RSS 1.0Business and management
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Designing an organization for innovation in emerging economies: the mediating role of readiness for innovationThe study proposes an organizational design framework that impacts innovation in corporate firms. In an emerging economy like Oman, innovation helps to reduce the dependence on oil revenues and enhance its international competitiveness. However, the corporate organizations in emerging economies are unable to innovate effectively because they are not designed for innovation. Further, scarcity of resources undermines their readiness for innovation. This study empirically validates measures of an entrepreneurial organizational design framework in Omani corporate sector. In order to explain how a corporate organizational design promotes innovation and clarify the missing links between corporate entrepreneurial activity and innovation, the mediating role of readiness for innovation (RFI) is tested. Using a quantitative research approach, data is collected from 401 corporate firms in Oman and analysed using structural equation modelling. The findings support the proposition that entrepreneurial organizational design promotes both radical and incremental innovation degree and frequency, while RFI partially mediates the relationship between entrepreneurial inputs and innovation outputs. The study contributes to the understanding of innovation in emerging economies as it explains that RFI helps firms to enhance its innovation potential by optimizing its resources, capabilities and processes for innovation. These measures are essential for organizations, particularly in emerging economies focused on low cost innovation. The findings of the study will inform managerial decision-making in terms of designing organizations for innovation and implementation of measures related to readiness for innovation. PDF
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Board gender diversity and organizational determinants: empirical evidence from a major developing countryThis article seeks to identify and analyze the organizational determinants of women presence on Indian corporate boards. Using a sample set of 294 Indian firms between years 2004–2014, Tobit regression analysis indicates that firm size, family ownership and affiliation with the high-tech sector exhibit positive association with the number of female directors on corporate boards. Further, we do not find any significant impact of state-ownership on the number of women on those boards. Notably, the effects of the organizational variables are more pronounced for the proportion of female non-executive directors, as compared to female executive directors. We conclude that understanding the organizational characteristics in conjunction with business environment can provide useful insights into state of board gender diversity, particularly in developing countries.
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Water footprint assessment for coal-to-gas in ChinaTo increase its domestic gas production and achieve cleaner end-use utilization of its coal resources, China is actively promoting its coal-to-gas (CTG) industry. However, one of the major concerns for CTG development is the consequent significant water usage. To better understand this aspect, this paper presents a quantitative assessment of the water footprint (WF) for China’s CTG industry. The results show that the WF of CTG in China is typically in the region of 0.055 m3 water per cubic meter of produced gas. In addition, the analysis of the components of this WF indicates that most of the water resources are used both in the process of CTG production itself, and also in the dilute discharge of pollutants. In terms of the planned production capacity of China’s CTG projects, this paper finds that the water use in some regions of Xinjiang, Inner Mongolia, Shanxi and Liaoning may account 30–40% of regional water resources, which means the large-scale development of CTG projects may present significant risks to regional water resources. Therefore, this paper suggests that the status of regional water availability should be one of the key factors considered by policy makers in order to achieve sustainable development of the country’s CTG industry.
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The role of government and the international competitiveness of SMEs Evidence from Ghanaian non-traditional exportsPurpose The nature of international markets and the challenges with respect to the competitiveness of small- and medium-sized enterprises (SMEs) makes it imperative to examine government support. This study aims to assess the role and effectiveness of government and the export promotion agencies in supporting exports by non-traditional horticultural SMEs in Ghana. Design/methodology/approach The study used a qualitative research design, which involved semi-structured interviews with senior managers of six export facilitating institutions to gain an understanding of the services offered to SMEs with respect to exports of non-traditional horticultural products. Findings The findings reveal inadequate cost-efficient sources of non-traditional horticultural export financing for SMEs. This is a hindrance to the international competitiveness of exporting SMEs in developing countries such as Ghana. In addition, effective and coordinated support from export promotion agencies was found to be critical. Originality/value The study highlights the importance of government's role in policymaking and implementation of export-led programmes for horticultural exporting firms in Ghana. Despite their strategic importance, this area of research has not attracted the attention of researchers, with little or no information on the horticultural international competitiveness of non-traditional horticultural products.
