Traditionally, environmental impacts of man made products have been determined by performing a life cycle assessment (LCA) on the product. As the name implies, LCA is usually covers the entire life of the product in a so-called "cradle-to-grave" assessment. In determining environmental impacts over the whole product life, LCA's are reasonably adequate. However, in providing detailed impacts on a particular phase of life, LCA's are lacking. Detailed assessments are important because very few stakeholders have influence over a product during all phases of life. Stakeholders need detailed impact assessments in their particular phase of life. More detailed assessments give stakeholders more information that can be used for better environmental management (EM) and more environmentally benign operations. In many LCA's, the manufacturing phase of life has been over-generalized and over-simplified because of its relatively small environmental impact, as compared to other phases of life. Nevertheless, certain stakeholders, such as manufacturing companies, need detailed impact information for the manufacturing phase of life so that they can create a more sustainable manufacturing process. Most traditional LCA's use a case-based approach, which was deemed to be inadequate. For these LCA's, the information provided for each case is often quite detailed and specific. However, this makes the assessment less flexible, limiting the quality of the assessment to the degree that the current scenario matches the existing cases. In order to make a more user-specific assessment, a model-based approach was used. To give the model flexibility, a parametric model was created based on mathematical equations that represent various parts of the manufacturing process. To give the model structure, an activity-based costing (ABC) approach was used. Using the ABC structure, the manufacturing process was broken down into activities, each of which was characterized by mathematical models. Large models would be difficult to construct and simulate by hand, so a model was built with the aid of a computer. The modeling language SysML (Systems Modeling Language) was used to create an object-oriented model of the manufacturing process, using the ABC structure. SysML defines overall properties and behaviors of the various elements in the model, while the plug-in tool ParaMagic was used to execute the model via a Mathematica Solver. The model computes carbon dioxide emissions, energy consumption, and waste mass generation for a particular manufacturing scenario. The goal of the model was to quantify environmental impact factors in order to aid manufacturing stakeholders in EM. The overall goal of the research was to determine whether an activity-based, object-oriented model was a valid approach, and whether the computer-aided tools adequately implemented this approach. Findings show that SysML is capable of modeling large and complex systems. However, due to some limitations of Paramagic, only some of SysML's capabilities were utilized. Nevertheless, Paramagic is capable of extracting information out of a manufacturing model built in SysML, and solving parametric relations in Mathematica in a timely manner. Timely solutions of complex models are critical for stakeholders keeping a competitive edge.

The early stages of a product’s design are a critical time for decisions that impact the entire life-cycle cost. Product designers have mastered the first generation; however, they currently do not have the ability to know the impact of their decisions on the multi-generational view. This thesis aims at closing the gap between total life-cycle information and the traditional design process in order to harbor sustainable value creation among all stakeholders involved. A framework is presented that uses a combination of a life-cycle costing methodology and an evolutionary algorithm in order to achieve a sustainability assessment for a true multi-generational component. An illustration of the implementation of the framework shows the value to current engineering scenarios. A foundation is also laid for the overall future vision of this work to utilize proper databases and existing design tools to evaluate the overall sustainability and life-cycle cost of multi-generational components.

