Managing Product Life Cycle Data Using Automatic Identification

Kuluttajat ja lainsäätäjät vaativat yhä tarkempaa tietoa tuotteiden alkuperästä ja ympäristökuormituksesta. Yhä monimutkaisemmiksi käyvät tuotteiden tuotantoketjut ovat haasteellisia tämän tiedon keräämisen kannalta. Tuotantoketjussa voi olla satoja eri alihankkijoita sekä lukuisia jatkojalostajia. Tähän asti yleisin tapa laskea tuotteen elinkaaren ympäristövaikutukset on ollut mitata resurssien ja energian käyttö prosesseissa sekä prosessien päästöt ilmaan, maahan ja veteen esimerkiksi vuoden jaksolla ja käyttää saatua lukua keskiarvona kaikille tuotetuille tuotteille. Tämä väitös esittää mallin, joka mahdollistaa yksittäisen tuotteen elinkaaren seuraamisen ja sen elinkaari-informaation keräämisen sekä tämän informaation jakamisen. Esitettyä mallia voidaan käyttää pohjana kehitettäessä luotettava järjestelmä tuotteiden ympäristövaikutusten mittaamiseen ja tämän tiedon jakamiseen kuluttajille. Näin kuluttajat voivat tehdä ostopäätöksensä oikean ja tarkan tiedon perusteella. Työssä on tarkasteltu metsäteollisuutta esimerkkitapauksena, jossa tuotteiden ja komponenttien tunnistaminen perustuu RFID-tekniikkaan. Automaattinen tunnistaminen mahdollistaa jopa yksittäisen tuotteen seuraamisen koko tuotantoketjun läpi ja tarkan elinkaari-informaation keräämisen. Tätä informaatiota käyttämällä yksittäiselle tuotteelle voidaan laskea tarkka ympäristövaikutus.Managing the life cycle of products is becoming more and more important. Organizations are facing increasing pressure from consumers and legislators to accurately measure and manage the environmental impact of products. However, the complexities of today s supply chains pose a challenge for gathering accurate data throughout the life cycle of the product. The life cycle of a product can be defined as a network of entities responsible for the procurement, manufacturing and distribution of the product. In order to enable tracing through the dynamic supply chain, the products must be identified. The development of automatic identification enables us to identify each object in the supply chain and trace it through the complex and dynamic supply chain where each organization manages a part of the chain. Thanks to traceability, we can connect the information about the products' movements with the information about processes. In other words, we can allocate the properties of the processes to the actual product instances involved in each process. To be able to store the life cycle information of products, we must have a model that enables the allocation of life cycle information to the traced product throughout the supply chain. This dissertation defines such a model (traceability graph) that can be used to allocate life cycle information from processes to individual products. Further, the model enables multidimensional analyzes of data associated with the life cycle information of products and their components. The dissertation also specifies a solution for collecting, storing and sharing life cycle information about the product throughout its life cycle, enabling consumers to make educated choices based on accurate information regarding products they are purchasing. The method enables supply chain stakeholders to exchange life cycle information by utilizing the EPCGlobal Network architecture. The case example used in this dissertation is environmental impact information. In recent times, consumers and legislators have become increasingly interested in the environmental impacts of products throughout their life cycle. The biggest challenge with measuring the environmental impact is the fact that supply chains are complex and dynamic. A manufacturer can use various subcontractors and supply various end manufacturers or retailers in different countries. So far, the most common method of calculating the environmental impact of a product has been to measure the resources used, emissions and production for a certain period of time and then calculate the average environmental impact of the product. This work provides methods to monitor environmental performance even at a product level

Formal Definition of Traceability Graph

Data-centric workflows focus on how the data is transferred between processes and how it is logically stored. In addition to traditional workflow analysis, these can be applied to monitoring, tracing, and analyzing data in processes and their mutual relationships. In many applications, e.g. manufacturing, the tracing of products thorough entire lifecycle is becoming more and more important. In the present paper we define the traceability graph that involves a framework for data that adapts to different levels of precision of tracing. Advanced analyzing requires modeling of data in processes and methods for accumulating resources and emissions thorough the lifecycle of products. The traceability graph enables tracing and accumulation of resources, emissions and other information associated with products. The traceability graph is formally defined by set theory that is an established and exact specification method

