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The purpose of this paper is to develop a tool design for assembly and disassembly using rating factors. Design engineers need an automated tool to effectively analyze the ease of assembly and disassembly of the products or subassemblies. A good assembly design helps in easier disassembly and thus makes it easier to service, repair and maintain. Reuse and recycling aspects are given importance in the present days due to environmental regulations. Designers now use the life cycle design of the products. This creates an environment for the successful application of design for manufacturing, assembly and disassembly tools. This paper addresses some of those issues.

The analysis of a product design for ease of assembly/disassembly depends largely on whether the product is to be assembled/disassembled manually, with automation or a combination of these. For example, the criteria for ease of automatic feeding and orienting are much more stringent than those for manual handling of parts. The new design for assembly/disassembly (DFA/DFD) evaluation tool explained here enables the designer to review the existing design. This paper examines the existing techniques in the area of DFA/DFD and suggests a new methodology based on rating factors. Excel is used to create the interface for the user. Other popular methods were examined such as Boothroyd-Dewhurst, Lucas. Access, reuse, removal, tool, task and time method and assembly score method (Poli) were used as a base for this study.

The end result of this research is a new approach linked to assembly/disassembly rating score.

The new DFA/DFD evaluation tool enables the designer to review the existing DFA and DFD difficulties.

This research was conducted to study benefits of a concept, known as lean manufacturing, which elicits organizations to do more with less effort, time, space, and equipment by eliminating waste. Because lean implementation involves finances, dedication, and cultural change in business, there is a need to assess a company's lean initiative.

A comprehensive lean assessment tool is designed targeted for use by manufacturing companies. Research of lean principles and practices was conducted through lean organizations, trade publications, manufacturing web sites, professional articles, textbooks, lean exhibitions, and non‐profit lean organizations.

This led to the development of a lean survey questionnaire delivered to 143 lean companies and lean consultants for authentic input. Based on the response, survey participation met a goal of 95 percent accuracy with 15 percent error. Completed surveys were compiled and analyzed for fundamental practices of lean organizations. Answers to the survey were assigned numerical values based on lean significance, and a lean assessment model was developed based on Excel. The lean assessment model was fine‐tuned and programmed to output a numerical and descriptive lean assessment grade. It was tested for soundness by inputting mock lean and traditional company answers into the model. Output results from the model correlated with the input.

Lean thinking philosophy and lean manufacturing methods and principles were exhaustively researched to make a successful lean organization. The research led to the development of a visual indicator, the Lean Thinking Management Wheel. This model to assess lean thinking manufacturing initiatives unique and has a great potential to use industry wide.

This paper aims to propose the use of net present value methods to derive the quality costs of quality improvement projects. The proposed methodology is then demonstrated in a case study of a quality improvement to an inspection process in an automobile assembly plant.

The approach takes the form of application of accounting net present value methods to the cost of quality methods.

Quality improvements in the manufacture of durable goods do not usually have instantaneous results in warranty cost reductions, customer satisfaction or revenue expansion. The net present value method proposed gives a more accurate accounting of the expected results of quality improvement projects by considering the temporal effects of the change and the time value of money.

The case study presented contains fictitious data to protect the confidentiality of the source. While it is useful in demonstrating the application of the net present value method, it should not be used as an indication of actual costs of plant operations.

The paper provides a unique approach to cost of quality analysis that is particularly useful in the assessment of quality improvement projects for durable goods. While much research is focused on cost of quality methods and philosophies, only a little provides the level of detail in the actual application of the methods found in the case study.

This paper focuses on assembly line performance of an automotive body shop that builds body‐in‐white (BIW) assembly utilizing about 700+ process robots. These robots perform various operations such as welding, sealing, part handling, stud welding and inspection. There is no accurate tool available for the plant personnel to predict the future throughput based on plant's data. The purpose of this paper is to provide future throughput performance prediction based on plant data using Box‐Jenkins' ARMA model.