<div>Machine tools are an important piece of manufacturing equipment that are widely used throughout many industries. Machine tools shape and form raw materials into desired products through processes such as grinding, cutting, bending, and forming, and when they perform these operations, they consume large amounts of energy. Due to the significant energy consumption, machine tools have a large environmental footprint. Addressing the environmental footprint of machine tools through energy reduction is important to addressing manufacturing and industry’s footprint. One strategy with great potential to reduce machine tool energy consumption is lightweighting. Lightweighting is a design strategy that reduces the mass of moving components with a goal of reducing energy consumption. This strategy is effective since a greater mass requires more energy to move. Lightweighting has had great success in the transportation sector where lightweight materials and lightweight design strategies have been implemented. There has been some work to explore the potential benefits of lightweighting machine tools, however an in-depth study relating mass to energy consumption in machine tools along with exploring other potential concerns, i.e., impact on dynamics and cost, is required.</div><div>To explore the lightweighting of machine tools, a lightweighting application along with models are proposed to investigate the connection between mass and energy in machine tools and potential concerns associated with lightweighting, i.e., decreased dynamic performance and increased machine tool cost. First, a method to reduce the mass of a vertical milling machine tool table is proposed. This method will include the implementation of a sandwich panel for the table while optimizing the structure of the table to maximize its strength and minimize its mass. Following, to link mass to energy consumption, an energy model is proposed to quantify the energy required to drive the table throughout the use of the machine, including cutting and non-cutting moves. In addition to modeling energy, this model will explore the role of motor sizing in the energy consumption of the drive system. To address dynamic concerns resulting from lightweighting, a dynamic model is proposed. This model will provide insight into the dynamic performance of the table and explore the impact of lightweighting on machine tool performance. Finally, a cost model of machine tools is proposed to study the impact of lightweighting on cost. Machine tool cost drivers will be explored along with the role that design complexity has on purchase price.</div><div>This dissertation provided a proof of concept for a lightweighting application through the sandwich panel design of the slide table. The energy model built considering the lightweight table provided a link between the mass and energy consumption in the machine tool. It was shown that more than 30% of the drive system energy could be saved by lightweighting the table. A 30% savings is substantial, especially if applied to multiple systems throughout the machine tool. The static and dynamic models showed that designing lightweight components can be accomplished without sacrificing performance. Various design tools, e.g., finite element analysis, can be used to address static and dynamic concerns. The cost model showed how lightweighting will not increase the cost of the machine tool and therefore will not discourage machine builders from implementing lightweighting to reduce energy consumption.</div><div>The contributions of this research are summarized as follows:</div><div>1.A shape optimization method to design the sandwich panel table, accomplished through a genetic algorithm. This provides a lower-cost lightweighting application.</div><div>2.A mechanistic model linking mass to energy consumption. This provides insight into design considerations required to implement lightweighting</div><div>3.Static and dynamic models of the milling machine slide table. These provide understanding of how lightweighting affects the performance machine tools</div><div>4.A cost model of milling machines. This provides insight into how lightweighting affects the machine tool cost</div><div><br></div>

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As a result of digitalization, previously analog systems in the manufacturing industry have become digitalized, including the decision-making processes. Companies are, therefore,becoming more dependent on data for strategic decisions. However, because of the rapid development of digitalization, companies are left blindfolded in the path towards smarter manufacturing which often leads to unsuccessful technological implementations. Therefore, the thesis will explore this problem by asking: What are the required initiatives for successfully implementing digital data-driven decision-making to improve efficiency and sustainability by Swedish manufacturing companies? To answer the research questions, an exploratory multiple case study approach was conducted, where interviews with informants from the industry as well as researchers within the context of smarter manufacturing were made. The findings were then used to derive propositions which worked as the foundation of a conceptual model which functionality would be to illuminate the results in the form of a strategy map. Findings suggest that it is not always necessary for companies to implement technologies linked to large investments to enable digital data-driven decision-making. However, for those that do, there needs to be a clear organizational plan and agenda before executing theprojects since they otherwise often lead to insufficient results. That means, the technological aspects are often not the culprit in failed digital data-driven decision-makingprojects. Additional findings suggest that there are synergies connected to digital data-driven decision-making such as data-sharing possibilities that have the potential of becoming a major aspect within the context of sustainability and efficiency. / Som ett resultat av ökad digitalisering har analoga system i tillverkningsindustrin blivit digitaliserade, vilket inkluderar beslutsfattandet. Företag har därför börjat förlita sig alltmer på data för sina strategiska beslut. Men på grund av den snabba utveckling av digitalisering har tillverkningsföretagen lämnats utan klara riktlinjer för hur de bör gå tillväga för att implementera digitalt datadrivet beslutsfattande på ett effektivt men hållbart sätt. Avhandlingen kommer därför att undersöka detta problem genom att fråga: Vilka är de initiativ som krävs för att framgångsrikt implementera digital datadrivet beslutsfattande med målet att förbättra effektiviteten och hållbarheten hos svenska tillverkningsföretag? För att svara på forskningsfrågorna användes en undersökande metod med flerafallstudier, där intervjuer gjordes med informanter från industrin såväl som forskare inom ramen för smartare tillverkning. Resultaten användes sedan för att härleda förslag som därefter användes till konstruktionen av en konceptuell model vars huvuduppgift var att illustrera resultaten i form av en strategikarta. Slutsatserna pekar på att det inte alltid är nödvändigt för företag att implementera teknik kopplad till stora investeringar för att möjliggöra digitalt datadrivet beslutsfattande. Men för de som valt att implementera sådana system behövs en tydlig organisationsplan innan projekten genomförs eftersom de annars ofta leder till ofördelaktiga resultat. Detta tyder på att de tekniska aspekterna oftast inte är vad som orsakar misslyckade datadrivna beslutsprojekt. Dessutom tyder resultaten på att det finns synergier kopplade till digitalt datadrivet beslutsfattande, till exempel möjligheter att dela data som har potential att bli en viktig aspekt inom hållbarhet och effektivitet.

