2021
Authors
Pessot, E; Macchion, L; Marchiori, I; Fornasiero, R; Senna, P; Vinelli, A;
Publication
BOOSTING COLLABORATIVE NETWORKS 4.0: 21ST IFIP WG 5.5 WORKING CONFERENCE ON VIRTUAL ENTERPRISES, PRO-VE 2020
Abstract
This paper focuses on the identification of collaborative strategies and practices adopted by companies of the fashion industry in the management of customized offerings (both products and services) along their supply chain (SC). A multiple case study approach is applied and four companies (both medium and large) were interviewed. The cross-case analysis enabled mapping the cases following two dimensions: type of market asking for the customization (B2B vs. B2C) and scope of customization (products vs. services). The analysis highlights the practices and processes related to the customization, the enabling technologies adopted, and the actors involved by a focal company in the collaboration (both in upstream and downstream networks) to offer the product or service that meet customer needs.
2021
Authors
Barros, AC; Senna, PP; Marchiori, I; Kalaitzi, D; Balech, S;
Publication
Lecture Notes in Management and Industrial Engineering - Next Generation Supply Chains
Abstract
2021
Authors
Zimmermann, R; Barros, AC; Senna, PP; Pessot, E; Marchiori, I; Fornasiero, R;
Publication
Lecture Notes in Management and Industrial Engineering - Next Generation Supply Chains
Abstract
2021
Authors
Stute, M; Sardesai, S; Parlings, M; Senna, PP; Fornasiero, R; Balech, S;
Publication
Lecture Notes in Management and Industrial Engineering - Next Generation Supply Chains
Abstract
2021
Authors
Senna, PP; Stute, M; Balech, S; Zangiacomi, A;
Publication
Lecture Notes in Management and Industrial Engineering - Next Generation Supply Chains
Abstract
2021
Authors
Dias, RC; Senna, PP; Goncalves, AF; Reis, J; Michalaros, N; Alexopoulos, K; Gomes, M;
Publication
IFAC PAPERSONLINE
Abstract
Zero Defects is one of the ultimate targets for manufacturing quality control and assurance. Such systems are becoming common in advanced manufacturing industries but are at an initial stage in more traditional industrial sectors, such as wood panels, laminates production, pulp and paper processing and composite panels production. This paper proposes the PREFAB framework, applied to the wood based panels industry, to minimize rejected products using AI, machine learning and IoT devices. The framework was built through action research with a Portuguese wood-based panel manufacturing. This framework delivered an innovative decision support system that provides relevant and timely recommendations for shopfloor decision making and to support process/product engineering. Copyright (C) 2021 The Authors.
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