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Modelling and analysis of intermodal food grain transportation under hub disruption towards sustainabilityEscalating global food security concerns across several nations has shifted the focus of policy makers towards risk adaptive sustainable food grain operations. This paper builds a sustainable food grain transportation model for intermodal transportation operations between two Indian states, in the presence of hub disruption. A hub and spoke system is used to connect origin and destination warehouses through intermodal hubs in a multi-layered network. The problem is formulated as a multi-period mixed integer nonlinear single objective optimization problem considering minimization of transportation, hub location, rerouting, environmental and social costs with near optimal shipment quantities and hub allocations as the prime decisions. The proposed MINLP is solved using Particle Swarm Optimization with Differential Evolution (PSODE), a superior metaheuristic to deal with NP-hard problems. Convergence graphs and global optimal costs are reported for small, medium and large size instances consisting of 1824, 9768 and 28848 variables respectively, inspired from food grain industry in the southern part of India. Pareto plots are generated to capture the complementarity between economical and socio-environmental cost categories for all instances. The effect of hub location, hub disruption, cost consolidation and vehicle resource availability factors on individual and total costs is studied through sensitivity analysis. Results indicate that food grain demand is fulfilled with 14% increase in the mean total cost for single hub disruption case and with 40% increase for multiple hub disruption. Finally, managerial implications provide specific factor level recommendations for different strategic objectives.
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Environmentally conscious logistics planning for food grain industry considering wastages employing multi objective hybrid particle swarm optimizationThis paper develops a hub and spoke network based multi-objective green transportation model for evaluating optimal shipment quantity, modal choice, route selection, hub location, and vehicle velocity decisions considering wastages in Indian food grain context. A hybrid version of multi-objective meta-heuristic, Multi-Objective Particle Swarm Optimization with Differential Evolution (MOPSODE) is proposed to tackle the resulting non-linear formulation. Benchmarking with NSGA-II confirms the dominance of MOPSODE over NSGAII pertaining to near optimal pareto fronts obtained for the tested cases. Finally, the study derives the economic and environmental impact of varying hub location level, food grain wastage threshold and intermodal hub capacity.
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Can environmental investments benefit environmental performance? the moderating roles of institutional environment and foreign direct investmentContribution of environmental investments (EI) to environmental performance (EP) is a lively topic for environmental researchers across the world. In spite of huge amount of research, there is still lack of clarity on the moderating factors that affect the role played by EI. In this study, we distinguish EI into pollution control investments (PCI) and pollution prevention investments (PPI). We further investigate whether institutional environment and foreign direct investment (FDI) can play their moderating effects both on the relationship between EI and EP and on the relationships between different types of investments and EP or not. The results indicate that EI has a positive effect on EP. More specifically, PPI plays a stronger positive role in EP, but PCI does not have a significant effect on EP. In addition, both institutional environment and FDI can strengthen the positive impact of EI on EP. The increase of EI in regions with better institutional environment or high FDI can lead to greater improvement in EP. These moderating effects of institutional environment and FDI are also confirmed on the link between PPI and EP. In summary, our results reinforce the existing views that EI, and specifically PPI, can improve EP, but further contribute to the understanding of the positive moderating roles played by the institutional environment and FDI on the link between EI and EP.
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Opening the black box: the impacts of environmental regulations on technological innovationWhether environmental regulations (ERs) can stimulate technological innovation (TI) is the key for realizing the win-win strategy between economic development and environmental protection. This study seeks to analyze the impacts of ERs on TI. Though previous literature has highlighted that the black box of TI can be decomposed into technology investment and technology transformation, further empirical studies on such a decomposition has largely been ignored. Moreover, a detailed discussion of the links between ER and the decomposed components of TI has not been conducted in developing countries such as China. Our study attempts to address these research gaps by (i) decomposing TI using a novel DEA procedure and to further analyze the impacts of ERs on the decomposed components of TI, and (ii) apply this novel methodology to Chinese context. Accordingly, this study is conducted in two stages. First, a novel application of the slack-based Network DEA model is developed to uncover the black box of TI using Chinese data; to estimate the overall efficiency of technological innovation (TIE) and decompose it into the efficiency of technology investment (TVE) and the efficiency of technology transformation (TTE). Second, a random effect Tobit model is applied to (i) investigate both the linear and non-linear impacts of ERs on TIE in all sectors, and (ii) examine whether the impacts of ERs on TVE and TTE in different sub-processes are heterogeneous or not. Our results have brought out the benefits of decomposing TI; while technology transformation in China closely follows the trend of TI, the trend of technology investment is somewhat different. The estimation results further indicate that the impacts of ERs on TIE are non-linear. Besides, ERs have heterogeneous impacts on the decomposed components of TI. The impacts of ERs on TVE are non-linear, whereas the impacts of ERs on TTE become insignificant.