A paradigm shift is taking place in the manufacturing industry, where companies strive for adopting digital tools to be able to compete against their competitors. The endeavor of becoming digitized is taking place simultaneously as the global awareness of sustainability increases. For the reasons that current literature is experiencing a knowledge gap that links data-driven processes, sustainability, and supply chain operations, there is a need for further exploration within this area. Therefore, the aim of this report is to investigate the business opportunities and challenges of data-driven decision-making, and how it relates to more sustainable supply chain operations within the manufacturing industry. To investigate the area within data-driven decision-making and its impact on manufacturing supply chain operations, a literature review was initially conducted and was followed by interview sessions with case companies and experts. In total, 14 interviews were conducted within the area of sustainability, supply chain operations, and data-driven decision-making. The interviews were conducted to follow the designed framework and thus provide knowledge for the challenges, advantages, applications, and value capture in relation to data-driven decision-making and supply chain operations. Comparing the empirical data with previous literature it was noted that data-driven decision-making entails both multiple challenges and advantages when it comes to improving manufacturers' sustainable performance. The main challenges include establishing efficient information sharing, standardized systems, and obtaining data that shows both reliability and validity. Consequently, by solving these challenges the sustainable benefits can be fulfilled, including a mitigated bullwhip-effect, improved planning, and reduced CO2 emissions. These benefits are driven by the transparency, automatization, and optimization that is incorporated with data-driven decision-making. In conclusion, realizing data-driven decision-making within the manufacturing industry entails several challenges, but if companies overcome the challenges the potential benefits will be unlimited. / Ett paradigmskifte pågår för närvarande i tillverkningsindustrin, där företag strävar efter att använda digitala verktyg för att kunna konkurrera mot sina konkurrenter. Strävan efter att bli digitaliserad sker samtidigt som den globala medvetenheten om hållbarhet ökar. Av anledningarna till att den aktuella litteraturen upplever ett tomrum av kunskap som länkar datadrivna processer, hållbarhet och leveranskedjedrift, så finns det ett behov av ytterligare forskning inom detta område. Målet med denna rapport är därför att undersöka affärsmöjligheterna och utmaningarna med datadrivet beslutsfattande, och hur det relaterar till mer hållbara försörjningskedjor inom tillverkningsindustrin. För att undersöka området inom datadrivet beslutsfattande och dess inverkan på leveranskedjedriften och tillverkningsindustrin så genomfördes först en litteraturundersökning som följdes av intervjussessioner med utvalda företag och experter inom området. Sammanlagt intervjuades nio företag och sex experter som valdes ut efter deras kompetenser inom hållbarhet, leveranskedjedrift och datadrivet beslutsfattande. Intervjuerna genomfördes med hjälp av en intervjuguide och därmed ge kunskap om kopplingarna mellan data, aktuella affärsverksamheter och förbättrad ekonomisk, social och miljöprestanda. Detta inkluderar att utforska utmaningar, fördelar, applikationer och värdefångst i kontext till datadrivet beslutsfattande och leveranskedjedrift. Vid analysen av EMPIRISK data och jämförelse med aktuell litteratur noterades det att datadrivet beslutsfattande medför flera olika utmaningar och fördelar när det gäller att förbättra tillverkningsföretagens hållbara prestanda. De viktigaste utmaningarna är att etablera effektiv informationsdelning, standardiserade system och att erhålla data som visar både tillförlitlighet och giltighet. Genom att hantera dessa utmaningar kan de hållbara fördelarna uppnås, vilket inkluderar en minskad bullwhip-effekt, koldioxidutsläpp och förbättrad planering. Dessa fördelar drivs vidare av transparens, automatisering och optimering som ett datadrivet beslutsfattande medför. Sammanfattningsvis innebär förverkligandet av att använda datadrivet beslutsfattande inom tillverkningsindustrin flera utmaningar, men om företag övervinner utmaningarna kommer de potentiella fördelarna att vara obegränsade.

