Life-cycle-assessment of cast stone manufacturing: a case study

This research paper aims to perform a “cradle-to-gate” carbon dioxide emissions Life Cycle Assessment (LCA) on cast stone products, i.e. to quantify their accumulated CO2 emissions from the extraction of raw materials to a complete finished product. The collected data is mapped using Energy Value Stream Mapping (EVSM) and Sankey diagrams. Areas of carbon footprint reduction are identified, and transportation, packaging and mold-making recommendations are made. The study was undertaken at a manufacturing facility located in the UK and based on three types of materials

Smart Maintenance - maintenance in digitalised manufacturing

What does digitalised manufacturing entail for maintenance organizations? This is a pressing question for practitioners and scholars within industrial maintenance management who are trying to figure out the best ways for responding to the rapid advancement of digital technologies. As technology moves faster than ever before, this is an urgent matter of uttermost importance. Specifically, in order to secure the success of highly automated, intelligent, connected and responsive production systems, industrial maintenance organizations need to transform to become leading enablers of high performance manufacturing in digitalised environments. In this thesis, this transformation is referred to as “Smart Maintenance”. The purpose of this thesis is to ensure high performance manufacturing in digitalised environments by enabling the adoption of Smart Maintenance. In order to stimulate adoption, a holistic understanding of Smart Maintenance is needed. Therefore, the aim of this thesis is to describe future scenarios for maintenance in digitalised manufacturing as well as to conceptualize and operationalize Smart Maintenance. The holistic understanding has been achieved through a phenomenon-driven research approach consisting of three empirical studies using multiple methods. Potential changes for maintenance organizations in digitalised manufacturing are described in 34 projections for the year 2030. From these projections, eight probable scenarios are developed that describe the most probable future for maintenance organizations. In addition, three wildcard scenarios describe eventualities that are less probable, but which could have large impacts. These scenarios serve as input to the long-term strategic development of maintenance organizations.Smart Maintenance is defined as “an organizational design for managing maintenance of manufacturing plants in environments with pervasive digital technologies” and has four core dimensions: data-driven decision-making, human capital resource, internal integration and external integration. Manufacturing plants adopting Smart Maintenance are likely to face implementation issues related to change, investments and interfaces, but the rewards are improved performance along multiple dimensions when internal and external fit have been achieved. Smart Maintenance is operationalized by means of an empirical measurement instrument. The instrument consists of a set of questionnaire items that measure the four dimensions of Smart Maintenance. It can be used by practitioners to assess, benchmark and longitudinally evaluate Smart Maintenance in their organization, and it can be used by researchers to study how Smart Maintenance impacts performance. This thesis has the potential to have a profound impact on the practice of industrial maintenance management. The scenarios described allow managers to see the bigger picture of digitalisation and consider changes that they might otherwise ignore. The rich, understandable, and action-inspiring conceptualization of Smart Maintenance brings clarity to practitioners and policy-makers, supporting them in developing implementation strategies and initiatives to elevate the use of Smart Maintenance. The measurement instrument makes it possible to measure the adoption of Smart Maintenance in manufacturing plants, which serves to develop evidence-based strategies for successful implementation. Taken together, the holistic understanding achieved in this thesis enables the adoption of Smart Maintenance, thereby ensuring high performance manufacturing in digitalised environments

On Discrete-Event Simulation and Integration in the Manufacturing System Development Process

DES is seldom used in the manufacturing system development process, instead it is usually used to cure problems in existent systems. This has the effect that the simulation study alone is considered being the cost driver for the analysis of the manufacturing system. It is argued that this is not a entirely correct view since the analysis has to be performed anyway, and the cost directly related to the simulation study is mainly in the model realization phase. It is concluded that it is preferred if the simulation study life cycle coincides with the corresponding manufacturing system's life cycle to increase the usability of the simulation model and to increase efficiency in the simulation study process. A model is supplied to be used for management and engineering process improvements and for improvements of the organizational issues to support simulation activities. By institutionalizing and utilizing well defined processes the conceived complexity related to DES is considered to be reduced over time. Cost is highly correlated to the time consumed in a simulation study. The presented methodology tries to reduce time consumption and lead-time in the simulation study by: (i)~reducing redundant work, (ii)~reducing rework, and (iii)~moving labor intensive activities forward in time. To reduce the time to collect and analyze input data a framework is provided that aims at delivering high granularity input data without dependencies. The input data collection framework is designed to provide data for operation and analysis of the manufacturing system in several domains. To reduce the model realization time two approaches are presented. The first approach supplies a set of modules that enables parameterized models of automated subassembly systems. The second approach builds and runs the simulation model based on a copy of an MRP database, i.e. there is no manual intervention required to build the simulation model. The approach is designed to forecast the performance of an entire enterprise. Since the model is generated from a database, the approach is highly scalable. Furthermore, the maintenance of the simulation model is reduced considerably

