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The purpose of this paper is to develop a systematic strategic approach to handle corrective maintenance onto the failures/breakdowns of construction equipment. For the maintenance crew/team, a breakdown code management is proposed, which will provide focused and unambiguous approach to manage any kind of breakdowns in construction equipments.

The past breakdown records of a construction organization in the UAE are considered for analysis. From the failure data, through cause effect analysis (CEA) tools, the components and the breakdown codes namely breakdown main codes (BMC) and breakdown sub-codes (BSC) are formulated. With Pareto analysis, the critical codes are identified and validated through failure modes and effects analyses (FMEA) tools for the critical effect on the affected components. From this identified BSC's further closer failure identification codes namely breakdown symptom codes (BSyC) and breakdown reason codes (BRC) are identified through fault tree analysis (FTA) tools. The approach to modified breakdown maintenance management (MB2M) with breakdown maintenance protocol (BMP) is envisaged.

The study was conducted on four different types of heavy lifting/earth moving/material handling system of equipment and further focused with two earth moving equipment namely dumpers and wheel loaders. Failure analysis is performed and the failure ratio and the component contribution to the failures are identified. Based on the information, the preliminary codes namely BMC and BSC are identified through CEA tools and the BMC and BSC are identified to find the most contributing codes to the maximum number of failures through Pareto analysis. Further the critical sub-codes are further verified through FMEA tools on the severity levels of the sub components due to these codes. The FTA methods are used to identify the closer reasoning and relations of these codes and the further codes namely BSyC and BRC are identified which are the exact cause of the failures. The management of breakdowns is further proposed through MB2M which includes BMP which provides all resources for the breakdowns.

The failure data collected are only pertaining to the Middle East region and applicable to similar regions for similar plant mix in construction companies. The sample equipment is only part representative of the construction equipment. A more robust model can be suggested in the future covering all aspects and for other regions as well.

The proposed methodology and model approach is highly adaptable to similar industries operating in the Middle East countries.

Many authors have studied the preventive maintenance models and procedures and proposals have been proposed. On the breakdown maintenance management of construction equipment, very few studies have been proposed mostly on the cost analysis. This model attempts to provide a code management solution to manage the unpredictable failures in construction equipment through failure data analysis on a construction organization.

The purpose of this paper to develop a novel strategic approach to handle corrective maintenance procedure in the event of a breakdown/disruption of service. A proposal to minimize the recovery time and the breakdown cost in the system in construction plant is presented.

The past plant breakdown records of a construction organization are considered for the analysis. From the previous breakdown records, a high level metric using Pareto analysis and the cause effect analysis is used to identify the main breakdown main codes (BMC) and the subsequent breakdown sub codes (BSC). Prioritized BMC and BSCs are used to formulate dedicated breakdown maintenance teams, which act swiftly in the event of the breakdown with the modified methods.

The study was conducted, on four different types of heavy lifting/earth moving/material handling system equipment, which are used to load/unload/haul and transport construction materials. Failure due to tyre puncture and allied problems contribute to maximum failure. A strategy plan to minimize this type of failure is proposed. With the identification of the most contributing BMCs and BSCs, it is further proposed to develop an “overall breakdown maintenance management”.

The collected data pertains to the construction plant located in a particular region, namely the Middle East, and hence the proposed solution is dedicated/relatively applicable to similar plant from the same region. A more robust model can be suggested considering the work environment in the other regions.

The proposed methodology is highly adaptable by similar industries operating in the Middle East region.

Construction plant and equipment contribute to the success of construction organizations, by providing enhanced output, reduced manpower requirement, ease of work and timely completion of the project. Delays in completion of projects generally have both social and economical impact on the contractors and the buyers. The proposed model will bring down the lead‐time of the project and enable the contractors to crash down their project completion time.

Numerous studies on preventive maintenance models and procedures are available for a system and in particular to construction plant maintenance in the literature. This model attempts to handle the issues of unpredictable breakdowns in the construction plant to minimise the breakdown time. The proposed model is a novel approach which enables a quick recovery of the construction plant, attributed from the breakdown parameters derived from the previous history of the work records/environment.

The artificial intelligence (AI) based total productive maintenance (TPM) condition based maintenance (CBM) approach through Industry 4.0 transformation can well predict the breakdown in advance to eliminate breakdown.

