Digital Society Ecosystem Impact on Creative Industry

Industry 4.0 phenomenon has emerged since many technological breakthroughs developed in the past decades. Human well-being behavior are basically influenced by the digital technology. The current customers incline the need for customized products. This situation drive the production paradigm shift from the mass production to the individual production. This paradigm shift force companies to own more resources. Companies’ collaboration is a way to win the competition. Industrial revolution era bring the fact that dominant economic activity is coming from a strong business ecosystem. The major impact of digitalization is faced by the creative industries, an industry priority and a \u27laboratory\u27 for studying economic transformation and modern society. This paper will review the digitalization in industry 4.0 era, business ecosystem and society shift, and the digitalization impact on creative industry. Keywords Industry 4.0; business ecosystem; society shift; creative industr

Development of an Extended Product Lifecycle Management through Service Oriented Architecture.

Organised by: Cranfield UniversityThe aim of this work is to define new business opportunities through the concept of Extended Product Lifecycle Management (ExtPLM), analysing its potential implementation within a Service Oriented Architecture. ExtPLM merges the concepts of Extended Product, Avatar and PLM. It aims at allowing a closer interaction between enterprises and their customers, who are integrated in all phases of the life cycle, creating new technical functionalities and services, improving both the practical (e.g. improving usage, improving safety, allowing predictive maintenance) and the emotional side (e.g. extreme customization) of the product.Mori Seiki – The Machine Tool Company; BAE Systems; S4T – Support Service Solutions: Strategy and Transitio

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Essential Considerations for Establishing Partnerships Among Agencies Addressing the Employment-Related Needs of Individuals with Disabilities

[Excerpt] The Collaboration Brief series is intended to assist both generic and disability-specific agencies to work collaboratively and enhance their capacity to serve individuals with disabilities. To help agencies become familiar with their mandated and non-mandated partners, these briefs provide information that will contribute to better understanding of the goals, eligibility criteria, and policy parameters of the respective generic and disability-specific agencies; the development of expanded and improved collaborative relationships; and the coordination of resources, services, and supports. These briefs are consistent with and reflect the overarching goal of the Workforce Investment Act (WIA)— to develop a seamless workforce investment system that includes multiple agencies and programs. The series includes briefs on the One-Stop Career Centers established under Title I of the WIA and the one disability-related mandatory partner participating in the workforce investment system (vocational rehabilitation agencies). In addition, the series includes employment-related services and supports provided by other federal, state, and local agencies and programs serving people with significant disabilities, including Mental Health, Developmental Disabilities, and Special Education. Further, the series explains the potential role the Medicaid program can play in supporting employment and the work incentive provisions in Supplemental Security Income (SSI) and Social Security Disability Insurance (SSDI) programs, and describes the Ticket to Work Program operated by the Social Security Administration. Each brief provides information on the purpose of the program, eligibility for benefits or services, funding sources, administrative structure, and resources provided to support jobseekers and employers. Further, the briefs provide considerations for assessing the respective programs in each state and suggestions for the development of collaborative relationships. The concepts and strategies of Customized Employment—a dynamic set of assessment and job development tools—will be used to contextualize the collaborative strategies discussed in each brief. Most importantly, the briefs show that no agency is alone or limited to their own resources in serving people with significant disabilities; this series should be used as a source for the basic information upon which cross-system partnerships are built. Collaborative relationships between One-Stop Career Centers, Vocational Rehabilitation, community provider organizations, and other systems that provide benefits and services will create new employment opportunities for people with significant disabilities

Recommender System Using Collaborative Filtering Algorithm

With the vast amount of data that the world has nowadays, institutions are looking for more and more accurate ways of using this data. Companies like Amazon use their huge amounts of data to give recommendations for users. Based on similarities among items, systems can give predictions for a new item’s rating. Recommender systems use the user, item, and ratings information to predict how other users will like a particular item. Recommender systems are now pervasive and seek to make profit out of customers or successfully meet their needs. However, to reach this goal, systems need to parse a lot of data and collect information, sometimes from different resources, and predict how the user will like the product or item. The computation power needed is considerable. Also, companies try to avoid flooding customer mailboxes with hundreds of products each morning, thus they are looking for one email or text that will make the customer look and act. The motivation to do the project comes from my eagerness to learn website design and get a deep understanding of recommender systems. Applying machine learning dynamically is one of the goals that I set for myself and I wanted to go beyond that and verify my result. Thus, I had to use a large dataset to test the algorithm and compare each technique in terms of error rate. My experience with applying collaborative filtering helps me to understand that finding a solution is not enough, but to strive for a fast and ultimate one. In my case, testing my algorithm in a large data set required me to refine the coding strategy of the algorithm many times to speed the process. In this project, I have designed a website that uses different techniques for recommendations. User-based, Item-based, and Model-based approaches of collaborative filtering are what I have used. Every technique has its way of predicting the user rating for a new item based on existing users’ data. To evaluate each method, I used Movie Lens, an external data set of users, items, and ratings, and calculated the error rate using Mean Absolute Error Rate (MAE) and Root Mean Squared Error (RMSE). Finally, each method has its strengths and weaknesses that relate to the domain in which I am applying these methods

Geoweb 2.0 for Participatory Urban Design: Affordances and Critical Success Factors

In this paper, we discuss the affordances of open-source Geoweb 2.0 platforms to support the participatory design of urban projects in real-world practices.We first introduce the two open-source platforms used in our study for testing purposes. Then, based on evidence from five different field studies we identify five affordances of these platforms: conversations on alternative urban projects, citizen consultation, design empowerment, design studio learning and design research. We elaborate on these in detail and identify a key set of success factors for the facilitation of better practices in the future