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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.

It is important to consider economic and political factors when designing the tax mix and setting the level of corporate taxation. Increasing corporate taxation can be seen as an inefficient way to raise revenue for the state, as it can have a negative impact on investment and the competitiveness of firms. However, lowering corporate taxation can encourage investment and job creation, but it can also be perceived as supporting large corporations. The aim of this chapter is to evaluate corporate taxation, its position in the tax mix and its potential impact on economic growth. The revenues of corporate income tax (CIT) have an increasing tendency even though the tax rate was reduced from 41% to 19%. Revenues are influenced by both legislative changes and economic cycles. The level of taxation is also influenced by deductions, which include asset depreciations, research and development expenses, or loss deductions. The Pearson Correlation Coefficient was used to examine the correlation between the selected factors. A moderately strong positive correlation was found between GDP growth and CIT as a percentage of total taxes, as well as between GDP growth and CIT as a percentage of GDP.

Extensive macro- and micro-economics research has been conducted on China's tax reform, which replaced business tax with value-added tax (VAT). However, existing studies have not clarified the reform's impact on firm-level investment decisions. Hence, this study explored the effect of replacing business tax with VAT on firms' investment efficiency.

The study used 2010–2018 data from China's A-share listed companies and a difference-in-differences (DID) model to explore the effect of the reform on firm-level investment decisions.

The authors found that China's tax reform has improved investment efficiency in underinvested firms, increased liquidity and decreased the level of reliance on external financing. The tax reform had a greater effect on investment efficiency in firms with lower liquidity and higher external financing reliance. Its effect was also more significant among non-state-owned and small companies.

This study fills the aforementioned research gap by exploring the effects of China's tax reform, thus providing a theoretical reference and a basis for policymaking.

The purpose of this paper is to develop a systematic literature review on the sunk cost effect from consumers’ perspectives. By applying a comprehensive approach, this paper aims to synthesise and discuss the impact of financial and behavioural sunk costs on consumers’ decisions, judgements and behaviour before and after purchasing. This study also identifies potential research avenues to inspire further studies.

Following a search in the Scopus and Web of Science databases, a systematic literature review was conducted by identifying and analysing 56 peer-reviewed articles published between 1985 and 2022 (November). Descriptive and content analysis was implemented based on the selected papers to examine and synthesise the effect of sunk costs on consumers’ choices, evaluations and actions in a comprehensive approach; uncover research gaps; and recommend paths for future research.

The research results found in the literature are discussed according to five related themes: factors affecting the sunk cost effect; the impact of past investments on purchasing decisions; consumers’ post-purchasing evaluation, behaviour and choices; the mental amortisation of price; and the sunk cost effect on loyalty and switching.

The originality of this study lies in the comprehensive approach to the sunk cost effect from consumers’ perspectives. This review paper synthesises and discusses the research results found in the literature related to financial and behavioural sunk costs that can influence consumers’ decisions, judgements and behaviour before and after paying for a good or service.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.

This paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.

Four domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.

This paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

The learning outcomes of this study are to identify and discuss ways in which energy consumption in a residential educational institute can be reduced by improving demand-side energy management for sustainable development; summarise the challenges that an institute faces in transitioning to a more environmentally friendly mode of operations concerning energy management; illustrate the difference between operating expense and capital expenditure methods used for solar rooftop projects from the perspective of Goa Institute of Management (GIM); and analyse different project proposals for solar rooftop power generation energy using capital budgeting techniques.

Dr Ajit Parulekar, director at GIM, was evaluating the steps taken over the past few years for sustainable energy management to understand their impact and consider ways in which to take the environmental sustainability agenda forward. One of the projects that he was considering was the rooftop solar power plant. GIM had received proposals from several different vendors and evaluated three proposals out of these. He needed to decide on the capacity of the rooftop solar power generation and the type of contract that he should get into for the implementation of the project. This case study describes the differences and highlights the advantages and disadvantages of all the mentioned models with respect to GIM.

This case study is suitable for post-graduate level management students, as well as for undergraduate-level finance and management students.

Teaching notes are available for educators only.

CSS4: Environmental management.

This paper recommends a method entitled “SMED 4.0” as a development of conventional single minute exchange of die (SMED) to avoid defect occurrence during production and improve sustainability, besides reducing setup time.

The method builds upon an extensive literature review and in-depth explorative research in SMED and zero defect manufacturing (ZDM). SMED 4.0 incorporates an evolutionary stage that employs predict-prevent strategies using Industry 4.0 technologies including the Internet of Things (IoT) and machine learning (ML) algorithms.

It presents the applicability of the proposed approach in (1) identifying the triple bottom line (TBL) criteria, which are affected by defects; (2) predicting the time of defect occurrence if any; (3) preventing defective products by performing online setting on machines during production as needed; (4) maintaining the desired quality of the product during the production and (5) improving TBL sustainability in manufacturing processes.

The extended view of SMED 4.0 in this research, as well as its analytical approach, helps practitioners develop their SMED approaches in a more holistic way. The practical application of SMED 4.0 is illustrated by implementing it in a real-life manufacturing case.