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The availability of critical minerals for China’s renewable energy development: an analysis of physical supplyIn the context of depletion of fossil energy and environmental impacts of its use, society has begun to develop vigorously renewable energy (RE). As a result, concerns about the availability of critical minerals used in RE systems have been raised. This paper uses a generalized Weng model to analyze the long-term production of critical minerals for China’s RE development. In our pessimistic case, the results show that the production of most of the minerals investigated for China will peak before 2030, with a relatively high decline rate thereafter. This is an unsustainable situation for China’s RE development unless large and growing quantities of these minerals can be imported. In our optimistic case, although this delays the peak date only slightly, it significantly increases the maximum production rate and lowers the subsequent decline rate. The impacts of many other factors on production, and the implications of China’s domestic minerals production on world’s minerals supply chain, are also analyzed. We conclude that both China and the world should pay close attention to the potential supply risks to critical minerals. Possible measures in response are suggested for both China and the world.
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Automatic recognition methods of fish feeding behavior in aquaculture: a reviewFeeding is a major factor that determines the production costs and water quality of aquaculture. Analysis of fish feeding behavior forms an important part of the feeding optimization. Fish feeding has generally been performed with automatic feeding machines which can lead to excessive or insufficient feeding. Recognition of fish feeding behavior can provide valuable input for optimizing feeding quantity. Due to the complexity of the environment and the uncertainty of fish behavior, the correlation and accuracy of behavior recognition are generally low. The accurate identification of fish feeding behavior till faces substantial challenges. This paper reviews the technical methods that have been used to identify fish feeding behavior in aquaculture over the past 30 years. The advantages and disadvantages of each method under different experimental conditions and applications are analyzed. Many methods are effective at evaluating and quantifying fish feeding intensity, but the recognition accuracy still needs further improvement. It is proposed by this paper that technologies such as data fusion and deep learning has great potential for improving the recognition of fish feeding behavior.
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Modelling world natural gas productionAs the cleanest fossil fuel in terms of carbon dioxide (CO2) emissions, natural gas demand is expected to increase rapidly in future due to its important role in the transition of the world energy system. In this case, understanding potential limits to future production of the world’s natural gas resources becomes increasingly important. This paper uses a modified multi-cycle generalized Weng model to forecast the long-term production of natural gas by region, and also globally. Both conventional and unconventional gas production are considered. Our results show that world natural gas production is likely to peak in the range 3.7 to 6.1 trillion cubic meters per year (tcm/y) between 2019 and 2060 depending on assumptions made on the size of the global ultimately recoverable resource (URR) of natural gas. A comparison of this paper’s forecasts with those from the scientific literature and from major energy institutes shows that the projection in this paper’s ‘high scenario’ can be seen as a likely upper-bound on future global natural gas production. To turn this upper-bound projection into reality, great efforts will be needed from the gas industry to discover more conventional and unconventional gas resources, and to make these recoverable.
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Factors motivating Indian manufacturing SME employers’ to adopt GSCM practicesThe growth of manufacturing SMEs is vital, as their contribution towards the national economy is significant. In this era of globalisation, SMEs are compelled to ensure sustainable profitability through cost saving, while being environmentally conscious at the same time. It has been reported in the past empirical studies, that adoption of green supply chain management (GSCM) practices by SMEs could enable such enterprises to improve their performance and succeed in their operations. Hence, to gain and maintain competitive advantage and succeed, SMEs need to change their practices and adapt their strategies to the dynamic environment of today. The factors motivating adoption of GSCM among Indian SMEs have not been thoroughly explored in the past studies. This sets the motivation for the present research. Thus, the purpose of this paper is to explore the factors motivating Indian manufacturing SME employers in adopting GSCM practices in their firms and to develop a GSCM framework based on the literature review and the empirical findings of this study.
