Analysing energy use in supply chains: the case of fruits & vegetables and furniture

An increasing number of producers, retailers and third-party logistics providers are interested in carrying out energy assessments of their product supply chain. This is due to sensitivity about climate change and carbon emissions, but also to high energy prices. This paper presents an analytical approach developed to measure energy use in logistics activities in product supply chains. The approach (based on the Life Cycle Approach) quantifies energy use in transport and logistics activities at all stages of a product supply chain. The work has demonstrated that such an assessment approach based on the supply chain is useful in comparing the energy use implications of different strategies. This supply chain approach can be used to consider options such as sourcing and distribution centre locations, transport modes, road freight vehicle types and weights, vehicle load factors, empty running, transport distance and the balance between consumer shopping trips and delivery to the home.

Towards an Application-Specific Thermal Energy Model of Current Processors

Recent developments of high-end processors recognize temperature monitoring and tuning as one of the main challenges towards achieving higher performance given the growing power and temperature constraints. To address this challenge, one needs both suitable thermal energy abstraction and corresponding instrumentation. Our model is based on application-specific parameters such as power consumption, execution time, and asymptotic temperature as well as hardware-specific parameters such as half time for thermal rise or fall. As observed with our out-of-band instrumentation and monitoring infrastructure, the temperature changes follow a relatively slow capacitor-style charge-discharge process. Therefore, we use the lumped thermal model that initiates an exponential process whenever there is a change in processor’s power consumption. Initial experiments with two codes – Firestarter and Nekbone – validate our thermal energy model and demonstrate its use for analyzing and potentially improving the application-specific balance between temperature, power, and performance.