The following data were collected for five major assembly lines. First, the assembly machine‐in‐cycle time: the assembly line machines include robots that perform various functions like load, welding or sealing and unloading parts; the manual operators loading cycle time to the production fixtures. The conveyors act as buffers in between stations, and also feed to the production cells, and carry parts from station to station. The conveyors' downtime and uptime were also part of the machine‐in‐cycle time; second, the number of units produced from the beginning to the end of the assembly line; third, the number of fault occurrences in the assembly line due to various machine breakdowns; fourth, the machine availability percentage – i.e. the machine is readily available to perform its functions (the machine blocked upstream (starving) and blocked down (downstream) state is considered here); fifth, the actual efficiency of the machine measured in percentage based on output percentage; sixth, the expected number of units at designed efficiency.

In summary, this research paper provided a systematic development of a forecast model based on Box‐Jenkin's ARMA methodology to analyze the complex assembly line process performance data. The developed ARMA forecast models proved that the future prediction can be accurately predicted based on the past plant performance data. The developed ARMA forecast models predicted the future throughput performance within 99.52 percent accuracy. The research findings were validated by the actual plant performance data.

In this study, the automotive assembly process machines (robots, conveyors and fixtures) production data were collected, statistically analyzed and verified for viable ARMA model verification. The verified ARMA model has been used to predict the plant future months' throughput with 99.52 percent accuracy, based on the plant production data. This research is unique because of its practical usage to improve production.

This study aims to compare the safe-haven properties of different asset markets such as gold, dollar, oil and disaggregated real estate sector (house, plot and residential) against equity returns in Pakistan over the monthly period of January 2011–December 2020.

The authors use wavelet coherence to encapsulate the overall dependence and correlation of asset classes. Further, the authors also study the potential of diversification at the tail of returns distribution by applying the wavelet value-at-risk (VaR) framework.

The results of wavelet coherence show that the dependence is weaker (stronger) in the short (long)-term investment horizon. Moreover, the findings of wavelet VaR reveal that the degree of co-movement between gold and equity returns greatly affects the portfolio risk followed by residential property and oil.

The findings are beneficial for the individual investor, fund managers and financial advisors looking for the optimal portfolio combination that hedges the excessive negative movements in equity returns subject to the heterogeneity in the investment horizon.

This is a primary effort to estimate safe-haven investments opportunities at a large spectrum, including disaggregated real estate sector against stock returns in Pakistan. Moreover, this study uses wavelet coherence and wavelet VaR which have an advantage over traditional analysis for diversification.

Governance quality has been a dominant factor to formulate policies for the development of financial institutions in the world. Therefore, this study aims to explore the impact of governance quality on financial institutions along with globalization in the case of Pakistan.

Time series data from 1996 to 2018 are considered for analysis. The NG-Perron is applied to check the order of integration. In addition, Kim and Perron (2009) structural break unit root test is used to identify break years. The autoregressive distributive lags (ARDL) bound testing approach is used to detect the long-run association among governance quality, financial institutions and globalization.

The results of unit root analysis show that all series are stationary at a different level of integration, I(0)/I(1). However, the long-run association is detected in the presence of break years. The authors find a positive impact of governance quality to determine financial institutions in the long-short-run. Similarly, globalization also enhances financial institutions but only in long run.

This study fills the gap in the economic literature by exploring the linkages between the financial institution and disaggregated governance indicators in the case of Pakistan. Moreover, a role of structural break is also captured during analysis. This study also opens some new insights for policymaking.

This study addresses the prevailing complexities and limitations in estimating and managing construction overhead costs (COCs) in the existing literature, with the purpose of enhancing the accuracy of cost performance indicators in construction project management.

An innovative approach is proposed, employing the activity-based costing (ABC) accounting method combined with building information modelling (BIM) to assign real overhead costs to project activities. This study, distinguished by its incorporation of a real case study, focuses on an administrative building with a four-story concrete structure. It establishes an automated method for evaluating project cost performance through the detailed analysis of earned value management (EVM) cost indicators derived from ABC results and BIM data.

