This paper investigates using simulation to predict the benefits and costs of digitalising cold distribution chains. The study focuses on the distribution of refrigerated beef in the UK, where digitalisation was implemented to re-route cargo carriers. By comparing simulations of both digitalised and non-digitalised supply chains, the study found that digitalisation can reduce beef waste and decrease the number of miles driven per successful delivery, leading to potential cost savings. Note that this work is not attempting to prove that digitalisation is appropriate for the chosen scenario, only to justify a simulation approach as a decision making tool. The proposed modelling approach provides decision-makers with more accurate predictions of the cost–benefit of increased sensorisation in supply chains. By accounting for stochastic and variable parameters, such as weather and demand fluctuations, simulation can be used to identify potential challenges and estimate the economic benefits of digitalisation. Moreover, qualitative assessments of the impact on customer satisfaction and product quality can help decision-makers consider the broader impacts of digitalisation. Overall, the study suggests that simulation can play a crucial role in facilitating informed decisions about the implementation of digital technologies in the food supply chain. By providing a better understanding of the potential costs and benefits of digitalisation, simulation can help organisations make more strategic and effective decisions.

Every year more than 900 million tonnes of food is wasted, contributing to almost 10% of total greenhouse gas emissions. Reducing food waste has been identified as essential to tackle the current climate crisis, and links to several UN’s sustainable development goals. This is especially critical for energy and resource-intensive food products like meat, whose consumption is predicted to reach an historical maximum by 2030. Whilst wastage occurs at all stages of the supply chain, tractable data about the journey of food from production to consumer remains largely hidden or unrecorded. Powered by the latest advances in sensing like smart food packaging and digital technologies such as Big Data and IoT, Digital Twins offer a valuable opportunity to monitor and control meat products and processes across the whole supply chain, enabling food waste to be reduced and by-products reintegrated into the supply chain. This paper proposes a new framework for a Digital Twin that integrates key technological enablers across different areas of the meat supply chain towards with the goal of a “zero-waste”, circular meat supply chain.

This paper presents an ultra low-power class AB operational amplifier (OpAmp) designed in a low-cost 0.18 μm CMOS technology. Rail-to-rail input operation is achieved by using complementary input pairs with adaptive bias to enhance slew-rate. A class AB output stage is employed. For low-voltage low-power operation, the transistors both in the input and the output stage are biased in the sub-threshold region. The simulated DC open loop gain is 51 dB, the unity gain frequency is 40 kHz with a 65° phase margin and the slew-rate is 0.12 V/μs with 10 pF capacitive loads. A common-mode feed-forward circuit (CMFF) increases CMRR, keeping the DC gain almost constant: its relative error remains below 1 % for a (-40°C, +120°C) temperature range. The proposed OpAmp consumes only 1 μW at 0.8 V supply.

Achieving Net Zero requires designers to have a better understanding of the product use with studies showing user behaviour, cultural norms, seasonality and product interactions concomitantly dictate energy consumption. Data on product use can support data-driven design processes that have been shown to improve the efficiency of existing products. The paper reports a method that generates data for data-driven design processes from non-intrusive load monitoring (NILM) of household energy consumption data. The method produced appliance classification accuracies of 0.9984 while reducing sample size, sampling frequency and machine learning model complexity showing potential for it to be deployed at scale across communities.

Food production systems are shaped by external factors, such as social events and economic shifts, which influence and are influenced by labour dynamics—e.g., workforce availability—and human factors—e.g., worker skills. Using a systems approach, this paper explores how labour shortages impacting worker teams—such as in terms of mixture of availability, skills, and human behaviours—affect production and quality. UK apple harvesting is chosen as a case study due to its reliance on skilled seasonal migrant workers. Findings highlight the need for strategies such as upskilling local workers, enhancing training programmes, and adopting new technologies to mitigate labour shortages and enable high-performance collaborative worker groups.