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Recent advances in sensor fault diagnosis: a reviewAs an essential component of data acquisition systems, sensors have been widely used, especially in industrial and agricultural sectors. However, sensors are also prone to faults due to their harsh working environment. Therefore, the early identification of sensor faults is critical for making corrective actions to mitigate the impact. This paper provides a comprehensive review on the contemporary fault diagnosis techniques and helps researchers and practitioners to understand the current state of the art development in this emerging field. The paper introduces the common fault types and causes in sensors, and different types’ methods for fault diagnosis used in industry and agriculture sectors. It discusses the advantages and disadvantages of these methods, highlights the current challenges, and offers recommendations for future research directions.
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Best practices in the cost engineering of through-life engineering services in Life Cycle Costing (LCC) and Design To Cost (DTC)This chapter defines a number of Cost Engineering challenges from industry and their potential best practice solutions as industry case studies and industry practices surveys completed during the previous 5 years. In particular Life Cycle Costing in the context of upgrade and revamp in the process industry and also an example of design for full life cycle target cost for the manufacturing industry. Life Cycle Costing of complex long life cycle facilities is exemplified by identification and development of a life cycle costing of oil refineries through a survey of 15 companies and full life cycle experts and a review of the literature. Life cycle costing practices and a standardised life cycle cost breakdown structure are identified. Design to full life cycle target cost practices have been identified in the development of a full life cycle cost estimating tool for marine radar systems. In particular a survey of 17 companies and a case study with a marine radar systems company has identified specific practices useful in developing products to full life cycle target cost. In planning for future Through Life Engineering Services it is proposed that the collection of cost data and the understanding of Cost Engineering practices is a potential competitive advantage.
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Uncertainty of Net Present Value calculations and the impact on applying integrated maintenance approaches to the UK rail industryThe Public performance indicator (PPI) is an important Key Performance Indicator for Network Rail and monitored carefully by the organisation and their external stakeholders. Condition monitoring is of increasing interest within network rail as a suitable method for increasing asset reliability and improving the PPI metric. As condition monitoring methods are identified each will need assessment to establish the cost and benefit. Benefit can be measured in cost savings as poor PPI performance results in fines. Within many industries Net Present Value (NPV) calculations are used to determine how quickly investments will break-even. Cost-risk is a term that is used to describe the financial impact of an unexpected event (a risk). This paper outlines a more detailed approach to calculating NPV which considers the cost-risk effect of changes of the denial of service charging rate. NPV prediction is of importance when assessing when to deploy different fault detection strategies to maintenance issues, and therefore the cost-risk of the NPV calculation should be used to support asset management decisions.
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Performance of supply chain collaboration - a simulation studyIn the past few decades several supply chain management initiatives such as Vendor Managed Inventory, Continuous Replenishment and Collaborative Planning Forecasting and Replenishment (CPFR) have been proposed in literature to improve the performance of supply chains. But, identifying the benefits of collaboration is still a big challenge for many supply chains. Confusion around the optimum number of partners, investment in collaboration and duration of partnership are some of the barriers of healthy collaborative arrangements. To evolve competitive supply chain collaboration (SCC), all SC processes need to be assessed from time to time for evaluating the performance. In a growing field, performance measurement is highly indispensable in order to make continuous improvement; in a new field, it is equally important to check the performance to test conduciveness of SCC. In this research, collaborative performance measurement will act as a testing tool to identify conducive environment to collaborate, by the way of pinpointing areas requiring improvements before initializing collaboration. We use actual industrial data and simulation to help managerial decision-making on the number of collaborating partners, the level of investments and the involvement in supply chain processes. This approach will help the supply chains to obtain maximum benefit of collaborative relationships. The use of simulation for understanding the performance of SCC is relatively a new approach and this can be used by companies that are interested in collaboration without having to invest a huge sum of money in establishing the actual collaboration. © 2013 Elsevier Ltd. All rights reserved.
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Cost drivers of integrated maintenance in high-value systemsHigh value systems are determined by a wide structure, where operations are considered to be one structural component. Nowadays “downtime” as a major impact in the operation costs of any system. To avoid or minimize “down-time” it is essential to match the appropriate maintenance to each failure. Therefore, it is relevant to determine the cost drivers of integrated maintenance in any system, in order to minimize the overall cost. It is common to use Value Driven Maintenance (VDM) to capture the cost drivers in maintenance. VDM is a methodology which relies in four distinct areas: Asset Utilization; Resource Allocation; Control Cost and Health and Safety and Environment. Within each category it is possible to allocate different cost drivers, building a framework for each system studied. The aim of this paper is to categorize the cost drivers of rail infrastructure networks, associating them with the maintenance preformed for each case. Furthermore, analysis of which part of the track falls under each VDM category as well as the general failure causes and effects will be included in the framework presented. Finally relating the maintenance type for each effect will provide the necessary inputs towards a cost model structure. The benefit of achieving a successful model will be the optimization of the cost in integrated maintenance of the rail infrastructure.
