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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.

This study aims to address three research questions pertaining to climate neutrality within the supply chain of metal and mining industry: (1) How can an organization implement practices related to climate neutrality in the supply chain? (2) How do members of the supply chain adopt different measures and essential processes to assist an organization in responding to climate change-related concerns? (3) How can the SAP-LAP framework assist in analyzing and proposing solutions to attain climate neutrality?

To address the proposed research questions concerning climate neutrality, this study employs a case study approach utilizing the SAP-LAP (situation, actor, process–learning, action, performance) framework. Within the SAP-LAP framework, adopting a natural resource-based perspective, the study thoroughly examines the intricacies and interactions among existing situations, pertinent actors and processes that impact climate initiatives within a metal and mining company.

The study's findings suggest that organizations can achieve the objective of climate neutrality by prioritizing resources and capabilities that lead to reduced GHG emissions, lower energy consumption and optimal resource utilization. The study further proposes key elements that significantly influence the pursuit of climate neutrality within enterprises.

This study is one of the earliest contributions to the development of a holistic understanding of climate neutrality in the supply chain of the metal and mining industry.

The study will assist practitioners and policymakers in comprehending the present circumstances, actors and processes involved in enterprises' supply networks in order to attain climate neutrality in supply chains, as well as in taking the right steps to enhance performance.

This study presents a climate neutrality model and provides valuable insights into emission management, contributing to the achievement of the climate neutrality objective.

The accurate valuation of second-hand vessels has become a prominent subject of interest among investors, necessitating regular impairment tests. Previous literature has predominantly concentrated on inferring a vessel's price through parameter estimation but has overlooked the prediction accuracy. With the increasing adoption of machine learning for pricing physical assets, this paper aims to quantify potential factors in a non-parametric manner. Furthermore, it seeks to evaluate whether the devised method can serve as an efficient means of valuation.

This paper proposes a stacking ensemble approach with add-on feedforward neural networks, taking four tree-driven models as base learners. The proposed method is applied to a training dataset collected from public sources. Then, the performance is assessed on the test dataset and compared with a benchmark model, commonly used in previous studies.

The results on the test dataset indicate that the designed method not only outperforms base learners under statistical metrics but also surpasses the benchmark GAM in terms of accuracy. Notably, 73% of the testing points fall within the less-than-10% error range. The designed method can leverage the predictive power of base learners by incrementally adding a small amount of target value through residuals and harnessing feature engineering capability from neural networks.

This paper marks the pioneering use of the stacking ensemble in vessel pricing within the literature. The impressive performance positions it as an efficient desktop valuation tool for market users.

Artificial intelligence (AI) applications are increasingly used for day-to-day operations in healthcare. Each has a relatively limited scope or task, and several find application in managerial and organizational processes. More and more, AI and machine learning (ML) devices have received US FDA approval in the last decade. This chapter covers the main AI applications in healthcare, with a focus on organizational AI solutions (administrative AI), the main AI developers, their investment and real-world data and case studies in healthcare and other sectors. AI can be applied in resource management and procurement, resource allocation, clinical case management, staff work shift scheduling and handling of emergencies. AI applications are becoming ubiquitous in hospital (e.g. emergency room and operating theatre) and outpatient settings (e.g. ambulatory care and dentistry clinics). Their implementation is expected to bring direct benefits for patient care and satisfaction. This chapter gives a broad definition of AI in healthcare settings, with a focus on administrative applications and their use in case study data.

This study aims to examine the perceptions and opinions of relevant stakeholders regarding entrepreneurship opportunities and growth in the Gilgit-Baltistan (GB) province of Pakistan. Specifically, it focuses on the role of special economic zones (SEZs), such as Maqpondass SEZ and the China–Pakistan Economic Corridor (CPEC), in fostering nascent entrepreneurship (NE) and promoting regional development.

The study employs ordered logistic regression to estimate the relationship between various independent variables and nascent entrepreneurship (NE). The independent variables include awareness of CPEC (AAC), awareness of Maqpondass SEZ (AAMEZ), SEZ incentives (SEZInc), regional market competitiveness (RMC), loan availability (LA) and education and experience (EE).

The findings indicate a robust positive relationship between SEZ-based industries and the growth of local small businesses and enterprises in Gilgit-Baltistan. Furthermore, the study suggests that government incentives, access to finance, skill development, relevant knowledge, and connections with local businesses facilitate the establishment of new ventures.

