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It is argued that total quality management has, as one of its cornerstones, the measurement of quality‐related costs. These establish where the main areas of failure costs are and also monitor the progress of the quality improvement programme. A practical approach to both these aspects is discussed.

VUTRAX, Computamation's PCB design system, now has drivers for the Metheus 1004, 1008 and 1104 graphics cards offering 1024×768 resolution in 16 colours. This enhancement, giving significant increases in display speed, follows the recent introduction of a VUTRAX family for schematic entry, through basic manual PCB design to fully integrated automatic design systems. Major price advantages have been realised on many system modules while preserving the concept of function modularity, and systems can still be tailored to exact requirements.

Internet of things (IoT) and Big Data, which are among the pioneers of Industry 4.0 technologies, have gained great importance in recent years. Within the scope of Industry 4.0, organizations are trying to undertake digital transformation by adapting these two important technologies to their business processes. Undoubtedly, while this transformation provides great advantages for organizations in terms of management, organization, and marketing, it also carries disadvantages such as difficulties and complexity regarding the privacy of the collected data and systems. However, IoT and Big Data Analytics play a role as restructuring factors for products, services, and especially business processes. This study discusses the impact of IoT and Big Data Analytics on the digital transformation of organizations from the perspective of corporate culture, marketing, and management. Simultaneously, the effects of the COVID-19 epidemic that the world has experienced recently, on the business of institutions, are also discussed. By adopting IoT and Big Data Analytics, the attitudes, benefits, and challenges of the institutions that are or are not willing to realize digital transformation during the epidemic process are examined, and a projection is tried to be made to the post-COVID-19 period. While the study specifically highlights the positive effects of IoT and Big Data Analytics on the business, it sheds light on available opportunities and provides useful implications for managers and marketers.

The purpose of this paper is to propose an interpretive structural modeling (ISM) model which highlights the relationships between the identified just-in-time (JIT) elements useful for the implementation of JIT in maintenance and understand mutual influences of these identified JIT elements on JIT implementation in maintenance. Further, this paper seeks to identify dependence power and driving power of identified JIT elements using an ISM and Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis.

The methodology used in the paper is ISM with a view to evolving mutual relationships among JIT elements. The identified JIT elements have been further classified, based on their dependence power and driving power using MICMAC analysis.

This paper has developed the relationships among 16 identified JIT elements using the ISM methodology. Further, this paper analyses the driving power and dependence power of identified JIT elements with the help of MICMAC analysis. The incorporated approach is developed here, as the ISM provides only binary correlation among identified JIT elements. The MICMAC analysis is adopted here as it is useful in specific examination related to driving and the dependence power of identified JIT elements. The ISM developed model and MICMAC analysis finding are validated with the help of industrial experts.

The weightage and validation for the ISM and MICMAC analysis are obtained throughout the opinion of academics and industry experts. Further hypothesis may be conducted to examine the validity of the planned model, and structural model may also be validated statistically with the help of structural equation modeling.

The ISM model development and MICMAC analysis of identified JIT elements provide academics and maintenance managers a macro picture of the profits gained by the organizations by the implementation of JIT in maintenance of an organization.

The results will be useful for maintenance managers to understand the process of implementation of JIT in maintenance and to gain benefits after the implementation of JIT in maintenance of an organization.

Opportunistic maintenance (OM) policy is a prospective maintenance approach that instigates for a more effective and optimized system. The purpose of this paper is to provide the steps and methods used in model development processes for the application of the OM policy.

Dubbed as opportunistic principle toward optimal maintenance system (OPTOMS) for OM policy toward optimal maintenance system, the model is devised as a decision support system model and contains five phases. The motivation and focus of the model resolve around the need for a practical framework or model of maintenance policy for the application in an industry. In this paper, the OPTOMS model was verified and validated to ensure that the model is applicable in the industry and robust as a support system in decision making for the optimal maintenance system.

From the verification steps conducted in a case study company, it was found that the developed model incorporated simple but practical tools like check sheet, failure mode and effect analysis (FMEA), control chart that has been commonly used in the industry.

This paper provides the general explanations of the developed model and tools used for each phase in implementing OM to achieve an optimal maintenance system. Based on a case study conducted in a semiconductor company, the OPTOMS model can align and prepare the company in increasing machine reliability by reducing machine downtime.

The novelty of this paper is based on the in-depth discussion of all phases and steps in the model that emphasize on how the model will become practical theories in conducting an OM policy in a company. The proposed methods and tools for data collection and analysis are practical and commonly used in the industry. The framework is designed for practical application in the industry. The users would be from the Maintenance and Production Department.

The purpose of this paper is to formulate a benchmark to increase the tyre curing press production rate while minimizing tyre curing press downtime and maintenance cost with the help of a maintenance management technique based on overall equipment effectiveness (OEE).

The methodology is based on determining the OEE of tyre curing press before and after rectifying the causes of failures. The failure mode and effect analysis (FMEA) technique is used to find out the root causes of repetitive failures in tyre curing press by using the risk priority number.

A significant change in the value of OEE is observed after rectifying the repetitive failures, which were determined using the FMEA technique. Thus, it is concluded that the OEE and FMEA assist in improving the industrial performance and competitiveness of the production equipment studied.

This study is limited to determining the OEE of single equipment only, not the whole production system. Manufacturing facilities are dependent on the operating environment; therefore a comparison of two different manufacturing plants based on the OEE value would not be justified.

