A decision support methodology to enhance the competitiveness of the Turkish automotive industry

This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2013 Elsevier B.V. All rights reserved.Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum’s competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey

Modeling the Process of Financing Small Organizations

Most small business organizations are constantly lacking financial resources. This situation is associated with a small amount of own funds and the difficulty of raising borrowed funds. In the prevailing conditions, the need for well-founded financing management, this ensures the stable financial condition of a small organization, increases. Modeling the process of financing an organization allows you to consider different financing options, to influence the most significant factors in a particular situation. It is proposed to use the cognitive approach for dynamic management of the financing process and the choice of a rational variant based on the logit model. Determination of the degree of influence of various factors is proposed to be determined using correlation-regression modeling. With the help of the proposed modeling of the financing process of a small organization, it is possible to forecast positive changes in financial performance

Appropriate Wisdom, Technology, and Management toward Environmental Sustainability for Development

The protection and maintenance of environmental resources for future generations require responsible interaction between humans and the environment in order to avoid wasting natural resources. According to an ancient Native American proverb, “We do not inherit the Earth from our ancestors; we borrow it from our children.” This indigenous wisdom has the potential to play a significant role in defining environmental sustainability. Recent technological advances could sustain humankind and allow for comfortable living. However, not all of these advancements have the potential to protect the environment for future generations. Developing societies and maintaining the sustainability of the ecosystem require appropriate wisdom, technology, and management collaboration. This book is a collection of 19 important articles (15 research articles, 3 review papers, and 1 editorial) that were published in the Special Issue of the journal Sustainability entitled “Appropriate Wisdom, Technology, and Management toward Environmental Sustainability for Development” during 2021-2022.addresses the policymakers and decision-makers who are willing to develop societies that practice environmental sustainability, by collecting the most recent contributions on the appropriate wisdom, technology, and management regarding the different aspects of a community that can retain environmental sustainability

Exploring the Significant Cash Flow Factors Influencing Building Projects Profitability in Ghana

This paper identifies and explore significant quantifiable cash flow factors influencing building projects profitability in Ghana. A thorough literature was undertaken to unravel the quantifiable cash flow factors which facilitated design of questionnaire. A survey with prime focus on large firms registered with the Association of Building and Civil Engineering Contractors, Ghana was undertaken. A total of 50 questionnaires were received from 63 administered representing 79.36% response rate with a Cronbach Alpha value of 0.895 and Kappa value of 0.743 respectively were attained. One-sample t-test was performed on the rated responses to establish 12 significant factors. Principal component analysis was subsequently employed to reduce factors to the most significant components. Prominent variables selected from rotated and component score matrixes were: wages of labour and staff; progress payment duration; bank interest rate; and replacement of defective works as significant variables. This study was limited to quantifiable cash flow factors and large construction firms hence, recommended further study with focus on qualitative factors, procurement types, broader scope of construction firms and other developing countries. The outcome of this is to aid construction managers effectively manage the significant cash flow factors to maximize profit

Multiple criteria decision analytical tools in assessing risk for green growth: the case of oil palm biomass in Malaysia

The heightening world issues arises from climate change and energy security has created a strong resonance for sustainable development. The utilisation of biomass resources is amongst one of the best strategies to counter carbon emission and energy security issues for waste-to-wealth. Over the last decade, the Malaysian government has shown its clear intent to be a front-runner in the green economy through its various green economy policies and programs, particularly focus on oil palm biomass industry. However, it is observed that the diffusion rate of the industry remains relatively slow as compared to other developing countries such as Thailand and Philippine. Literature, anecdotal evidence, and advocates as well as businesses have identified that one of the non-technical factors that contribute to this problem is financing difficulties. The complication of biomass value chain creation often engaged with high risk profile, capital intensive and long payback period which is unfavourable for financing based on conventional risk assessment. Thus, this research focusses on developing a full range risk assessment model in aiding the industry stakeholder to comprehend the risk profile in managing and mitigating risk in biomass value chain in Malaysia. Multiple decision analytical tools have been employed and developed to integrate non-quantitative factors in risk assessment and design risk mitigation strategy based on the strengths and preferences of different stakeholders’ role. The outputs can serve as policy recommendation to aid the authorities and policy makers to undertake policy reviews to effectively spur the biomass industry for green growth. Furthermore, financier and investor are recommended to utilise the information to enhance its financing decision, to offer financial products that customised the need of sustainable projects without losing great business opportunity. Last but not least, the framework also offers industry stakeholders a practical decision analysis and making tool to integrate preferences as well as quantitative information to mitigate risks before any losses in venturing into the biomass industry occurred

Superstructure optimization and forecasting of decentralized energy generation based on palm oil biomass

Malaysia realizes the importance of addressing the concern of energy security to accomplish the nation’s policy objectives by mitigating the issues of security, energy efficiency and environmental impacts. To meet the rising demand for energy and incorporation of Green Technology in the national policy, Malaysian government during the last three decades has developed several strategies and policies. National Green Technology Policy was an initiative, which marked the firm determination of the government to incorporate Green Technology in the nation’s economy policy. Malaysia has abundant biomass resources, especially oil palm residues with power generation potential of about 2400 MW, which is promising for decentralized electricity generation (DEG). The aim of this study is to determine the best location to install appropriate biomass electricity generation plant in Johor and forecasting the electricity market (i.e. electricity demand) in order to provide a strategic assessment of measures for the local energy planners of Malaysia, as an optimization bottom-up model. A superstructure was developed and optimized to represent DEG system. The problem was formulated as Mixed Integer Nonlinear Programming (MINLP) and implemented in General Algebraic Modeling System (GAMS). Electricity demand was modeled using Adaptive Neuro Fuzzy Inference System (ANFIS). Based on GAMS and ANFIS models, palm oil biomass based DEG system and distribution network scenarios for current as well as next ten, twenty and thirty years have been proposed for State of Johor, Malaysia. Biomass from sixty six Palm Oil Mills (POMs) would be collected and transported to eight selected locations. Empirical findings of this study suggested that total production cost is minimized by placing biomass gasification based integrated combine cycle (BIGCC) power plant of 50MW at all eight locations. For 2020 Scenario, no additional infrastructure will be required. For 2030 Scenario, additional units of BIGCC of 50MW will be required at five out of eight locations. While for 2040 Scenario, again no additional infrastructure development will be needed. Total minimum cost varied from 6.31 M$/yr for current scenario to 22.63 M$/yr for 2040 scenario

Exchange rate misalignments in ASEAN-5 countries

The purpose of this paper is to estimate the exchange rate misalignments for Indonesia, Malaysia, Philippines, Singapore and Thailand before the currency crisis. By employing the sticky-price monetary exchange rate model in the environment of vector error-correction, the results indicate that the Indonesia rupiah, Malaysian ringgit, Philippines peso and Singapore dollar were overvalued before the currency crisis while Thai baht was undervalued on the eve of the crisis. However, they suffered modest misalignment. Therefore, little evidence of exchange misalignment is found to exist in 1997:2. In particular, Indonesia rupiah, Malaysia ringgit, Philippines peso and Singapore dollar were only overvalued about 1 to 4 percent against US dollar while the Thai baht was only 2 percent undervalued against US dollar