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This empirical study explores the profound impact of management functions on the productivity of yard cargo handling equipment within container terminals.

By closely examining crucial management aspects such as planning, organizing, leading, and controlling, a comprehensive managerial behavior framework was developed through focus group studies (FGS) and focal interviews. These qualitative methods were complemented by the distribution of questionnaires to practitioners in Vietnam. To validate the concept of management functions and analyze their influence on effective management practices for equipment efficiency, a structural equation model (SEM) technique was employed using partial least-squares estimation (PLS).

The findings of this study demonstrate that planning (PL), organizing (OR), and controlling (CT) significantly contribute to the productivity of yard cargo handling equipment, while leading (LD) does not exhibit a direct positive impact.

Theoretically, this study contributes by providing clarity to the definition, purpose, and value of management functions in the field of cargo handling equipment management. Furthermore, these research findings offer valuable insights to terminal operators and managers, enabling them to optimize their management strategies and enhance productivity levels, ultimately resulting in improved operational outcomes.

The equipment resale price plays an important role in calculating the optimum time for equipment replacement. Some of the existing models that predict the equipment resale price do not take many of the influencing factors on the resale price into account. Other models consider more factors that influence equipment resale price, but they still with low accuracy because of the modeling techniques that were used. An easy tool is required to help in forecasting the resale price and support efficient decisions for equipment replacement. This research presents a machine learning (ML) computer model helping in forecasting accurately the equipment resale price.

A measuring method for the influencing factors that have impacts on the equipment resale price was determined. The values of those factors were measured for 1,700 pieces of equipment and their corresponding resale price. The data were used to develop a ML model that covers three types of equipment (loaders, excavators and bulldozers). The methodology used to develop the model applied three ML algorithms: the random forest regressor, extra trees regressor and decision tree regressor, to find an accurate model for the equipment resale price. The three algorithms were verified and tested with data of 340 pieces of equipment.

Using a large number of data to train the ML model resulted in a high-accuracy predicting model. The accuracy of the extra trees regressor algorithm was the highest among the three used algorithms to develop the ML model. The accuracy of the model is 98%. A computer interface is designed to make the use of the model easier.

The proposed model is accurate and makes it easy to predict the equipment resale price. The predicted resale price can be used to calculate equipment elements that are essential for developing a dependable equipment replacement plan. The proposed model was developed based on the most influencing factors on the equipment resale price and evaluation of those factors was done using reliable methods. The technique used to develop the model is the ML that proved its accuracy in modeling. The accuracy of the model, which is 98%, enhances the value of the model.

In today’s academic library, the reference department relies heavily on computer workstations to provide patrons with access to reference sources in CD‐ROM and Web formats. Many reference departments also supervise an electronic classroom which is used to provide hands‐on instruction. Planning for the hardware, software, and peripherals necessary to provide patrons with access and training must be an ongoing process in order to keep up with rapid technological changes, both in computer hardware and software applications. Through the maintenance of comprehensive records of existing equipment, including the purpose, capabilities and maintenance of each item, information will be readily available for use in planning for computer equipment. In this article, the authors discuss various types of records that should be kept for computer equipment and how the information contained in these records can be applied to ongoing planning and decision making for management and maintenance.

This study is intended to investigate the current practices and problems in heavy equipment management as well as to identify practices capable of alleviating equipment management problems for highway contractors in Thailand.

Equipment management practices were identified and analysed by SPSS using a questionnaire survey. ANOVA test was used to reveal significant differences in equipment management practices among different contractor sizes. Relationships between equipment management practices and problems were also revealed.

The equipment management practices vary, to some extent, among different contractor sizes. While practices of medium and small contractors tend to be similar, practices of large contractors are different from those of smaller contractors. Large contractors often put more emphasis on outsourcing strategy for equipment management. Moreover, large contractors frequently dispose of or replace equipment as soon as the equipment becomes inefficient before incurring high repair costs. Conversely, smaller contractors tend to mainly emphasise on the company finance and the budget availability as they often rely on purchasing strategy, especially buying used machines. Overall, equipment practices of large contractors were found to be more successful than smaller contractors in minimising equipment management problems, including long downtime duration and cost.

