Sökresultat

Industry 4.0 (I4.0) is an emerging concept describing the business setting application of a broad set of digitalisation technologies, connectivity, and automation. The most common critical infrastructure (CI) uses Industrial Control Systems (ICS) for operation and supervisory control. However, the Supervisory Control and Data Acquisition (SCADA) and Internet of things (IoT) systems are examples of

One of the most critical issues faced by chemical industries is reducing the environmental impact caused due to pollution, which has resulted in global warming and health issues. Most chemical industries monitor and use Key Performance Indicators (KPIs) to control pollution, which is currently done by using various KPI's that are dependent on the product they are producing. Therefore, a study is c

“Food processing 4.0” concept denotes processing food in the current digital era by harnessing fourth industrial revolution (called Industry 4.0) technologies to improve quality and safety of processed food products, reduce production costs and time, save energy and resources, as well as diminish food loss and waste. Industry 4.0 technologies have been gaining great attention in recent years, revo

Medical waste is produced in huge quantities daily, and the increasing amount of it is a worldwide issue that makes managing medical waste more and more crucial. Leakage or improper use of medical waste can be harmful, risking the environment and human lives. The pandemic COVID-19 has challenged current practices with the increasing number of waste and the possibility of transferring the virus fro

In this paper, a life cycle assessment (LCA)-based sustainability assessment framework is developed to estimate the environmental impact of production processes. The framework provides a methodical, context-independent, approach to carry out LCA studies. The framework sets guiding principles for products and key performance indicators (KPIs) selection and the associated data requirements in a reco

The primary goal of this paper was to uncover the drivers of digital transformation (DT) in the pharmaceutical industry. The Covid-19 pandemic and tightening regulatory standards in the global pharma industry necessitated pharma executives need to understand the most critical drivers of digitalizing the sector and charting a new path that improves production processes and streamlines these compani

Organizations that manufacture high volumes of complex products (e.g., pharmaceutical, automotive, food) require specific strategies to ensure efficient processing of parts and a sustainable level of inventory by waste minimization. A review of literature sources revealed how inventory is classified and managed amongst organizations and industries and key challenges that current organizations face

Recently, the food supply chain (FSC) has been severely disrupted due to the COVID-19 pandemic, putting the vital flow of food products from farmers and producers to the ultimate consumers at risk. Furthermore, due to the pandemic, several food organizations have been prompted to rethink their strategies for the future. Although the literature on FSC research in the COVID-19 era is increasing, no

This book provides industry insights and fresh ideas for the advancement of the most vital global industry - food. Drawing on their industry and academic expertise the authors have identified three controlling aspects of food business operations that can unleash long term success: consumer health and wellbeing; product and process sustainability; and harnessing advances in digitalization. If devel

In the current competitive scenario, SMEs need to adopt advanced technology in order to comprehend the dynamics of Industry 4.0 and the sustainable operations for operational excellence. The present study discusses the relationship between Industry 4.0 with sustainable operations and operational excellence. A research model is proposed and assessed through structural equation modelling (SEM). The

Food quality has recently received considerable attention from governments, researchers, and consumers due to the increasing demand for healthier and more nutritious food products. Traditionally, food quality is determined using a range of destructive and time-consuming approaches with modest analytical performance, underscoring the urgent need to develop novel analytical techniques. The Fourth In

Meat 4.0 refers to the application the fourth industrial revolution (Industry 4.0) technologies in the meat sector. Industry 4.0 components, such as robotics, Internet of Things, Big Data, augmented reality, cybersecurity, and blockchain, have recently transformed many industrial and manufacturing sectors, including agri-food sectors, such as the meat industry. The need for digitalised and automat

Food loss and waste are major issues in the food industry, and they affect all stages of the food supply chain (FSC). Food loss and waste are linked to environmental deterioration, economic loss, and an increase in hunger. Therefore, the food industry requires sustainable consumption and production (SCP) to reduce losses and waste. The circular economy (CE) concept has become a popular strategy fo

Operations management is a key aspect in any manufacturing business. Optimising the management of manufacturing operations allows improvement of the productivity and efficiency of industrial activities. To achieve this, reducing waste from manufacturing processes and, therefore, implementing lean-manufacturing principles, is key. This article presents a case study to reduce waste in changeover pro

Background: This research aims to identify how blockchain technology could support the ecological embeddedness of the coffee supply chain. Ecological embeddedness is a subset of the circular economy (CE) that demands legitimacy through design changes to product, production and/or packaging for benefits to economic actors and the environment. This is in contrast with legitimacy as a public relation

In a complex manufacturing system such as the multistage manufacturing system, maintaining the quality of the products becomes a challenging task. It is due to the interconnectivity and dependency of factors that can affect the final product. With the increasing availability of data, Machine Learning (ML) approaches are applied to assess and predict quality-related issues. In this paper, several M