A Dynamic Knowledge Management Framework for the High Value Manufacturing Industry

Dynamic Knowledge Management (KM) is a combination of cultural and technological factors, including the cultural factors of people and their motivations, technological factors of content and infrastructure and, where these both come together, interface factors. In this paper a Dynamic KM framework is described in the context of employees being motivated to create profit for their company through product development in high value manufacturing. It is reported how the framework was discussed during a meeting of the collaborating company’s (BAE Systems) project stakeholders. Participants agreed the framework would have most benefit at the start of the product lifecycle before key decisions were made. The framework has been designed to support organisational learning and to reward employees that improve the position of the company in the market place

RAMARL: Robustness Analysis with Multi-Agent Reinforcement Learning - Robust Reasoning in Autonomous Cyber-Physical Systems

A key driver to offering smart services is an infrastructure of Cyber-Physical systems (CPS)s. By definition, CPSs are intertwined physical and computational components that integrate physical behaviour with computation. The reason is to autonomously execute a task or a set of tasks providing a service or a list of end-users services. In real-life applications, CPSs operate in dynamically changing surroundings characterized by unexpected or unpredictable situations. Such operations involve complex interactions between multiple intelligent agents in a highly non-stationary environment. For safety reasons, a CPS should withstand a certain amount of disruption and exert the operations in a stable and robust manner when performing complex tasks. Recent advances in reinforcement learning have proven suitable for enabling multi-agents to robustly adapt to their environment, yet they often depend on a massive amount of training data and experiences. In these cases, robustness analysis outlines necessary components and specifications in a framework, ensuring reliable and stable behaviour while considering the dynamicity of the environment. This paper presents a combination of multi-agent reinforcement learning with robustness analysis shaping a cyber-physical system infrastructure that reasons robustly in a dynamically changing environment. The combination strengthens the reinforcement learning, increasing the reliability and flexibility of the system by applying robustness analysis. Robustness analysis identifies vulnerability issues when the system interacts within a dynamically changing environment. Based on this identification, when incorporated into the system, robustness analysis suggests robust solutions and actions rather than optimal ones provided by reinforcement learning alone. Results from the combination show that this infrastructure can enable reliable operations with the flexibility to adapt to the changing environment dynamics.publishedVersio

Understanding Managerial Decisions about Global Sourcing: Offshoring and Reshoring of Production

As international commerce continues to emerge due to telecommunication and transportation breakthroughs, the eagerness of companies to send particular business functions offshore increases. Offshoring is the removal of a company function (particularly, manufacturing) from a domestic location to a remote destination. Since many developing economies contain low labor wages, companies in the United States and Europe are able to leverage cost savings by paying low compensation to foreign production employees. The low cost concept, though, does not always offer significant financial reward. For companies with particular product types, business models, or limited experience, offshoring proves to be an expensive mistake that is difficult to reverse. Even so, some U.S. enterprises are reshoring their production function to combat the issues faced in the foreign manufacturing sector. This study aims to investigate the problems of offshoring and proposes a “systems-view” decision framework for global sourcing

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Enabling flexibility through strategic management of complex engineering systems

”Flexibility is a highly desired attribute of many systems operating in changing or uncertain conditions. It is a common theme in complex systems to identify where flexibility is generated within a system and how to model the processes needed to maintain and sustain flexibility. The key research question that is addressed is: how do we create a new definition of workforce flexibility within a human-technology-artificial intelligence environment? Workforce flexibility is the management of organizational labor capacities and capabilities in operational environments using a broad and diffuse set of tools and approaches to mitigate system imbalances caused by uncertainties or changes. We establish a baseline reference for managers to use in choosing flexibility methods for specific applications and we determine the scope and effectiveness of these traditional flexibility methods. The unique contributions of this research are: a) a new definition of workforce flexibility for a human-technology work environment versus traditional definitions; b) using a system of systems (SoS) approach to create and sustain that flexibility; and c) applying a coordinating strategy for optimal workforce flexibility within the human- technology framework. This dissertation research fills the gap of how we can model flexibility using SoS engineering to show where flexibility emerges and what strategies a manager can use to manage flexibility within this technology construct”--Abstract, page iii

An experiment in remote manufacturing using the advanced communications technology satellite

The goal of the completed project was to develop an experiment in remote manufacturing that would use the capabilities of the ACTS satellite. A set of possible experiments that could be performed using the Advanced Communications Technology Satellite (ACTS), and which would perform remote manufacturing using a laser cutter and an integrated circuit testing machine are described in detail. The proposed design is shown to be a feasible solution to the offered problem and it takes into consideration the constraints that were placed on the experiment. In addition, we have developed two more experiments that are included in this report: backup of rural telecommunication networks, and remote use of Synthetic Aperture Radar (SAR) data analysis for on-site collection of glacier scattering data in the Antarctic