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The purpose of this paper is to establish a combined model for selecting key indexes of complex equipment, and then improve the cost forecasting precision of the model. The problem how to choose the key elements of complex products has always been concerned on many fields, such as cost assessment, investment decision making, etc.

Using Grey System Theory to establish a cost estimation model of complicated equipment is more reasonable under the few data and poor information. Therefore, this paper constructs cost index’s system of complex equipment, and then quantitative and qualitative analysis methods are utilized to calculate the grey entropy between the characteristic parameter and the behavior parameters. Further, establish the grey relational clustering matrix of the behavior sequences by using the grey relative incidence analysis. Finally, the authors select key indicators according to the grey degree.

The experiment demonstrates that the cost key parameters of complex equipment can be successfully screened out by the proposed approach, and the cost estimation accuracy of complicated products is improved.

The method proposed in this paper could be utilized to solve some practical problems, particularly the selection of cost critical parameters for complex products with few samples and poor information. Taking the cost key indexes of civil aircraft as an example, the results verified the validity of the GICM model.

In this paper, the authors develop the method of GICM model. Taking the data of civil aircraft as an example, the authors screen the key indicators of complex products successfully, and improve the prediction accuracy of the GM (1, N) model by using the selected parameters, which provides a reference for some firms.

The purpose of this paper is to propose a prediction model to predict the cost of complex products with lack of data. The cost estimating is one of the key elements of arguments around technological economy and investment decision‐making process of complex product.

A complex product has many characteristics, such as complex structure, large investment, high risk and it usually falls into small‐batch‐production category. Its cost estimation samples are small and cost data are very limited. Based on the characteristics of complex product and cost estimating, this paper introduces performance parameters sequence of associated known data, establishes an N‐GM (0, N) model of characteristic sequence with straddle missing data.

On the basis of the known key performance parameter sequence, N‐GM (0, N) model is used to predict the grey interval of overall cost vacancy data. Overall cost vacancy data is whitened by sorting reference sequence and realizing complex product overall cost estimation.

The method introduced in the paper can be used to solve practical problems, especially cost prediction of complex products with poor data. The model is also applied on the overall cost and the key component cost estimation of similar but different complex products. Moreover, it provides potential theoretical support for the development of complex product industry in the future.

In this paper, the complex product, which now plays a strategic industrial role in China, is systematically studied by utilizing a new methodology based on grey systems, especially the cost evaluation of the complex product. The use of grey correlation analysis in screening control key item index of complex product cost, the overall cost sequence of the complex product as related sequence and sorting reference sequence, the paper predicts and whitens vacant key item index, obtaining the key item cost index of complex product.

The purpose of this paper is to propose a system with the highest discriminatory power by selecting an indicator system based on the K–S test according to the unique circumstances of small enterprises.

The proposed method relies on calculating the K–S test statistical magnitude of D iteratively to reach a system with the maximum discriminatory power.

The empirical results, demonstrated using 3,045 small businesses from a Chinese bank, show that credit rating system should focus on the indicator system’s discriminatory power rather than a single indicator’s discriminatory power, because the interaction between indicators affects the discriminatory power of the system.

The proposed method creates a credit rating system with the highest discriminatory power, rather than its indicators, which is a more reasonable and novel approach to credit rating.

The approach is unique because the final system will have high discriminatory power and has excellent potential for decision support. The authors believe that this contribution is theoretically and practically relevant because credit rating for small business is especially difficult and complicated.

This paper analyzes the impact of economic downturns on the revenue and expense sides of city financing for the period 2003 to 2009 using a convenience sample of the audited end of year financial reports for thirty midsized US cities. The analysis focuses on whether and how quickly and how extensively revenue and spending directions from past years are altered by recessions. A seven year series of Comprehensive Annual Financial Report (CAFR) data serves to explore whether citiesʼ revenues and spending, especially the traditional property tax and core functions such as public safety and infrastructure withstood the brief 2001 and the persistent 2007 recessions? The findings point to consumption (spending) over stability (revenue minus expense) for the recession of 2007, particularly in 2008 and 2009.

Real flight is cognitively demanding; accordingly, both indicators and display panel layout should be user-friendly to improve pilot-aircraft interaction. Poor pilot-interface interactions in aircrafts could result in accidents. Although a general reason of accidents is improper displays, relatively few studies were conducted on interfaces. This study aims to present an optimization model to create intuitively integrated user-friendly cockpit interfaces.

Subjectivity within most usability evaluation techniques could bring about interface design problems. A priori information about indicator’s possible locations may be available or unavailable. Thus different analytical approaches must be applied for modifications and new interface designs. Relative layout design (RLD) model was developed and used in new interface designs to optimize locations of indicators. This model was based on layout optimization and constructed in accordance with design requirements, ergonomic considerations with the pilot preferences. RLD model optimizes interface design by deploying indicators to the best locations to improve usability of display panel, pilot-aircraft interaction and flight safety.

Optimum interfaces for two problem instances were gathered by RLD model in 15.77 CPU(s) with 10 indicators and 542.51 CPU(s) with 19 indicators. A comparison between relative and existing cockpit interfaces reveals that locations of six navigation and four mechanical system indicators are different. The differences may stem from pilots’ preferences and relativity constraints. Both interfaces are more similar for the central part of the display panel. The objective function value of relative interface design (Opt: 527938) is far better than existing interface (737100). The RLD model improved usability of existing interface (28.61 per cent considering decrease in the objective function values from 737100 to 527938.