Building the Hyperconnected Society- Internet of Things Research and Innovation Value Chains, Ecosystems and Markets

This book aims to provide a broad overview of various topics of Internet of Things (IoT), ranging from research, innovation and development priorities to enabling technologies, nanoelectronics, cyber-physical systems, architecture, interoperability and industrial applications. All this is happening in a global context, building towards intelligent, interconnected decision making as an essential driver for new growth and co-competition across a wider set of markets. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from research to technological innovation, validation and deployment.The book builds on the ideas put forward by the European Research Cluster on the Internet of Things Strategic Research and Innovation Agenda, and presents global views and state of the art results on the challenges facing the research, innovation, development and deployment of IoT in future years. The concept of IoT could disrupt consumer and industrial product markets generating new revenues and serving as a growth driver for semiconductor, networking equipment, and service provider end-markets globally. This will create new application and product end-markets, change the value chain of companies that creates the IoT technology and deploy it in various end sectors, while impacting the business models of semiconductor, software, device, communication and service provider stakeholders. The proliferation of intelligent devices at the edge of the network with the introduction of embedded software and app-driven hardware into manufactured devices, and the ability, through embedded software/hardware developments, to monetize those device functions and features by offering novel solutions, could generate completely new types of revenue streams. Intelligent and IoT devices leverage software, software licensing, entitlement management, and Internet connectivity in ways that address many of the societal challenges that we will face in the next decade

Intelligent shop scheduling for semiconductor manufacturing

Semiconductor market sales have expanded massively to more than 200 billion dollars annually accompanied by increased pressure on the manufacturers to provide higher quality products at lower cost to remain competitive. Scheduling of semiconductor manufacturing is one of the keys to increasing productivity, however the complexity of manufacturing high capacity semiconductor devices and the cost considerations mean that it is impossible to experiment within the facility. There is an immense need for effective decision support models, characterizing and analyzing the manufacturing process, allowing the effect of changes in the production environment to be predicted in order to increase utilization and enhance system performance. Although many simulation models have been developed within semiconductor manufacturing very little research on the simulation of the photolithography process has been reported even though semiconductor manufacturers have recognized that the scheduling of photolithography is one of the most important and challenging tasks due to complex nature of the process. Traditional scheduling techniques and existing approaches show some benefits for solving small and medium sized, straightforward scheduling problems. However, they have had limited success in solving complex scheduling problems with stochastic elements in an economic timeframe. This thesis presents a new methodology combining advanced solution approaches such as simulation, artificial intelligence, system modeling and Taguchi methods, to schedule a photolithography toolset. A new structured approach was developed to effectively support building the simulation models. A single tool and complete toolset model were developed using this approach and shown to have less than 4% deviation from actual production values. The use of an intelligent scheduling agent for the toolset model shows an average of 15% improvement in simulated throughput time and is currently in use for scheduling the photolithography toolset in a manufacturing plant

A methodology of manufacturing strategy analysis for the manufacturing industries in Saudi Arabia

To further enhance the progress made by its manufacturing industries during the last two decades, the Sixth Development Plan of Saudi Arabia has stressed the importance of diversifying the country’s economic base, reducing its dependence on the production and export of crude oil, and increasing the industrial sector’s contribution to GDP. Since national level industrial policies cannot succeed without the full participation and support of the individual companies, it is necessary for its manufacturing organisations to adopt appropriate methods for increasing their overall competitiveness. This research is concerned with the development of a methodology for manufacturing strategy formulation to help Saudi companies achieve competitiveness in both the local and the international market environment. The work has resulted in a prototype methodology known as MSAMSA - a Methodology of Manufacturing Strategy Analysis for the Manufacturing Industries in Saudi Arabia. The basic concepts of MSAMSA is based on a framework developed previously by the CAMSD research team at Cranfield University, UK. However, the structure and procedures have been further developed to reflect Saudi-specific requirements, and to help link the country’s long-term industrial policy to the medium-term strategic direction of the individual companies. In particular, MSAMSA adopts a generic, extended scheme of manufacturing strategy evaluation, tackling a number of key requirements such as: the need for a more structured way to coherently link strategic policies at different levels, and the need to provide both local- level (internal) and global-level (external) measures to prioritise and evaluate strategic concerns. Industrial case studies have shown that MSAMSA’s approach and compatibility with the current national level policies are both timely and conceptually logical. In addition, these have also highlighted issues which may be of value to the authorities’ future decision-making. Therefore the methodology’s further enhancement and application are anticipated to be of national importance. Due to its generic nature, it should be possible to adopt the extended scheme to satisfy the needs of manufacturing companies within different industrial sectors or even in different countries