Meeting the customer requirement as per the delivery schedule with the existing resources are always a big challenge in industries. Any catastrophic breakdown in the equipment leads to increase in production loss, damage to machines, repair cost, time and affects delivery. If these breakdowns are predicted in advance, the breakdown can be addressed before its occurrence and the demand supply chain can be met. TPM is one of the essential operational excellence tool used in industries to utilize the existing resources of a plant in a optimal way. The conventional time based maintenance (TBM) and CBM approach of TPM in Industry 3.0 is time consuming and not accurate enough to achieve zero down time.

The proposed AI and IIoT based TPM is achieved in a digitalized data oriented platform to monitor and control the health status of the machine which may reduce the catastrophic breakdown by 95% and also improves the quality rate and machine performance rate. Based on the identified key signature parameters related to major breakdown are measured using the sensors, digitalised by programmable logic controller (PLC) and monitored by supervisory control and data acquisition (SCADA) and predicted in server or cloud.

Long short term memory based deep learning network was developed as a regression forecasting model to predict the remaining useful life RUL of the part or assembly and based on the predictions, corrective action has been implemented before the occurrence of breakdown. The reliability and consistency of the proposed approach are validated and horizontally deployed in similar machines to achieve zero downtime.

In pace with an increasing awareness of the negative effects, both in terms of productivity and social aspects, of taking the division of work and specialization too far, the question concerning transfer of maintenance work from the maintenance unit to the operators has increasingly been focused. Discusses the factors that exert an influence on the design of the interface between operation and maintenance as well as the implications of different design alternatives, in terms of different aspects of productivity and job satisfaction in three highly mechanized production units (one producing disposable napkins and two vehicle components) within two Swedish companies. The case studies demonstrate that creating well‐defined areas of responsibility, placing overall responsibility for maintenance in the hands of one individual and establishing direct contacts between operators and repairmen are measures that exert a positive influence on productivity. In addition, the case studies show the necessity of linking the design of the maintenance work to the operator′s overall work situation and competence level, and that operator competence is an important factor for attaining productivity and job satisfaction.

The advancement of data analysis tools makes analyzing a huge amount of literature possible. This paper aims to identify tourism information technology research trends with the assistance of Toolkit for Academic Research (CATAR) to answer two research questions: the status of tourism information technology (IT) articles published in these selected journals from 1990 to 2016, and whether the number of Tourism IT articles is increasing or decreasing, and the main research topics studied in these articles from 1990 to 2016, and whether those have shifted over the 27 years.

Selected keywords identified by researchers that were used to extract Tourism IT articles published in Web of Science database. CATAR was used to carry out two analyses, namely, overview analysis and breakdown analysis. Overview analysis is performed to apply citation analysis to identify the general trend and the most cited reference of Tourism IT research represented by the selected articles. Breakdown analysis is based on bibliographic coupling (BC) analysis.

The number of Tourism IT publications grew rapidly in the second half of the 2000s, as mobile phones with touch screens became increasingly popular. It signals changes in the way of information searching behaviors. A total of 769 authors from 44 countries contributed to Tourism IT research. The USA, China, UK and Australia are the countries contributing the most. Six subject areas were revealed, namely, the interaction of people and organization; interaction of people and information; interaction of people and technology; technological implementation; the interaction of organization and information; and interaction of organization and technology.

The major limitation of this study must be considered – the use of data merely derived from WoS database. The BC analysis makes a comparison between references of the given articles. This analysis requires the citation sources with better data quality, which is mainly based on the accuracy and standardization of databases. WoS is the database currently fit this requirement. The emergence of the new citation tools will enable scholars to pull data from new databases and to clean the data. Incorporating data from those new databases will reveal more accurate and comprehensive picture of Tourism IT research trend.

This paper revealed the research trend and major research subject areas of Tourism IT research for the past 16 years. The results can assist scholars to quickly grasp the Tourism IT research. This paper also provides an alternative approach to conduct literature review with computer-assisted analysis program.

The purpose of this paper is to describe the current knowledge management activities at the European Space Operations Centre (ESOC), specifically in knowledge capture, sharing and preservation and focusing on the developed and adopted methods and on the results obtained related to the process of knowledge capture using video‐recording of experts.