Nous présentons les enjeux du management environnemental et soulignons l’importance d’une politique d’économie d’énergie pour les entreprises. Nous proposons un modèle pour déterminer le bilan énergétique de la fabrication en intégrant les différentes phases productives et non-productives. Nous définissons un double objectif pour la minimisation de la durée de production et de la consommation d’énergie. Nous appliquons ce modèle à l’ordonnancement d’ateliers job-shop flexibles. Pour déterminer la solution optimale nous utilisons deux classes de méthodes : - La première relève des algorithmes génétiques. Nous proposons différents types d’algorithmes pour résoudre ce problème multicritère. Nous proposons par exemple de faire évoluer deux populations pour minimiser respectivement l’énergie consommée et la durée de production et de les croiser pour atteindre l’objectif global. - La seconde relève de la programmation sous contrainte. Nous proposons de rechercher la solution optimale en développant une double arborescence pour évaluer l’énergie consommée et la durée de production. Nous construisons notre algorithme en partant des tâches à réaliser sur les machines ou en partant des machines qui réaliseront les tâches. Nous discutons de la construction du front de Pareto pour l’obtention de la meilleure solution.Nous terminons en comparant les différentes approches et en discutant leur pertinence pour traiter des problèmes de différentes tailles. Nous proposons également plusieurs améliorations et quelques pistes pour de futures recherches. / We present the challenges of environmental management and underline the importance of an energy saving policy for companies. We propose a model to determine the energy balance of manufacturing by integrating the different productive and non-productive phases. We define two purposes for minimizing production time and energy consumption. We apply this model to the scheduling of flexible job-shop workshops. To determine the optimal solution we use two types of methods: - The first is genetic algorithms. We propose different types of algorithms to solve this multi-criteria problem. For example, we propose to develop two populations to minimize the energy consumed and the production time, and to cross them to achieve the overall objective. - The second is constraint programming. We propose to find the optimal solution by developing a double tree to evaluate the energy consumed and the production time. We build our algorithm starting from the tasks to be performed on the machines or from the machines that will perform the tasks. We discuss the construction of the Pareto front to get the best solution.We finish by comparing the different approaches and discussing their relevance to deal with problems of different sizes. We also offer several improvements and some leads for future research.