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COTECHMO: The Constructive Technology Development Cost ModelA detailed analysis of the available literature and the aerospace manufacturing industry has identified a lack of cost estimation techniques to forecast advanced manufacturing technology development effort and hardware cost. To respond, this article presents two parametric ‘Constructive Technology Development Cost Models’ (COTECHMO). The COTECHMO Resources model is the first and is capable of forecasting aerospace advanced manufacturing technology development effort in personhours. When statistically analyzed, this model had an outstanding R-squared value of 98% and a high F-value of 106.65, validating model significance. The general model accuracy was tested with 53% of the forecast data falling within 20% of the actual. The second, the COTECHMO Direct Cost model is capable of forecasting the development cost of the aerospace advanced manufacturing technology process hardware. This model had an inferior R-squared value of 76% and an F-value of 5.59, although each was still valid to determine model significance. However, the Direct Cost model accuracy exceeded the Resources model, with 93% of the forecast data falling within 20% of the actual. The article concludes with recommendations for future research, including suggestions for further enhancement of each model verification and validation, within and outside of the supporting organization.
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Integration of cost-risk assessment of denial of service within an intelligent maintenance systemAs organisations become richer in data the function of asset management will have to increasingly use intelligent systems to control condition monitoring systems and organise maintenance. In the future the UK rail industry is anticipating having to optimize capacity by running trains closer to each other. In this situation maintenance becomes extremely problematic as within such a high-performance network a relatively minor fault will impact more trains and passengers; such denial of service causes reputational damage for the industry and causes fines to be levied against the infrastructure owner, Network Rail. Intelligent systems used to control condition monitoring systems will need to optimize for several factors; optimization for minimizing denial of service will be one such factor. With schedules anticipated to be increasingly complicated detailed estimation methods will be extremely difficult to implement. Cost prediction of maintenance activities tend to be expert driven and require extensive details, making automation of such an activity difficult. Therefore a stochastic process will be needed to approach the problem of predicting the denial of service arising from any required maintenance. Good uncertainty modelling will help to increase the confidence of estimates. This paper seeks to detail the challenges that the UK Railway industry face with regards to cost modelling of maintenance activities and outline an example of a suitable cost model for quantifying cost uncertainty. The proposed uncertainty quantification is based on historical cost data and interpretation of its statistical distributions. These estimates are then integrated in a cost model to obtain accurate uncertainty measurements of outputs through Monte-Carlo simulation methods. An additional criteria of the model was that it be suitable for integration into an existing prototype integrated intelligent maintenance system. It is anticipated that applying an integrated maintenance management system will apply significant downward pressure on maintenance budgets and reduce denial of service. Accurate cost estimation is therefore of great importance if anticipated cost efficiencies are to be achieved. While the rail industry has been the focus of this work, other industries have been considered and it is anticipated that the approach will be applicable to many other organisations across several asset management intensive industries.
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Challenges in cost analysis of innovative maintenance of distributed high value assetsCondition monitoring is an increasingly important activity, but there is often little thought given to how a condition monitoring approach is going to impact the cost of operating a system. This paper seeks to detail the challenges facing such an analysis and outline the likely steps such an analysis will have to take to more completely understand the problem and provide suitable cost analysis. Adding sensors might be a relatively simple task, but those sensors come with associated cost; not only of the sensor, but of the utilities required to power them, the data gathering and processing and the eventual storage of that data for regulatory or other reasons. By adding condition monitoring sensors as a subsystem to the general system an organisation is required to perform maintenance to the new sensors sub-system. Despite these difficulties it is anticipated that for many high value assets applying condition monitoring will enable significant cost savings through elimination of maintenance activities on assets that do not need such cost and effort expended on them. Further savings should be possible through optimisation of maintenance schedules to have essential work completed at more cost efficient times.