The knowledge management activities undertaken at ESOC during the past years have been organized into four phases: preliminary study and investigation on knowledge management systems including a review of already existing initiatives; a pilot project within the Flight Dynamics area (a key technical domain within ESOC taking care of the orbit and attitude aspects for the satellite operations) with respect to the questions of knowledge transfer and its barriers; the definition of the ESOC knowledge management system; and the introduction of knowledge capture procedures.

The inventory of the knowledge assets and the analysis of the knowledge coverage and criticality were essential to be able to identify the existing knowledge in specific areas and to perform a gap analysis. Through this analysis, it is now possible to identify those areas requiring improvement and to allow management to make reliable decisions for which areas the knowledge level should be increased.

With regard to the firm strategic behaviours, it seems particularly crucial to exploit all three dimensions of proximity, in order to guarantee openness and sustain innovativeness and competitiveness. Concerning policy implications, the local governments should address their actions to help and promote the openness of technology districts and the formation of technology clusters. With this aim, actions should be devoted also to sustain single local firms that are part of a technology cluster but not of a technology district. These, in fact, by increasing their competitive position, may generate positive externalities in the local area, fostering the diffusion and sharing of knowledge in the area and then, acting as knowledge gatekeepers for the whole area.

The paper describes the implementation strategy of the knowledge management within ESOC. Based on this practical experience, guidelines can be derived for the implementation of a more general knowledge management system in the aerospace industry.

Describes how a training programme enabled UK food producer Campbell's Grocery Products Ltd to make the most of its technicians' skills.

Details the situation at the company before the training was delivered, the form the training took, and the training outcomes.

Reveals that Campbell's has achieved all its aims from the programme in three years: stock accuracy has improved, stockholding has fallen, breakdowns have been reduced, customer service has improved, and time taken to repair faults has dropped. Technicians' morale and team spirit are greatly improved, especially as a result of the increased efficiencies generated by the training, which has resulted in less fire‐fighting and frustration.

Reveals that the UK operations director of Campbell's regards the plant as a world‐class operation.

Describes how the technicians' former feelings of frustration have been replaced by the feeling that they are really contributing to the site's success.

During the last decade a substantial number of empirical research studies on export performance have been conducted. This article reviews 55 of these studies, summarises the findings according to a “strategic export model”, synthesises current knowledge, and suggests directions for future export research activities.

This research aims to develop a new generic framework for estimating different maintenance costs (preventive, corrective and conditional based) and its distribution to original equipment manufacturer (OEM), customer and supply chain due to no fault found (NFF) events. The study extend the domain of NFF to military aircraft maintenance, repair and overhaul (MRO) by including broader range of cost drivers than are normally found in maintenance NFF literature.

The research applies the soft system methodology involving 80 field surveys and five in depth semi-structured interviews with practicing experts having background in military aircraft NFF MRO. For impact analysis, authors have used an agent-based model to represent and prioritize the critical NFF cost drivers during aircraft MRO based on the cost inputs of 21 technology transfer cases.

The paper provides imperial insights about how NFF cost drivers affect the OEM, customer and supply chain. It suggests that NFF cost need to be part of the commercial MRO contract, depending on its frequency pattern in different types of maintenances.

The context of the current research is military aircrafts industry and may lack generalizability to commercial aircrafts. Therefore, researchers are encouraged to test the proposed propositions further.

The developed framework will provide invaluable help in key financial decision-making during signing of MRO contract in technology transfer cases.

This paper proposed a new prediction model for NFF cost estimation across its shareholders and current status of NFF in military aircraft NFF MRO in India.

Construction plant breakdown affects projects by prolonging duration and increasing costs. Therefore, prediction of plant breakdown, as a precursor to conducting timely maintenance works, cannot be underestimated. This paper thus sought to develop a model for predicting plant breakdown time from a sequence of discrete plant breakdown measurements that follow non‐random orders. An ARIMA (1,1,0) model was constructed following experimentation with exponential smoothening. The model utilised breakdown observations obtained from six wheeled loaders that had operated a total of 14,467 hours spread over a 300‐week period. The performance statistics revealed MAD and RMSE of 5.03 and 5.33 percent respectively illustrating that the derived time series model is accurate in modelling the dependent variable. Also, the F‐statistics from the ANOVA showed that the type and frequency of fault occurrence as a predictor variable is significant on the model's performance at the five percent level. Future work seeks to consider a more in depth multivariate time series analyses and compare/contrast the results of such against other deterministic modelling techniques.