The adoption of Industry 4.0 technologies within the manufacturing and process industries is widely accepted to have benefits for production cycles, increase system flexibility and give production managers more options on the production line through reconfigurable systems. A key enabler in Industry 4.0 technology is the rise in Cyber-Physical Systems (CPS) and Digital Twins (DTs). Both technologies connect the physical to the cyber world in order to generate smart manufacturing capabilities. State of the art research accurately describes the frameworks, challenges and advantages surrounding these technologies but fails to deliver on testbeds and case studies that can be used for development and validation. This research demonstrates a novel proof of concept Industry 4.0 production system which lays the foundations for future research in DT technologies, process optimisation and manufacturing data analytics. Using a connected system of commercial off-the-shelf cameras to retrofit a standard programmable logic controlled production process, a digital simulation is updated in real time to create the DT. The system can identify and accurately track the product through the production cycle whilst updating the DT in real-time. The implemented system is a lightweight, low cost, customable and scalable design solution which provides a testbed for practical Industry 4.0 research both for academic and industrial research purposes.

Increasingly, modular housing manufacturers choose to compete by becoming system integrators. The transition to this new business model requires companies to develop system-level knowledge that `they know more than they produce' in managing supply networks. Engineering system design tools, through visualizing supply network design and make processes, can help companies to overcome many uncertainties when they choose to become system integrators within the supply network. This paper illustrates how simulation models can be derived from design characteristics, experiential data, and a pragmatic system engineering design `vee' framework. This helps modular housing managers to observe and compare the risks of different products and supply network configurations, and see how behavior changes of individual organizations impact system-level performance. A modular housing case study illustrates the implementation and benefits of our approach.

In October 2018, The Boeing CompanyCase studies Boeing company, The opened their first production facility in Europe. Located in Sheffield in the United Kingdom, the factory will become an Industry 4.0 flagship facility for Boeing; with robust IT infrastructure and a fully connected virtual simulation model working between its digital and physical systems—a “digital twinDigital twin ” factory. With the vision of developing a digital twinDigital twin factory, the Boeing Information Technology and Data AnalyticsData Analytics (DA) team collaborated with the University of Sheffield Advanced Management Research Centre’s (AMRC) Manufacturing Intelligence (MI) team led by Dr Ruby Hughes to set out a strategic plan to simulate the current factory concept, de-risk the introduction of new technologies, monitor factory performance in real-time, and feedback optimal decisions back to the physical environment based on the latest factory situation data. This chapter presents the key elements within the first stage of the strategy plan—simulate—and discusses the approach of linking the simulation model to physical systems to achieve the creation of a digital twinDigital twin factory.

Productivity in the UK construction sector has historically lagged behind other industry sectors. The government is aiming to improve this through increasing the level of pre-manufactured value in built assets. Since 2001, the University of Sheffield's Advanced Manufacturing Research Centre has been developing technological innovations for the aerospace and automotive sectors. This paper shows how lessons learnt from, and technologies developed for, these sectors can be transferred into the construction supply chain through horizontal innovation. Technologies such as robotics and automation, augmented and virtual reality, discrete event simulation, large volume metrology, and improved tools and processes all have a role to play. Significant productivity increases are possible, with the benefit often driven by the digitalisation of traditionally manual paper-based processes.

Manufacturing is a competitive global market and efforts to mitigate climate change are at the forefront of public perception. Current trends in manufacturing aim to reduce costs and increase sustainability without negatively affecting the yield of finished products, thus maintaining or improving profits. Effective use of energy within a manufacturing environment can help in this regard by lowering overhead costs. Significant benefit can be gained by utilising simulations in order to predict energy demand allowing companies to make effective retrofit decisions based on energy as well as other metrics such as resource use, throughput and overhead costs. Traditionally, Building Energy Modelling (BEM) and Manufacturing Process Simulation (MPS) have been used extensively in their respective fields but they remain separate and segregated which limits the simulation window used to identify energy improvements. This review details modelling approaches and the simulation tools that have been used, or are available, in an attempt to combine BEM and MPS, or elements from each, into a holistic approach. Such an approach would be able to simulate the interdependencies of multiple layers contained within a factory from production machines, process lines and Technical Building Services (TBS) to the building shell. Thus achieving a greater perspective for identifying energy improvement measures across the entire operating spectrum and multiple, if not all, manufacturing industries. In doing so the challenges associated with incorporating BEM in manufacturing simulation are highlighted as well as gaps within the research for exploitation through future research. This paper identified requirements for the development of a holistic energy simulation tool for use in a manufacturing facility, that is capable of simulating interdependencies between different building layers and systems, and a rapid method of 3D building geometry generation from site data or existing BIM in an appropriate format for energy simulations of existing factory buildings.