O conceito de Green Manufacturing (GM) se remete à manufatura ambientalmente sustentável de produtos. As pesquisas envolvendo GM tem se intensificado nos últimos anos, com destaque para a utilização da técnica de Avaliação do Ciclo de Vida (ACV). Contudo, a adoção prática da GM pelas indústrias de manufatura ainda esbarra em diversas limitações. Entre as barreiras gerenciais, diversos autores destacam que o uso isolado da ACV e de outras técnicas e ferramentas muitas vezes se mostra limitado em prol de uma adequada promoção da GM no ambiente fabril. Assim, visando superar este tipo de barreira, a presente tese de doutorado foi proposta. Seu objetivo foi o de desenvolver e testar um novo procedimento de GM para a avaliação e o monitoramento ambiental de processos de manufatura. Para isso, uma pesquisa bibliográfica foi realizada, onde ao todo 333 artigos foram selecionados e analisados, e diversas limitações foram identificadas nos mesmos, as quais serviram de base para o desenvolvimento do novo procedimento de GM desta tese. Como resultado, o procedimento desenvolvido foi organizado em dez etapas distintas, todas distribuídas em três macroetapas: pré-avaliação, avaliação e monitoramento ambiental, e pós-avaliação. Sobre o teste prático do procedimento, foram realizados dois estudos de caso: 1) uma empresa fabricante de painéis de madeira aglomerada para uso na produção de móveis residenciais e comerciais; e 2) uma empresa produtora de válvulas para uso em motores de veículos automotivos. O estudo 1) mostrou que o processo de prensagem à quente dos painéis foi destaque, especialmente devido ao consumo de eletricidade e as emissões ao ar de formaldeído livre geradas pelo processo. A prensagem à quente apresentou potencial para afetar não somente os impactos do processo de fabricação, mas também os impactos a montante e a jusante da manufatura. Já no estudo 2), foi destaque o processo de retificação centerless da haste das válvulas, devido ao consumo de eletricidade e de fluido de corte na retificação. O processo de retificação das válvulas não se mostrou capaz de influenciar significativamente os impactos de outras etapas do ciclo de vida do produto (e.g., uso, pós-uso). Finalmente, a partir dos resultados do seu teste prático, três versões simplificadas foram propostas para o procedimento de GM originalmente desenvolvido. Tais versões simplificadas se mostraram vantajosas, pois permitem uma melhor aplicabilidade do procedimento de GM no ambiente fabril. / Green manufacturing (GM) is the environmental benign manufacturing of products with a minimal negative impact on the natural environment. Research studies on GM have been increased in the last years with particular focus on the application of Life Cycle Assessment (LCA). However, the manufacturing industry still faces some barriers and challenges that hinder the application of green manufacturing in practice. Regarding to managerial barriers, many authors highlight that the isolated use of LCA and other tools/techniques are limited in view of supporting the promotion of an effective implementation of GM at the shop floor area. Thus, this thesis was proposed to overcome part of such gaps. The present study was aimed at developing and testing a new GM methodological framework to environmentally evaluate and monitor manufacturing processes. For this, the GM methodological framework was based on a bibliographical research, where 333 articles were found and systematically analyzed. As result, the GM methodological framework was organized into ten different steps, and three main phases: pre-assessment, environmental assessment and monitoring, and post-assessment. Concerning the practical testing of the GM methodological framework, two case studies were carried out: 1) the production of medium density particleboards for consumption by the residential and commercial wood furniture sector; and 2) the production of exhaust valves for using by automotive vehicles. Case study 1) highlighted the hot pressing process of wood-based panels, due to the consumption of electricity and the local airborne emissions of free formaldehyde. The hot pressing process showed potential to affect not only into the manufacturing environmental impacts, but also into the upstream and downstream life cycle stages. Case study 2) pointed out the centerless grinding machining of the stem of automotive valves, due to the electricity and cutting fluid consumption. However, the grinding machining process was not effective to influence into the environmental impacts of other product\'s life cycle stages (e.g., use, post-use). Based on the case studies results, it was proposed three simplified versions of the original GM methodological framework in order to improve its applicability into manufacturing industries.

