Digitalisation of Development and Supply Networks: Sequential and Platform-Driven Innovations

We draw from an eight-year dataset of 98 organisational entities involved in pre-competitive innovation networks across the UK pharmaceutical sector. These data map into three networks that are representative of: (i) a product development-led sequential pathway that begins with digitalised product development, followed by digitalisation of supply networks, (ii) a supply network-led sequential pathway that starts with digitalised supply networks, followed by digitalisation of product development, and (iii) a parallel — platform-driven — pathway that enables simultaneous digitalisation of development, production, and supply networks. We draw upon extant literature to assess these network structures along three dimensions — strategic intent, the integrative roles of nodes with high centrality, and innovation performance. We conduct within-case and cross-case analyses to postulate 10 research propositions that compare and contrast modalities for sequential and platform-based digitalisation involving collaborative innovation networks. With sequential development, our propositions are congruent with conventional pathways for mitigating innovation risks through modular moves. On the other hand, we posit that platform-based design rules, rather than modular moves, mitigate the risks for parallel development pathways, and lead to novel development and delivery mechanisms

Traceability systems in the manufacturing industry: A systematic literature review

Traceability, the ability to generate knowledge about where, when, how, and of what materials a product was made, is a basic requirement in manufacturing and important to all stake-holders of a supply chain. Thus, traceability systems are needed to enable traceability in the manufacturing industry. The goal of this work is to map existing knowledge on traceability systems by understanding the technology, requirements and benefits associated with these systems. For this work, academic literature discussing traceability and traceability systems in the manufacturing industry was examined using the Systematic Literature Review process. Out of 561 analysed sources, 62 were accepted into the full review. To verify the results of the litera-ture review, a survey to Finnish industry practitioners was conducted using Elomatic Oy cus-tomer contacts. The results show that the most common traceability system benefits discussed in academic literature were increased production efficiency, ability to handle production errors, increased product and production safety, higher customer trust, more efficient recalls, and improved quality assurance. The survey results showed high support for each of these benefits, although seemingly with slightly different prioritization. The most common technologies associated with traceability systems discussed in the academic literature were RFID, blockchain, IoT, QR codes, and barcodes. Additionally, cloud services were often also discussed in literature. The survey results showed support for the use of barcodes and cloud services in enabling traceability. Other surveyed technologies were not widely used in the participants’ companies. The most common requirements associated with traceability systems discussed in the academic literature were the ability to trace and track traceable resource units and the ability to identify them, the ability to share traceability information, the ability to integrate data from different sources, and the ability of maintaining a production history. An important non-functional requirement was the compliance with necessary requirements. The survey results showed high support for each of these requirements. Further research is required to better understand the current market of traceability systems, the prevalent systems used and the economics of traceability systems in general. The literature review conducted for this work did not find enough information on these aspects, and they were not addressed in the survey

Blockchain: Applications, Effects and Challenges in Supply Chains

The main objective of this research is to examine the potential use cases of blockchain technology in supply chains and its impacts in supply chain management. Moreover, the main challenges that companies are currently facing to introduce this technology are also discussed. In order to form a comprehensive picture of current supply chain digitalization issues and better understand the blockchain technology, a literature review was undertaken. Subsequently, a theoretical framework is formulated with the aim to explore the applications of blockchain to improve supply chain management. Through multiple study cases, the framework is tested with an explanatory approach, providing also a base to identify the main effects and challenges in its implementation in supply chains

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

Analysis of Linoprint distribution channels, development of marketing concepts and tools

PFC del programa Erasmus EPS elaborat a Fachhochschule Kiel University of Applied SciencesTreball desenvolupat dins el marc del programa 'European Project Semester'.The objective of this report is to analyze the effectiveness of current Linoprint distributors and potential new distributors, to analyze Linoprint’s company strengths compared to its competitors in the digital printing market, to develop Microsoft Excel based marketing tools, and to propose marketing concepts to Linoprint by analyzing Linoprint’s current marketing material and the package printing industry, which is the market that Linoprint is active in. The results of the project are used to develop tools and recommendations to improve Linoprint’s marketing and current company positioning, and to recommend strategies for Linoprint to consider as it enters the market

Development of traceability solution for furniture components

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIn the contemporary context, characterized by intensified global competition and the constant evolution of the globalization landscape, it becomes imperative for industries, including Small and Medium Enterprises (SMEs), to undertake efforts to enhance their operational processes, often through digital technological adaptation. The present study falls within the scope of the project named “Wood Work 4.0,” which aims to infuse innovation into the wood furniture manufacturing industry through process optimization and the adoption of digital technologies. This project received funding from the European Union Development Fund, in collaboration with the North 2020 Regional Program, and was carried out at the Carpintaria Mofreita company, located in Macedo de Cavaleiros, Portugal. In this regard, this study introduces a software architecture that supports the traceability of projects in the wood furniture industry and simultaneously employs a system to identify and manage material leftovers, aiming for more efficient waste management. For the development of this software architecture, an approach that integrates the Fiware platform, specialized in systems for the Internet of Things (IoT), with an Application Programming Interface (API) specifically created to manage information about users, projects, and associated media files, was adopted. The material leftovers identification system employs image processing techniques to extract geometric characteristics of the materials. Additionally, these data are integrated into the company’s database. In this way, it was possible to develop an architecture that allows not only the capturing of project information but also its effective management. In the case of material leftovers identification, the system was able to establish, with a satisfactory degree of accuracy, the dimensions of the materials, enabling the insertion of these data into the company’s database for resource management and optimization.No contexto contemporâneo, marcado por uma competição global intensificada e pela constante evolução do cenário de globalização, torna-se imperativo para as indústrias, incluindo as Pequenas e Médias Empresas (PMEs), empreender esforços para aprimorar seus processos operacionais, frequentemente pela via da adaptação tecnológica digital. O presente estudo insere-se dentro do escopo do projeto denominado “Wood Work 4.0”, cujo propósito é infundir inovação na indústria de fabricação de móveis de madeira por meio da otimização de processos e da adoção de tecnologias digitais. Este projeto obteve financiamento do Fundo de Desenvolvimento da União Europeia, em colaboração com o programa Regional do Norte 2020 e foi realizado na empresa Carpintaria Mofreita, localizada em Macedo de Cavaleiros, Portugal. Nesse sentido, este estudo introduz uma arquitetura de software que oferece suporte à rastreabilidade de projetos na indústria de móveis de madeira, e simultaneamente emprega um sistema para identificar e gerenciar sobras de material, objetivando uma gestão de resíduos mais eficiente. Para o desenvolvimento dessa arquitetura de software, adotou-se uma abordagem que integra a plataforma Fiware, especializada em sistemas para a Internet das Coisas (IoT), com uma Interface de Programação de Aplicações (API) criada especificamente para gerenciar informações de usuários, projetos, e arquivos de mídia associados. O sistema de identificação de sobras de material emprega técnicas de processamento de imagem para extrair características geométricas dos materiais. Adicionalmente, esses dados são integrados ao banco de dados da empresa. Desta forma, foi possível desenvolver uma arquitetura que permite não só capturar informações de projetos, mas também gerenciá-las de forma eficaz. No caso da identificação de sobras de material, o sistema foi capaz de estabelecer, com um grau de precisão satisfatório, as dimensões dos materiais, possibilitando a inserção desses dados no banco de dados da empresa para gestão e otimização do uso de recursos

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems