RSS 1.0Business and management
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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.
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An autonomous system for maintenance scheduling data-rich complex infrastructure: fusing the railways’ condition, planning and costNational railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment. Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain. Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel integrated system for automatic job scheduling is presented; from concept formulation to the examination of the data to information transitional level interface, and at the decision making level. The underlying architecture configures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value. A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise degradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum scheduling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account specific cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The demonstrator structure was found fit for purpose with logical component relationships, offering further scope for research and commercial exploitation.
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Software obsolescence drivers in aerospace: an industry analysisSoftware applications have become crucial for the aerospace industry, providing a wide range of functionalities and capabilities. However, due to the considerable time difference between aircraft and software life cycles, obsolescence has turned into a major challenge for industry in last decades. This paper aims to provide a view on the different causes of software obsolescence within aerospace industry, as well as a perception on the importance of each of them. The key research question addressed is what drives software obsolescence in the aerospace industry, managing large software application portfolios. This question has been addressed by conducting firstly an in depth review of current literature and secondly by arranging an industry workshop with professionals from aerospace and consulting companies. The result is a set of drivers of software obsolescence, distributed among three different environments and several domains. By incorporating monitoring methodologies to assess those software obsolescence drivers, benefits in maintenance efforts and operations disruption avoidance are expected.
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A cost–benefit framework for assessing advanced manufacturing technology development: a case studyDevelopment of new advanced manufacturing technology for the aerospace industry is critical to enhance the manufacture and assembly of aerospace products. This article presents, verifies and validates a cost–benefit forecasting framework for the initial stages of advanced manufacturing technology development. The framework improves the decision-making process of which potential advanced manufacturing technologies to select and develop from concept to full-scale demonstration. Cost is the first element and is capable of forecasting the advanced manufacturing technology development effort in person-hours and cost of hardware using two parametric cost models. Benefit is the second element and forecasts the advanced manufacturing technology tangible and intangible performance. The proposed framework plots these quantified cost–benefit parameters to present development value advice for a diverse range of advanced manufacturing technologies. A detailed case study evaluating a total of 23 novel aerospace advanced manufacturing technologies verifies the capability and high accuracy of the framework within a large aerospace manufacturing organisation. Further validation is provided by quantifying the responses from 10 advanced manufacturing technology development experts, after utilising the methodology within an industrial setting. The case study demonstrates that quantifying the cost-benefit parameters provides the ability to select advanced manufacturing technologies that generate the best value to a business.
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Individual values and SME environmental engagementWe study the values on which managers of small and medium-sized enterprises (SMEs) draw when constructing their personal and organizational-level engagement with environmental issues, particularly climate change. Values play an important mediating role in business environmental engagement, but relatively little research has been conducted on individual values in smaller organizations. Using the Schwartz Value System (SVS) as a framework for a qualitative analysis, we identify four “ideal-types” of SME managers and provide rich descriptions of the ways in which values shape their constructions of environmental engagement. In contrast to previous research, which is framed around a binary divide between self-enhancing and self-transcending values, our typology distinguishes between individuals drawing primarily on Power or on Achievement values and indicates how a combination of Achievement and Benevolence values is particularly significant in shaping environmental engagement. This demonstrates the theoretical usefulness of focusing on a complete range of values. Implications for policy and practice are discussed.
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The mediating effect of environmental and ethical behaviour on supply chain partnership decisions and management appreciation of supplier partnership risksGreen supply chain management and environmental and ethical behaviour (EEB), a major component of corporate responsibility (CR), are rapidly developing fields in research and practice. The influence and effect of EEB at the functional level, however, is under-researched. Similarly, the management of risk in the supply chain has become a practical concern for many firms. It is important that managers have a good understanding of the risks associated with supplier partnerships. This paper examines the effect of firms investment in EEB as part of corporate social responsibility in mediating the relationship between supply chain partnership (SCP) and management appreciation of the risk of partnering. We hypothesise that simply entering into a SCP does not facilitate an appreciation of the risk of partnering and may even hamper such awareness. However, such an appreciation of the risk is facilitated through CRs environmental and stakeholder management ethos. The study contributes further by separating risk into distinct relational and performance components. The results of a firm-level survey confirm the mediation effect, highlighting the value to supply chain strategy and design of investing in EEB on three fronts: building internal awareness, monitoring and sharing best practice.