Future cockpit and new helicopter interface designs may involve RLD model as an alternative interface design tool. Furthermore, other layout optimization problems, e.g. circuit boards, microchips and engines, etc. could be handled in a more realistic manner by RLD model.

Originality and impact of this study related to development and employment of a new optimization model (RLD) on cockpit interface design for the first time. Engineering requirements, human factors, ergonomics and pilots’ preferences are simultaneously considered in the RLD model. The subjectivity within usability evaluation techniques could be diminished in this way. The contributions of RLD model to classical facility layout models are relativity constraints with the physical constrictions and ergonomic objective function weights. Novelty of this paper is the development and employment of a new optimization model (RLD) to locate indicators.

Building energy efficiency retrofit (BEER) not only provides excellent opportunities to reduce overall energy consumption of buildings in a city but also encourages environmental protection, the rational use of resources, and occupants' healthcare, which all contribute towards the sustainability of existing buildings. However, there is a lack of effective performance indicators to measure the sustainability of BEER projects. The aim of this paper is to formulate a list of key performance indicators (KPI) for the sustainability assessment of BEER in hotel buildings.

First, a literature review and in‐depth interviews with industry experts and academic researchers were conducted, which filtered the performance indicators for assessing sustainability. Second, a questionnaire survey was conducted to collect data from various groups of experts to analyze the significance of the selected performance indicators. Finally, a model based on fuzzy set theory was designed to identify the key performance indicators (KPIs) for the sustainability of BEER.

Eight KPIs were identified based on fuzzy set theory in this study. They are: quality performance, hotel energy management, cost performance, project profitability, energy consumption and resources saving, health and safety, stakeholder satisfaction, and innovation and improvement.

The KPIs of sustainability of BEER identified for hotel buildings in China in this study can be useful reference for other similar research. However, with the different requirements for building types and building ownerships, the KPIs of sustainability of BEER for different buildings may be variable. The findings in this study may not be directly relevant to other types of building.

Key performance indicators for the sustainability assessment of BEER in hotel buildings in China are identified and analyzed in this study. The KPIs can help decision‐makers to identify an optimal solution between alternatives, which presents the maximum sustainability performance.

Sustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners because the indicators are still underdeveloped in achieving SSCF. This study proposes a bibliometric data-driven analysis from the literature to illustrate a clear overall concept of SSCF that reveals hidden indicators for further improvement.

A hybrid quantitative and qualitative approach combining data-driven analysis, fuzzy Delphi method (FDM), entropy weight method (EWM) and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is employed to address the uncertainty in the context.

The results show that blockchain, cash flow shortage, reverse factoring, risk assessment and triple bottom line (TBL) play significant roles in SSCF. A comparison of the challenges and gaps among different geographic regions is provided in both advanced local perspective and a global state-of-the-art assessment. There are 35 countries/territories being categorized into five geographic regions. Of the five regions, two, Latin America and the Caribbean and Africa, show the needs for more improvement, exclusively in collaboration strategies and financial crisis. Exogenous impacts of wars, natural disasters and disease epidemics are implied as inevitable attributes for enhancing the sustainability.

This study contributes to (1) boundary SSCF foundations by data driven, (2) identifying the critical SSCF indicators and providing the knowledge gaps and directions as references for further examination and (3) addressing the gaps and challenges in different geographic regions to provide advanced assessment from local viewpoint and to diagnose the comprehensive global state of the art of SSCF.

Based on the poll’s results, the financial services sector is now on the edge of a digital revolution. Financial institutions are undergoing a period of radical change due to technological advancements in the digital sphere. The widespread use of cutting-edge digital technologies in the real world has accelerated the shift from the conventional economy to the digital economy. Total operational risk as a percentage of total bank capital was 0.65%, which is below the minimum permitted figure. This metric allows for the diversification of company risks. This chapter proposes a novel, future-oriented strategy for studying financial crises, expanding the digital transformation research agenda across disciplines. Accordingly, the authors simulated the study object’s operational risk using an optimal approach to measurements (AMA) in accordance with Basel II (Santander Bank). The study’s findings enabled the authors to determine whether or not the value was necessary for Santander Bank to bear in the next years by calculating the overall value of operational risks and evaluating the indicator’s acceptability relative to the bank’s capital.

This paper reports the developments of a project cost control system (PCCS) for construction projects to (1) measure its current level of cost control maturity, (2) examine the relationships between elements within PCCS processes and (3) identify improvement areas.

This study adopts a mixed approach of descriptive analysis and partial least squares structural equation modelling (PLS-SEM) to measure the current maturity level of PCCS and evaluate the relationships between elements within PCSS to identify improvement areas. Further importance-performance matrix analysis (IPMA) of priority constructs was conducted to improve a target construct and identify the most important areas of specific actions at indicator levels. The results of IPMA revealed the contrast that has the greatest importance on the performance of others so that the recommendations can be made accordingly. Data collected in New Zealand were used to develop the research model.

This study develops structural and measurement models with the constructs including pre-control, in-control and post-control processes, enablers and their proposed interrelationships. Then, data from survey of 184 experienced project cost control team members reveal that post-control has the lowest maturity or weakest areas in the PCCS. Data analysis facilitated by PLS-SEM confirmed that all the constructs in the structural model have positive and significant relationships with each other and suggested that systematic cost analysis reports, communication, skills and experience, defining roles and responsibilities, and top management's support should be the highest priority for improving the PCCS in a more effective manner.

This study presents one of the earliest attempts to develop and test an integrated model that links sub-processes in PCCS and their enablers. Secondly, this research adds to the construction project management literature by empirically verifying the roles of enablers in enhancing maturity level of PCCS.