Objective The objective of this article was to conduct a systematic review of published research on the use of discrete event simulation (DES) for resource modelling (RM) in health technology assessment (HTA). RM is broadly defined as incorporating and measuring effects of constraints on physical resources (e.g. beds, doctors, nurses) in HTA models. Methods Systematic literature searches were conducted in academic databases (JSTOR, SAGE, SPRINGER, SCOPUS, IEEE, Science Direct, PubMed, EMBASE) and grey literature (Google Scholar, NHS journal library), enhanced by manual searchers (i.e. reference list checking, citation searching and hand-searching techniques). Results The search strategy yielded 4117 potentially relevant citations. Following the screening and manual searches, ten articles were included. Reviewing these articles provided insights into the applications of RM: firstly, different types of economic analyses, model settings, RM and cost-effectiveness analysis (CEA) outcomes were identified. Secondly, variation in the characteristics of the constraints such as types and nature of constraints and sources of data for the constraints were identified. Thirdly, it was found that including the effects of constraints caused the CEA results to change in these articles. Conclusion The review found that DES proved to be an effective technique for RM but there were only a small number of studies applied in HTA. However, these studies showed the important consequences of modelling physical constraints and point to the need for a framework to be developed to guide future applications of this approach.

Background Numerous studies examine simulation modelling in healthcare. These studies present a bewildering array of simulation techniques and applications, making it challenging to characterise the literature. Objective The aim of this paper is to provide an overview of the level of activity of simulation modelling in healthcare and the key themes. Methods We performed an umbrella review of systematic literature reviews of simulation modelling in healthcare. Searches were conducted of academic databases (JSTOR, Scopus, PubMed, IEEE, SAGE, ACM, Wiley Online Library, ScienceDirect) and grey literature sources, enhanced by citation searches. The articles were included if they performed a systematic review of simulation modelling techniques in healthcare. After quality assessment of all included articles, data were extracted on numbers of studies included in each review, types of applications, techniques used for simulation modelling, data sources and simulation software. Results The search strategy yielded a total of 117 potential articles. Following sifting, 37 heterogeneous reviews were included. Most reviews achieved moderate quality rating on a modified AMSTAR (A Measurement Tool used to Assess systematic Reviews) checklist. All the review articles described the types of applications used for simulation modelling; 15 reviews described techniques used for simulation modelling; three reviews described data sources used for simulation modelling; and six reviews described software used for simulation modelling. The remaining reviews either did not report or did not provide enough detail for the data to be extracted. Conclusion Simulation modelling techniques have been used for a wide range of applications in healthcare, with a variety of software tools and data sources. The number of reviews published in recent years suggest an increased interest in simulation modelling in healthcare.

The need to fully integrate simulation as a daily tool has been subject to much attention over the past few years. This study investigated the reasons behind the limited use of simulation and the past experiences of companies which implemented simulation. A literature review, questionnaire survey and case study were conducted to examine these factors. Subsequently, an easy-to-follow framework for enabling companies to embed simulation technologies into their business processes was developed. The framework comprises five key stages, namely: foundation, introduction, infrastructure, deployment and embedding. It provides a best practice approach to guide companies to integrate simulation as a mainstream technology.

Simulation is a well-known Operational Research technique, and it has been widely used in the healthcare sector for clinical decision-making, facility location planning, and resource allocation. Discrete-event simulation (DES) and System Dynamics (SD) are the most popular simulation techniques used for these purposes, mainly because of their abilities to model uncertainty and variability.This paper describes a case study of using SD approach to investigate the dynamic complexity of the National Health Service (NHS) 18-weeks pathway. Since the 18 weeks pathway involves a large population and with complex definition of patient status, SD was chosen for this purpose. This study applied both the qualitative aspect (by using causal loop diagram) and the quantitative aspect (by using stock and flow model). Healthcare managers found this SD approach very easy to apply, and very helpful for enhancing the understanding to the dynamic nature of a complex system.