The study underscores the importance of focusing on human capital development, providing financial assistance, and creating incentives for adopting advanced technology to foster the growth of local businesses in Gilgit-Baltistan through SEZs. It emphasizes the need for policymakers and stakeholders to prioritize initiatives that support entrepreneurship and innovation in the region.

This study contributes to the existing literature by providing novel insights into the perceptions of entrepreneurship development in Gilgit-Baltistan, particularly concerning the influence of natural resources and SEZs. It fills a gap in the research by offering valuable implications for policymakers, researchers, and practitioners seeking to promote sustainable economic development in the region.

Regional economic integrations bring many economic benefits to member countries. In international competition, there are integrations that make it easier for countries to reach their goals and attract attention with their effectiveness today. The Eurasian Economic Union (EAEU) is one of the main important regional economic integrations. This chapter details the relations Turkey can establish with the EAEU by considering the import and export data between Turkey and Russia. The research of recent data will discuss how Turkey's trade relations with Russia strengthen the EAEU and what kind of relationship will be possible in the future, considering commercial and economic integrations that are already included, as well as geographical and historical elements. This chapter will assess the potential relationship that can be established with the EAEU, given the strengthening of trade ties between Turkey and Russia. This chapter highlights the alternatives for the harmonization of Turkey's potential cooperation with the EAEU, which is planning a development strategy.

The Farm Service Agency (FSA) conservation loan program was introduced in the 2008 Farm Bill to provide additional credit to assist producers implementing approved Natural Resources Conservation Service (NRCS) conservation projects. This paper explores why this program has been widely underutilized despite an overall increase in United States Department of Agriculture (USDA) Conservation Program participation.

The FSA administrative loan data are merged with NRCS program participation and payments data for 2010–2021. The share of project costs paid by producers and resulting savings achieved by farmers participating in both programs if their cost-share portion was paid by FSA loans are estimated, as well as the impact on farmer conservation spending under different estimates of increased participation.

A significant share of FSA farmers are likely to take advantage of NRCS programs, with the majority of participants paying under $25,000 in cost-share portions. These loans are less suited to guaranteed conservation loans and more appropriate for the discontinued direct conservation loan program. Few FSA borrowers participating in NRCS cost-share programs pay more than $50,000 in cost-share portions. These loans would receive the majority of benefits from interest reduction schemes under the current guaranteed loan program.

Our results and suggestions provide valuable information when discussing the Guaranteed Conservation Loan Program in the 2023 Farm Bill legislation.

No prior research has attempted to merge FSA guaranteed or direct loan data with conservation program participation and payment data, focused on producer cost-share levels or the FSA Guaranteed Conservation Loan Program in the last decade, making this study a valuable contribution to the literature.

The concept of the “global production network” (GPN) has emerged as a framework for analyzing the intricate connections between a dominant or pivotal firm and its suppliers across various countries. ¹ The expansion of GPNs signifies that trade encompasses not only the final products but also the parts and components (P&C) involved in their production. The reduction of tariff barriers and advancements in transportation and communication technology have facilitated the fragmentation of production processes across different countries. This has led to a significant transformation in the nature and structure of global trade. This chapter aims to synthesize and present this literature. By identifying the key drivers, determinants, and consequences of fragmentation trade through a literature-based approach, this study aids in assessing the opportunities and challenges those lagging countries, like India, encounter in terms of increased participation in GPNs.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

The objective of this comparative quantitative study is to examine hypotheses regarding the impact of asset management and intellectual capital (IC) on the financial performance of the construction sector in the ASEAN-5 throughout 2019–2022 with enterprise resource planning (ERP) as a mediator variable.

A final sample of 65 construction industries listed on stock exchanges during 2019 and 2022 in ASEAN-5 (Indonesia, Philippines, Malaysia, Thailand and Singapore) was selected using the purposive sampling method. This research employs market-to-book value (MBV) as a measure of IC. The study also used path analysis, the Sobel test and analysis of variance test (ANOVA).

The findings demonstrate that asset management, intellectual capital and ERP have a significant and favorable impact on return on asset (ROA). Additionally, the asset management and IC of the ERP display weak and substantial outcomes. The implementation of ERP was also shown not to operate as a mediating factor. The ANOVA results on financial performance indicate significant differences for the year 2019. Furthermore, the use of various ERP software types demonstrates a comparable impact on enhancing financial performance.

This is the first study that utilizes ERP to mediate management asset and IC on financial performance. The ASEAN-5 construction sector is representative of developing nations' economic condition, offering the possibility to avoid the global economic collapse in 2023 and recover despite economic volatility.