The results show that the ABC integration improves the accuracy of cost performance indicators by over 9%, revealing the project's true cost index for the first time and demonstrating the substantial value of the approach in construction engineering and management.

The current study highlights a notable gap in the existing literature, addressing the challenges in onsite overhead cost estimation and offering a solution that incorporates the state-of-the-art techniques.

The proposed method has significant implications for project managers and practitioners, enabling better-informed decisions based on precise cost data, ultimately leading to enhanced project outcomes.

This research uniquely combines ABC and BIM, presenting a pioneering solution for the accurate estimation and management of COCs in construction projects, adding significant value to the current body of knowledge in this field.

Resilient hospitals have the vital role in reducing mortality, severity of injuries by providing required emergency services during accidents and disasters. This study aims to identify and prioritize key indicators on hospital resilience.

This cross-sectional study was conducted in 2019. The draft of the indicators obtained from the systematic review of the previous study was finalized, with three expert panel sessions and 14 experts in resilience fields. The outputs of these sessions were divided into three domains including constructive resilience, infrastructural resilience and administrative resilience, 17 sub-domains and 71 indicators. Then fuzzy analytic network process method was used to weight and prioritize the final indicators of hospital disaster resilience.

Administrative resilience, logistic and financial management and strategic outsourcing agreement allocated the highest weight as domain, sub-domains and indicators, respectively. The weight of each sub-domain and indicator was also determined.

Investigating the weight of domains, sub-domains and indicators shows the importance of managerial and operational issues in hospital resilience. By using the indicators and relative weights, a tool for measuring hospital disaster resilience can be created in further studies. The output of these assessments is effective in promoting safety and increasing awareness of hospital managers and health policymakers.

This research aims to inspect the existence of the “environmental Kuznets curve” (EKC) in the presence of foreign direct investment (FDI), financial development (FD) and urbanization throughout 1972–2018 for Pakistan.

For time series analysis, Phillips and Perron (PP) and Augmented Dickey–Fuller (ADF) unit root tests are used to confirm the level of integration. For robustness, Kim and Perron (2009)’s structural break unit root test is employed, which identifies the order of integration in the presence of structural break years. Further, combined cointegration analysis is performed to confirm the existence of a long-run association between underlying variables. Furthermore, autoregressive distributed lag (ARDL) analysis is employed for the robustness of the cointegration approach.

The cointegration analysis confirms the existence of a long-run association among variables. The authors find a positive and significant impact of urbanization, FD and foreign development on environmental degradation in the long run. Similarly, only FDI increases environmental degradation in the short run. In addition, the authors find an inverted U-shape relationship between economic growth and environmental quality which, further, confirms the presence of EKC in Pakistan.

This research contributes to applied economics in many ways: the combined effect of urbanization, FD, FDI and economic growth on carbon dioxide (CO₂) emission is checked simultaneously. To avoid ambiguity, this study constructs the FD index through the principal component analysis (PCA). Moreover, the role of structural breaks has been considered through the analysis. Novel Bayer-Hanck combined cointegration analysis is employed to detect the existence of long-run relationships among underlying variables.

The purpose of this paper is to measure the unidimensional and multidimensional inequality in the case of Pakistan and compare their results at the provincial as well as regional (urban and rural areas) level. The authors collected data from Pakistan Social and Living Standard Measurement and Household Integrated Economic Survey for fiscal years of 1998–1999 and 2013–2014.

The authors used Gini coefficient for unidimensional inequality and multidimensional indexing approach of Araar (2009) for multidimensional inequality.

The findings predicted that unidimensional inequality is relatively high in the urban area due to uneven dissemination of income, but multidimensional inequality is quite high in rural areas because of higher disparities among all dimensions. At the provincial level, Punjab has relatively high-income inequality followed by Sindh, KPK and Baluchistan.

This study is a pioneering effort to compare two time periods to explore unidimensional and multidimensional inequality in all provinces of Pakistan and their representative rural-urban regions by applying Araar and Duclos’s (2009) approach. Further, this study opens some new insights for policy makers.