This study can be applied in any tyre manufacturing industry in order to take competitive benefits, such as reduction in equipment downtime, increased production and reduction in maintenance cost.

The angle from which the paper approaches the bottleneck problem in a tyre production line is original for the studied company and shows positives results. It allows the company to apply the same approach in its other production equipment, lines and factories to achieve improvement in industrial performance and competitiveness.

Owing to the toxicity, biodegradability, and cost of most corrosion inhibitors, research attention is now focused on the development of environmentally benign, biodegradable, cheap, and efficient options. In consideration of these facts, chrysin, a phytocompound of Populus tomentosa (Chinese white poplar) has been isolated and investigated for its anticorrosion abilities on carbon steel in a mixed acid and chloride system. This highlights the main purpose of the study.

Chrysin was isolated from Populus tomentosa using column chromatography and characterized using Fourier Transform Infrared Spectroscopy and Nuclear Magnetic Resonance Spectroscopy. The investigations are outlined based on theory (Fukui indices, condensed density functional theory and molecular dynamic simulation) and experiments (electrochemical, gravimetry and surface morphology examinations).

Theoretical evaluations permitted the description of the adsorption characteristics, and molecular interactions and orientations of chrysin on Fe substrate. The interaction energy for protonated and neutral chrysin on Fe (110) were −149.10 kcal/mol and −143.28 kcal/mol, respectively. Moreover, experimental investigations showed that chrysin is a potent mixed-type corrosion inhibitor for steel, whose effectiveness depends on its surrounding temperature and concentration. The optimum inhibition efficiency of 78.7% after 24 h for 1 g/L chrysin at 298 K indicates that the performance of chrysin, as a pure compound, compares favorably with other phytocompounds and plant extracts investigated under similar conditions. However, the inhibition efficiency decreased to 62.5% and 51.8% at 318 K after 48 h and 72 h, respectively.

The novelty of this study relies on the usage of a pure compound in corrosion suppression investigation, thus eliminating the unknown influences obtainable by the presence of multi-phytocompounds in plant extracts, thereby advancing the commercialization of bio-based corrosion inhibitors.

The purpose of this paper is to analyse and compare the downtime of four drilling machines used in two underground mines in Sweden. The downtime of these machines was compared to show what problems affect downtime and which strategies should be applied to reduce it.

The study collects failure data from a two-year period for four drilling machines and performs reliability analysis. It also performs downtime analysis utilising a log-log diagram with a confidence interval.

There are notable differences in the downtime of most of the studied components for all machines. The hoses and feeder have relatively high downtime. Depending on their downtime, the significant components can be ranked in three groups. The downtime of the studied components is due to reliability problems. The study suggests the need to improve the reliability of critical components to reduce the downtime of drilling machines.

The method of analysing the downtime, identifying dominant factors and the interval estimation for the downtime, has never been studied on drilling machines. The research proposed in this paper provides a general method to link downtime analysis with potential component improvement. To increase the statistical accuracy; four case studies was performed in two different mines with completely different working environment and ore properties. Using the above method showed which components need to be improved and suggestions for improvement was proposed and will be implemented accordingly.

The purpose of this paper is to implement the six sigma (SS) strategy in a bag sector under actual operating circumstances based on defining-measure-analyze-improve and control (DMAIC). During the project, several statistical tools and methods have been used efficiently to create inferences. Thus, to measure and enhance system efficiency, the author calculate reliability, availability and maintainability (RAM) indices. Based on this research, the author show how the SS method and RAM analysis are very helpful in determining maintenance intervals, as well as in planning and organizing the appropriate maintenance strategy.

This study introduces the step-by-step application of the DMAIC methodology for the identification and reduction of bag production line downtime and examines the present operations management. Thus, statistical techniques are used to analyze the failure and repair database. Pareto analysis, histograms and descriptive statistics at the machine and line-level of the historical data were conducted. Trend and serial correlation testing validated the hypothesis of independence and identical distribution of database was performed. In addition, with their best fit allocation, the RAM of both the bag production line and its machines was estimated at separate mission times.

The main goals of the applied method are to understand the nature of the downtime patterns and to accurately and quantitatively estimate the RAM characteristics of the bag production system. The assessment defines the production line's critical points, requiring further enhancement through an efficient maintenance approach. Therefore, by improving plant efficiency and safety, the author can decrease unplanned downtime and equipment failures.

This research is expected to serve as an attempt to conduct SS DMAIC methodology through RAM assessment and its impact on system efficiency under actual circumstances. The benefit of the methodology is that the manufacturing process is continuously monitored by suitable indicators, the use of which leads to a continuous improvement cycle.

Assessment of disturbance data from production equipment provides valuable information for improving productivity. To implement total productive maintenance (TPM) it is necessary to assess the magnitude of different types of production losses, in order to direct activities and allocate resources in an optimal way. In many enterprises, the focus is directed towards major time losses due to break‐downs rather than minor losses in speed and time. A method for implementing data collection is to start gradually by a simple model and develop this to a combined model with computerised systems and manual recording. This gives both an exact assessment of the magnitude of the disturbance and a deeper understanding of the reason for losses. According to about 20 cases, the overall equipment effectiveness was only around 55 per cent. Further, it is quite clear that performance losses are the dominating ones. If the production process is new to the company it is hypothesised that overall equipment effectiveness will be lower than if the company is already familiar with production process.