This research is of value for better understanding practices and problems relating to heavy equipment management among different contractor sizes. The study also highlights practices that are capable of reducing problems relating to heavy equipment management for highway contractors.

The purpose of the paper is to provide a method for selection of an optimum level of repair by replacement of an equipment based on its cost. In a ship where the engineer has a vast variety of equipment and systems to operate and maintain within limited time frames and availability of human resources, it is often difficult to disassemble a whole equipment to replace a faulty component. It is instead a lot easier to just replace the faulty equipment with whole new equipment. However, such a decision comes at an enormous capital cost. Therefore, the key question is, can we have a model to help us arrive at a decision on the correct level of carrying out repairs?

The paper uses a model based on cost and convolution of failure distributions of critical sub-components of an equipment. Necessary assumptions based on real life experience have been incorporated in the model.

The paper used an example of a particular type of motor driven sea water centrifugal pump which was commonly used in main engine sea water system, firefighting system, air conditioning system, etc. The pump had one of the highest failure rates in the ship (approximately one failure per 150 days) and the engineers found it cost and time effective to replace the entire pump on failure rather than carrying out replacement of the failed components. The model analyzed that the engineer’s hunch was not off the mark.

The implication of the work presented in the paper will be savings in maintenance cost and downtime due to optimal level of repairs on a multi-component equipment. The limitations of the work are assumption of independence of failures of components. This may not be true in all the cases. Further, opportunity based maintenance has also not been considered.

The originality of the paper lies in the presentation of a method for selection of an optimum level of maintenance for a multi-component equipment

To develop, test and implement a sampling strategy for equipment auditing for a Fortune 100 company.

Regression analysis is applied to auditing of equipment for a large US corporation. Empirical data and test data sets are used to evaluate the efficacy of using regression for auditing and to determine reasonable and efficient sample sizes to be employed across more than 5,000 locations.

Regression is a viable and useful method for equipment auditing when there is anticipated high correlation between pre‐ and post‐audit equipment value. Recommended sample size is dependent upon the size of the location as measured by total pieces of equipment. Decision rules combining acceptable tolerance limits, desired confidence level and sample size are provided.

The method, recommended sample sizes and decision rules are particularly applicable to instances where high correlation is expected between pre‐ and post‐audit equipment values. Standard regression assumptions are not all met in all instances, especially with small sample sizes.

The regression approach and model, sample size recommendations and decision rules for passing or failing an equipment audit described herein have been implemented at a Fortune 100 company, and are generally applicable to equipment and inventory auditing when high correlation between pre‐ and post‐audit equipment is expected.

This paper provides a practical and useful regression‐based approach to sampling for equipment auditing. Recommended sample sizes and decision rules for passing or failing the audit are explicitly defined.

The purpose of this paper is to solve the comprehensive evaluation of the equipment maintainability level based on grey system theory, and make an analysis of the corresponding influencing factors and their prioritization process.

Considering the diversity, uncertainty and small sample size of the influencing factors of the equipment maintainability level, a multilayer evaluation attribute system is set up, and the grey relational method is utilized to assess the equipment's comprehensive maintainability. First, the bottom layer relational coefficient and weighted relational degree are analyzed, and, by means of the focus of relational degree through the bottom layer to top layer, the general evaluation of the equipment maintainability is carried out. Second, the equipment maintainability level and its influencing factors model, i.e. GM(1,N) model are set up, and the prioritization of the influencing factors is achieved through the comparison of the size of the model drive coefficients. Finally, the practical example calculation results show that this method has not only realized a sensible and effective evaluation of the equipment maintainability level, but also provided a prioritization of the influencing factors, which helps to focus attention on the major influencing factors and make this method of significant engineering application value in the improvement of the equipment maintainability level.

The modeling of electronic equipment maintainability level and analysis of its corresponding practical example prove that grey system theory could not only perform a comprehensive evaluation of the equipment maintainability level, but also provide a quantitative analysis of its various influencing factors, whereas, other methods such as fuzzy mathematics, etc. can only make a general evaluation of the equipment maintainability level.

