Supply chains are complex systems, and the decision making and support processes for the development and management of such multifaceted entities are quite challenging. In order to become sustainable, supply chains are well advised to improve their business processes continuously and strive for strategic as well as functional alignment between their member organizations. Current supply chain practices are often ad hoc and lack end-to-end support for the gradual transformation towards supply chain sustainability. This article specifies high-level decision-making stages of sustainable supply chain management (SSCM) and proposes a multi-layered SSCM maturity model. This proposed model leverages a roadmap for sustainable business transformation and is further developed using a sustainability modelling and reporting (SMART) system.

Next-generation power systems aim at optimizing the energy consumption of household appliances by utilising computationally intelligent techniques, referred to as load monitoring. Non-intrusive load monitoring (NILM) is considered to be one of the most cost-effective methods for load classification. The objective is to segregate the energy consumption of individual appliances from their aggregated energy consumption. The extracted energy consumption of individual devices can then be used to achieve demand-side management and energy saving through optimal load management strategies. Machine learning (ML) has been popularly used to solve many complex problems including NILM. With the availability of the energy consumption datasets, various ML algorithms have been effectively trained and tested. However, most of the current methodologies for NILM employ neural networks only for a limited operational output level of appliances and their combinations (i.e., only for a small number of classes). On the contrary, this work depicts a more practical scenario where over a hundred different combinations were considered and labelled for the training and testing of various machine learning algorithms. Moreover, two novel concepts—i.e., thresholding/occurrence per million (OPM) along with power windowing—were utilised, which significantly improved the performance of the trained algorithms. All the trained algorithms were thoroughly evaluated using various performance parameters. The results shown demonstrate the effectiveness of thresholding and OPM concepts in classifying concurrently operating appliances using ML.

Traditional Decision Support Systems (DSS) provide strong data management, modelling and visualisation capabilities for the decision maker but they do not explicitly support scenario management appropriately. Systems that purport to support scenario planning are complex and difficult to use and do not fully support all phases of scenario management. We introduce scenario as a core component of decision making systems and present a life cycle approach for scenario management, which helps the decision maker with idea generation, scenario planning, development, organisation, analysis, execution, evaluation, and decision support. This research designs and develops a domain independent framework and architecture for Scenario-driven Decision Support Systems Generator that aligns the DSS with the scenario management process. We then propose a generalised evaluation process that allows homogeneous and heterogeneous scenario comparisons among multiple instances of similar and dissimilar scenarios respectively. The framework and architecture is implemented and validated through a concrete prototype.

Traditional Decision Support Systems (DSS) provide strong data management, modelling and visualisation capabilities for the decision maker but they do not