from industry professionals, e.g., production house managers, senior producers, freelancers, and the so-called gig-lancers, to governmental policymakers, i.e., representatives from Authority Info-communications Technology Industry (AITI) in Brunei.

Lean distributed manufacturing (LDM) is being considered as an enabler of achieving sustainability and resilience in manufacturing and supply chain operations. The purpose of this paper is to enhance the understanding of how LDM characteristics affect the resilience of manufacturing companies by drawing upon the experience of food manufacturing companies operating in the UK.

The paper develops a conceptual model to analyse the impact of LDM on the operational resilience of food manufacturing companies. A triangulation research methodology (secondary data analysis, field observations and structured interviews) is used in this study. In a first step, LDM enablers and resilience elements are identified from literature. In a second step, empirical evidence is collected from six food sub-sectors aimed at identifying LDM enablers being practised in companies.

The analysis reveals that LDM enablers can improve the resilience capabilities of manufacturing companies at different stages of resilience action cycle, whereas the application status of different LDM enablers varies in food manufacturing companies. The findings include the development of a conceptual model (based on literature) and a relationship matrix between LDM enablers and resilience elements.

The developed relationship matrix is helpful for food manufacturing companies to assess their resilience capability in terms of LDM characteristics and then formulate action plans to incorporate relevant LDM enablers to enhance operational resilience.

Based on the literature review, no studies exist that investigate the effects of LDM on factory’s resilience, despite many research studies suggesting distributed manufacturing as an enabler of sustainability and resilience.

The purpose of this paper is to present an application of cellular manufacturing techniques and the evaluation of alternative production scenarios.

Advanced techniques were implemented in the production process of a company and then the efficiency of the effected changes on its operation were evaluated. Group technology was adopted as the (re)organization tool of its production. Indices concerning the performance of the system were identified using “real world” production results and statistical data. The operational characteristics of 12 alternative production scenarios were evaluated using the cellular manufacturing approach. The behavior of the manufacturing system as a whole, for each one of the different scenarios, was investigated using the discrete events simulation technique for a period of two years. Finally, data envelopment analysis was employed to evaluate the efficiency of each scenario.

Results indicated that more than one of these scenarios could be efficient, while significant improvements can be achieved without actually changing the basic production parameters.

The proposed procedure was implemented in a company from the cosmetics industry.

The paper presents a successful application of cellular manufacturing techniques together with the evaluation of alternative production scenarios.

The purpose of this paper is to identify maintenance improvement potentials using an overall equipment effectiveness (OEE) assessment within the manufacturing industry.

The paper assesses empirical OEE data gathered from 98 Swedish companies between 2006 and 2012. Further analysis using Monte-Carlo simulations were performed in order to study how each OEE component impacts the overall OEE.

The paper quantifies the various equipment losses in OEE, as well as the factors availability, utilization, speed, quality, and planned stop time. From the empirical findings, operational efficiency losses are found to have the largest impact on OEE followed by availability losses. Based on the results, improvement potentials and future trends for maintenance are identified, including a systems view and an extended scope of maintenance.

The paper provides detailed insights about the state of equipment effectiveness in terms of OEE in the manufacturing industry. Further, the results show how individual OEE components impact overall productivity and efficiency of the production system. This paper contributes with the identification of improvement potentials that are necessary for both practitioners and academics to understand the new direction in which maintenance needs to move. The authors argue for a service-oriented organization.

The purpose of this paper is to establish mechanisms for process improvement so that production efficiency and product quality can be expected, and create a sustainable development in terms of circular economy.

The authors obtain a critical value from statistical hypothesis testing, and thereby construct a process capability indices chart, which both lowers the chance of quality level misjudgment caused by sampling error and provides reference for the processes improvement in poor quality levels. The authors used the bottom bracket of bicycles as an example to demonstrate the model and methods proposed in this study.

This approach enables us to plot multiple quality characteristics, despite varying attributes and specifications, onto the same process capability analysis chart. And it therefore increases accuracy and precision to reduce rework and scrap rates (reduce), increase product availability, reduce maintenance frequency and increase reuse (reuse), increase the recycle rates of components (recycle) and lengthen service life, which will delay recovery time (recovery).

Parts manufacturers in the industry chain can upload their production data to the cloud platform. The quality control center of the bicycle manufacturer can utilized the production data analysis model to identify critical-to-quality characteristics. The platform also offers reference for improvement and adds the improvement achievements and experience to its knowledge management to provide the entire industry chain. Feedback is also given to the R&D department of the bicycle manufacturer as reference for more robust product designs, more reasonable tolerance designs, and selection criteria for better parts suppliers, thereby forming an intelligent manufacturing loop system.

The purpose of this paper is twofold: first, a case study on the application of lean production principles in a manufacturing facility is presented to demonstrate the impact of frequent and systematic use of a Kaizen event on quality and delivery performance. Second, the detailed description and analysis of the Kaizen event and its impact are provided, including a comprehensive analysis of the role of Kaizen events on employee participation and motivation.

The study utilizes a Kaizen event’s case study data with the help of various waste detection and elimination tools and techniques. Changes in overall productivity along with potential long-term improvements in the delivery process are also analyzed and documented.

Pre- and post-quality measures are provided to demonstrate the results of the event on the production quality and on the performance of the overall manufacturing processes. Qualitative findings regarding performance measurements and the impact on the employees are reported.

The Kaizen team applied analytical techniques to one manufacturing site in North America of a company that has a manufacturing presence in 20 different countries.

Kaizen studies involving packaging operations are quite limited. This study fills this gap by detailing the Kaizen event implementation in a packaging delivery and dispensing systems manufacturer for the cosmetic industry. The implementation of this Kaizen event is detailed along with the data and techniques utilized for process improvement. The study also reports findings regarding the impact of the Kaizen event on employee participation.