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Applying blockchain technology to improve agri-food traceability: A review of development methods, benefits and challengesTraceability plays a vital role in food quality and safety management. Traditional Internet of Things (IoT) traceability systems provide the feasible solutions for the quality monitoring and traceability of food supply chains. However, most of the IoT solutions rely on the centralized server-client paradigm that makes it difficult for consumers to acquire all transaction information and to track the origins of products. Blockchain is a cutting-edge technology that has great potential for improving traceability performance by providing security and full transparency. However, the benefits, challenges and development methods of blockchain-based food traceability systems are not yet fully explored in the current literature. Therefore, the main aim of this paper is to review the blockchain technology characteristics and functionalities, identify blockchain-based solutions for addressing food traceability concerns, highlight the benefits and challenges of blockchain-based traceability systems implementation, and help researchers and practitioners to apply blockchain technology based food traceability systems by proposing an architecture design framework and suitability application analysis flowchart of blockchain based food traceability systems. The results of this study contribute to better understanding and knowledge on how to improve the food traceability by developing and implementing blockchain-based traceability systems. The paper provides valuable information for researchers and practitioners on the use of blockchain-based food traceability management and has a positive effect on the improvement of food sustainability.
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Transport analytics in action: a cloud-based decision support system for efficient city bus transportationOptimising city bus transport operations helps conserve fuel by providing the urban transport service as efficiently as possible. This study develops a Cloud-based Decision Support System (C-DSS) for transport analytics. The C-DSS is based on an intelligent model on location of depots for opening new depots and/or closing a few existing depots and allocation of city-buses to depots. The C-DSS is built on the Cloud Computing architecture with three layers and includes an efficient and simple greedy heuristic algorithm. Using modern information and communications technology tools, the proposed C-DSS minimizes the cost of city bus transport operations and in turn to reduce fuel consumption and CO2 emissions in urban passenger transport. The proposed C-DSS is demonstrated for its workability and evaluated for its performance on 25 large scale pseudo data generated based on the observation from Bangalore Metropolitan Transport Corporation (BMTC) in India.
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Environmental factors influencing the management of key accounts in an Arab Middle Eastern contextWithin the sales and marketing literature, it is recognised that a range of external factors can influence how companies in the business-to-business field manage business relationships within national and across international borders. However, there have been very few studies that explore the influence of the external environment on key account relationships, especially within the context of emerging economies. This study draws on the network approach and contingency theory to identify and highlight the influence of external environmental factors on the management of inter-organisational relationships with key customers in emerging economies in the Arab Middle East region. It is based on an extensive qualitative enquiry that utilises 50 in-depth semi-structured interviews conducted in Jordan with endogenous and Western firms. It concludes that key account practices within an Arab context are shaped by a number of contingencies that are embedded in broader institutional contexts and the business environment, which may challenge the adoption of company-wide universal key account management policies across borders. © 2014 Elsevier Inc.
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Relational resources for emerging markets’ non-technological innovation: insights from China and TaiwanPurpose: This paper aims to highlight the importance of relational resources (trust and relationship effectiveness). The authors investigate how the Chinese guanxi is utilized to create and develop service exploitation and exploration activities for adopting non-technological innovations. Design/methodology/approach: This study surveyed 252 Chinese and Taiwanese firms. The results were analyzed through structural equation model. Findings: Relational antecedents of collaborative communication and constructive conflict positively relate to trust, as well as to relationship effectiveness. Constructive conflict positively relates to exploration and exploitation. Relationship effectiveness and trust mediate two relational antecedents to exploitation. Relationship effectiveness crucially mediates two relational antecedents to exploration. Research limitations/implications: Dyadic data would be more desirable to study firm interactions. Practical implications: Chinese society perceives conflict as being detrimental to relationships. Constructive conflict enhances inter-firm trust and relationship effectiveness. Relationship effectiveness, which motivates suppliers to mobilize their guanxi network, mediates the supplier–customer interaction in broadening relationships to produce new services, as well as reinforcing networks to strengthen existing ventures. Originality/value: This study contributes to a relatively under-explored relationship effectiveness area. Chinese suppliers capitalize their guanxi networks to achieve competitive advantages in non-technological innovation.
