Parts verification for multi-level-dependent demand manufacturing systems: a recognition and classification structure

This research has developed and implemented a part recognition and classification structure to execute parts verification in a multi-level dependent demand manufacturing system. The part recognition algorithm enables the parent and child relationship between parts to be recognised in a finite-capacitated manufacturing system. This algorithm was developed using SIMAN simulation language and implemented in a multi-level dependent demand manufacturing simulation model. The part classification structure enables the modelling of a multi-level dependent demand manufacturing between parts to be carried out effectively. The part classification structure was programmed using Visual Basic Application (VBA) and was integrated to the work-to-list generated from a simulated MRP model. This part classification structure was then implemented in the multi-level dependent demand manufacturing simulation model. Two stages of implementation, namely parameterisation and execution, of the part recognition and classification structure were carried out. A real case study was used and five detail steps of execution were processed. Simulation experiments and MRP were run to verify and validate the part recognition and classification structure. The results led to the conclusion that implementation of the recognition and classification structure has effectively verified the correct parts and sub-assemblies used for the correct product and order. No parts and sub-assemblies shortages were found, and the quantity required was produced. The scheduled release for some orders was delayed due to overload of the required resources. When the loading is normal, all scheduled release timing is adhered to. The recognition and classification structure has a robust design; hence it can be easily adapted to new systems parameter to study a different or more complex case

Towards sustainable food production and climate change mitigation: an attributional life cycle assessment comparing industrial and basalt rock dust fertilisers

This is the final version. Available on open access from Springer via the DOI in this recordData availability: The data supporting the findings of this study are available in the manuscript and its supporting analysis.Purpose Food production is set to double by 2050 to feed the increasing world population. This poses a global challenge to minimise environmental impacts from intensified production and use of chemical fertilisers. The study investigates whether basalt rock dust fertiliser can be an environmentally sustainable close substitute to expensive conventional rock-derived P and K fertilisers. Method The study uses the attributional life cycle assessment method to estimate and compare 15 environmental impacts between basalt rock dust fertiliser, a potential source of phosphorus (P) and potassium (K), and five widely used industrial P and K fertilisers. In addition, we model hypothetical basalt substitution rates for PK fertilisers to highlight potential ecological savings in terms of carbon capture. Results Basalt rock dust fertiliser has minimal embodied environmental impacts across all 15 impact categories, including global warming, compared to industrial P and K fertilisers. Conclusion Our results suggest that transitioning to milled basalt as a natural geo-fertiliser to support food production may help address several UN Sustainable Development Goals such as ‘Responsible consumption and production’ and ‘Climate Action and Zero Hunger’

An agenda for integrated system-wide interdisciplinary agri-food research

© 2017 The Author(s)This paper outlines the development of an integrated interdisciplinary approach to agri-food research, designed to address the ‘grand challenge’ of global food security. Rather than meeting this challenge by working in separate domains or via single-disciplinary perspectives, we chart the development of a system-wide approach to the food supply chain. In this approach, social and environmental questions are simultaneously addressed. Firstly, we provide a holistic model of the agri-food system, which depicts the processes involved, the principal inputs and outputs, the actors and the external influences, emphasising the system’s interactions, feedbacks and complexities. Secondly, we show how this model necessitates a research programme that includes the study of land-use, crop production and protection, food processing, storage and distribution, retailing and consumption, nutrition and public health. Acknowledging the methodological and epistemological challenges involved in developing this approach, we propose two specific ways forward. Firstly, we propose a method for analysing and modelling agri-food systems in their totality, which enables the complexity to be reduced to essential components of the whole system to allow tractable quantitative analysis using LCA and related methods. This initial analysis allows for more detailed quantification of total system resource efficiency, environmental impact and waste. Secondly, we propose a method to analyse the ethical, legal and political tensions that characterise such systems via the use of deliberative fora. We conclude by proposing an agenda for agri-food research which combines these two approaches into a rational programme for identifying, testing and implementing the new agri-technologies and agri-food policies, advocating the critical application of nexus thinking to meet the global food security challenge

Supply chain sustainability performance measurement of small and medium sized enterprises using structural equation modeling

Sustainability of small and medium sized enterprises (SMEs) is significant as SMEs contribute to GDP substantially in every economy. This research develops an innovative sustainable supply chain performance measurement model for SMEs. Prior researches predominantly use balanced score card (BSC) approach that presume causal relationship of criteria and Data Envelopment Analysis (DEA), which derive efficiency of units from a few input and output criteria. While DEA is effective for policymakers, BSC is more suitable for individual SME. The proposed method that uses structural equation modeling (SEM) approach to derive the relationship of criteria and criteria weights formulates regression-type models for a specific region as well as for specific SME. The SEM-based supply chain sustainability performance measurement model is beneficial to policymakers as they can determine means for improvement at a regional level. The proposed method could also facilitate managers/owners of individual SMEs with measures for improving their supply chain sustainability performance. The method has been applied to three varied geographical locations in the UK, France and India in order to demonstrate its effectiveness

Benchmarking carbon emissions performance in supply chains

Purpose: The paper aims to develop a benchmarking framework to address issues such as supply chain complexity and visibility, geographical differences and non-standardized data, ensuring that the entire supply chain environmental impact (in terms of carbon) and resource use for all tiers, including domestic and import flows, are evaluated. Benchmarking has become an important issue in supply chain management practice. However, challenges such as supply chain complexity and visibility, geographical differences and non-standardized data have limited the development of approaches for evaluating performances of product supply chains. This industry-level benchmarking approach ensures that individual firms can compare their carbon emissions against other similarly structured firms. Design/methodology/approach: Benchmarking has become an important issue in supply chain management practice. However, challenges such as supply chain complexity and visibility, geographical differences and non-standardized data have limited the development of approaches for evaluating performances of product supply chains. The paper aims to develop a benchmarking framework to address these issues, ensuring that the entire supply chain environmental impact (in terms of carbon) and resource use for all tiers, including domestic and import flows, are evaluated. This industry-level benchmarking approach ensures that individual firms can compare their carbon emissions against other similarly structured firms. Findings: Supply chain carbon maps are developed as a means of producing industry-level benchmarks to set a measure for the environmental sustainability of product supply chains. The industry-level benchmark provides the first step for firms to manage the environmental performance, identify and target high carbon emission hot-spots and for cross-sectorial benchmarking. Originality/value: The paper links the theoretical development of supply chain environmental system based on the Multi-Regional Input–Output model to the innovative development of supply chain carbon maps, such that an industry-level benchmarking framework is produced as a means of setting product supply chain carbon emissions benchmarks