Formal Definition of Traceability Graph

Data-centric workflows focus on how the data is transferred between processes and how it is logically stored. In addition to traditional workflow analysis, these can be applied to monitoring, tracing, and analyzing data in processes and their mutual relationships. In many applications, e.g. manufacturing, the tracing of products thorough entire lifecycle is becoming more and more important. In the present paper we define the traceability graph that involves a framework for data that adapts to different levels of precision of tracing. Advanced analyzing requires modeling of data in processes and methods for accumulating resources and emissions thorough the lifecycle of products. The traceability graph enables tracing and accumulation of resources, emissions and other information associated with products. The traceability graph is formally defined by set theory that is an established and exact specification method

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202

Regionalized implementation strategy of smart automation within assembly systems in China

Produzierende Unternehmen in aufstrebenden Nationen wie China, sind bestrebt, die Produktivität der Produktion durch eine Verbesserung der Lean Produktion mit disruptiven Technologien zu erreichen. Smart Automation ist dabei eine vielversprechende Lösung, allerdings können Unternehmen aufgrund von mangelnden Ressourcen oft nicht alle Smart Automation Technologien gleichzeitig implementieren. Ebenso beeinflusst eine Vielzahl an Einflussfaktoren, wie z.B. Standortfaktoren. Dementsprechend herausfordernd ist die Auswahl und Priorisierung von Smart Automation Technologien in Form von Einführungsstrategien für produzierende Unternehmen. Der Stand der Forschung untersucht nur unzureichend die Analyse der Interdependenzen zwischen Standortfaktoren, Smart Automation Technologien und Key Performance Indikatoren (KPIs). Darüber hinaus mangelt es an einer Methode zur Ableitung der Einführungsstrategie von Smart Automation Technologien unter Berücksichtigung dieser Interdependenzen. Entsprechend trägt diese Arbeit dazu bei, eine regionalisierte Einführungsstrategie von Smart Automation Technologien in Montagesystemen zu ermöglichen. Zunächst werden die Standortfaktoren, Smart Automation Technologien und KPIs identifiziert. In einem zweiten Schritt werden, mit Hilfe von qualitativen und quantitativen Analysen, die Interdependenzen bestimmt. Anschließend werden diese Interdependenzen auf ein Montagesystem mittels hybrider Modellierung und Simulation übertragen. Im vierten Schritt wird eine regionalisierte Einführungsstrategie durch eine Optimierung und eine Monte-Carlo-Simulation abgeleitet. Die Methodik wurde im Rahmen des deutsch-chinesischen Forschungsprojekts I4TP entwickelt, das vom Bundesministerium für Bildung und Forschung (BMBF) unterstützt wird. Die Validierung wurde erfolgreich mit einem produzierenden Unternehmen in Beijing durchgeführt. Die entwickelte Methodik stellt einen neuartigen Ansatz zur Entscheidungsunterstützung bei der Entwicklung einer regionalisierten Einführungsstrategie für Smart Automation Technologien in Montagesystemen dar. Dadurch sind produzierende Unter-nehmen in der Lage, individuelle Einführungsstrategien für disruptive Technologien auf Basis wissenschaftlicher und rationaler Analysen effektiv abzuleiten

Mapping innovation in the European transport sector : An assessment of R&D efforts and priorities, institutional capacities, drivers and barriers to innovation

The present document provides an overview of the innovation capacity of the European transport sectors. The analysis addresses transport-related innovation from three different angles. It identifies the drivers and barriers to innovation for the main transport sub-sectors; it assesses quantitative indicators through the detailed analysis of the main industrial R&D investors and public R&D priorities in transport; and it identifies the key actors for transport research and knowledge flows between them in order to detect shortcomings in the current institutional set-up of transport innovation. The analysis finds that despite the significant on-going research efforts in transport, largely driven by the automotive industry, the potential for systemic innovations that go beyond modal boundaries and leave the currently pre-dominant design are under-exploited due to prominent lock-in effects caused by infrastructure and the institutional set-up of the innovation systemsJRC.J.1-Economics of Climate Change, Energy and Transpor