This paper has realized an integral evaluation of the equipment maintainability level and has made an analysis of the prioritization of its various influencing factors. These investigation results could be introduced as a promising innovative idea in the evaluation of the equipments' other performances and the prioritization of its various corresponding influencing factors.

Considering the diversity and uncertainty of influencing factors of the equipment maintainability level, this paper has realized a multilayer evaluation attribute system to perform a comprehensive evaluation of equipment maintainability level by means of weighted grey relational degree model. Furthermore, the prioritization of its various influencing factors is achieved based on the GM(1,N) model.

With the rapid development of the sharing economy in manufacturing industries, manufacturers and the equipment suppliers frequently share capacity through the third-party platform. This paper aims to study influences of manufacturers sharing capacity on the supplier and to analyze whether the supplier shares capacity as well as its influences.

This paper deals with conditions that the supplier and manufacturers share capacity through the third-party platform, and the third-party platform competes with the supplier in equipment sales. Considering the heterogeneity of the manufacturer's earning of unit capacity usage and the production efficiency of manufacturer's usage strategies, this paper constructs capacity sharing game models. Then, model equilibrium results under different sharing scenarios are compared.

The results show that when the production or maintenance cost is high, manufacturers sharing capacity simultaneously benefits the supplier, the third-party platform and manufacturers with high earnings of unit capacity usage. When both the rental efficiency and the production cost are low, or both the rental efficiency and the production cost are high, the supplier simultaneously sells equipment and shares capacity. The supplier only sells equipment in other cases. When both the rental efficiency and the production cost are low, the supplier’s sharing capacity realizes the win-win-win situation for the supplier, the third-party platform and manufacturers with moderate earnings of unit capacity usage.

This paper innovatively examines supplier's selling and sharing decisions considering manufacturers sharing capacity. It extends the research on capacity sharing and is important to supplier's operational decisions.

With the deep development of the new technological revolution and industrial transformation, the development, application, expansion and integration of digital technology provide opportunities for transforming the manufacturing industry from traditional manufacturing to intelligent manufacturing. However, little research currently focuses on analyzing the influencing factors of intelligent development in this field. There is a lack of research from the perspective of the digital innovation ecosystem to explore the intrinsic mechanism that drives intelligent development. Therefore, this article starts with high-end equipment manufacturing enterprises as the research subject to explore how their digital innovation ecosystem promotes the effectiveness of enterprise intelligent development, providing theoretical support and policy guidance for enterprises to achieve intelligent development at the current stage.

This article constructs a logical framework for the digital innovation ecosystem using a “three-layer core-periphery” structure, collects data using crawling for subsequent indicator measurement and assessment and uses the fuzzy set Qualitative Comparative Analysis method (fsQCA) to explore how the various components of the digital innovation ecosystem in high-end equipment manufacturing enterprises work together to promote the development of enterprise intelligently.

This article finds that the various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises, through mutual coordination, can help improve the level of enterprise intelligence. Empirical analysis shows four specific configuration implementation paths for the digital innovation ecosystem of high-end equipment manufacturing enterprises to promote intelligent development. The core conditions and their combinations that affect the intelligent development of enterprises differ in each configuration path.

Firstly, this article discusses the practical problems of intelligent transformation and development in the manufacturing industry and focuses on the intelligent development effectiveness of various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises in the context of digitalization. Secondly, this article uses crawling, text sentiment analysis and other methods to creatively collect relevant data to overcome the research dilemma of being limited to theoretical analysis due to the difficulty in obtaining data in this field. At the same time, based on the characteristics of high-end equipment manufacturing enterprises, the “three-layer core-periphery” digital innovation ecosystem framework constructed in this article helps to gain a deep understanding of the development characteristics of the industry's enterprises, provides specific indicator analysis for their intelligent development, opening the “black box” of intelligent development in the industry's enterprises and bridging the gap between theory and practice. Finally, this study uses the fsQCA research method of configuration analysis to explore the complexity of the antecedents and investigate the combined effects of multiple factors on intelligent development, providing new perspectives and rich research results for relevant literature on the intelligent development of high-end equipment manufacturing enterprises.