O conceito de Green Manufacturing (GM) se remete à manufatura ambientalmente sustentável de produtos. As pesquisas envolvendo GM tem se intensificado nos últimos anos, com destaque para a utilização da técnica de Avaliação do Ciclo de Vida (ACV). Contudo, a adoção prática da GM pelas indústrias de manufatura ainda esbarra em diversas limitações. Entre as barreiras gerenciais, diversos autores destacam que o uso isolado da ACV e de outras técnicas e ferramentas muitas vezes se mostra limitado em prol de uma adequada promoção da GM no ambiente fabril. Assim, visando superar este tipo de barreira, a presente tese de doutorado foi proposta. Seu objetivo foi o de desenvolver e testar um novo procedimento de GM para a avaliação e o monitoramento ambiental de processos de manufatura. Para isso, uma pesquisa bibliográfica foi realizada, onde ao todo 333 artigos foram selecionados e analisados, e diversas limitações foram identificadas nos mesmos, as quais serviram de base para o desenvolvimento do novo procedimento de GM desta tese. Como resultado, o procedimento desenvolvido foi organizado em dez etapas distintas, todas distribuídas em três macroetapas: pré-avaliação, avaliação e monitoramento ambiental, e pós-avaliação. Sobre o teste prático do procedimento, foram realizados dois estudos de caso: 1) uma empresa fabricante de painéis de madeira aglomerada para uso na produção de móveis residenciais e comerciais; e 2) uma empresa produtora de válvulas para uso em motores de veículos automotivos. O estudo 1) mostrou que o processo de prensagem à quente dos painéis foi destaque, especialmente devido ao consumo de eletricidade e as emissões ao ar de formaldeído livre geradas pelo processo. A prensagem à quente apresentou potencial para afetar não somente os impactos do processo de fabricação, mas também os impactos a montante e a jusante da manufatura. Já no estudo 2), foi destaque o processo de retificação centerless da haste das válvulas, devido ao consumo de eletricidade e de fluido de corte na retificação. O processo de retificação das válvulas não se mostrou capaz de influenciar significativamente os impactos de outras etapas do ciclo de vida do produto (e.g., uso, pós-uso). Finalmente, a partir dos resultados do seu teste prático, três versões simplificadas foram propostas para o procedimento de GM originalmente desenvolvido. Tais versões simplificadas se mostraram vantajosas, pois permitem uma melhor aplicabilidade do procedimento de GM no ambiente fabril. / Green manufacturing (GM) is the environmental benign manufacturing of products with a minimal negative impact on the natural environment. Research studies on GM have been increased in the last years with particular focus on the application of Life Cycle Assessment (LCA). However, the manufacturing industry still faces some barriers and challenges that hinder the application of green manufacturing in practice. Regarding to managerial barriers, many authors highlight that the isolated use of LCA and other tools/techniques are limited in view of supporting the promotion of an effective implementation of GM at the shop floor area. Thus, this thesis was proposed to overcome part of such gaps. The present study was aimed at developing and testing a new GM methodological framework to environmentally evaluate and monitor manufacturing processes. For this, the GM methodological framework was based on a bibliographical research, where 333 articles were found and systematically analyzed. As result, the GM methodological framework was organized into ten different steps, and three main phases: pre-assessment, environmental assessment and monitoring, and post-assessment. Concerning the practical testing of the GM methodological framework, two case studies were carried out: 1) the production of medium density particleboards for consumption by the residential and commercial wood furniture sector; and 2) the production of exhaust valves for using by automotive vehicles. Case study 1) highlighted the hot pressing process of wood-based panels, due to the consumption of electricity and the local airborne emissions of free formaldehyde. The hot pressing process showed potential to affect not only into the manufacturing environmental impacts, but also into the upstream and downstream life cycle stages. Case study 2) pointed out the centerless grinding machining of the stem of automotive valves, due to the electricity and cutting fluid consumption. However, the grinding machining process was not effective to influence into the environmental impacts of other product\'s life cycle stages (e.g., use, post-use). Based on the case studies results, it was proposed three simplified versions of the original GM methodological framework in order to improve its applicability into manufacturing industries.

Current life cycle assessment tools are often deficient in assisting design for sustainable manufacturing efforts. Integrating an improved assessment method into a decision support framework will provide a means for designers and engineers to better understand the impacts of their decisions. A unit process modeling-based sustainability assessment method is presented to assist design decision making by accounting for and quantifying economic, environmental, and social attributes. A set of these sustainability metrics is defined as a basis for comparison of component design alternatives. The method is demonstrated using two titanium component production alternatives that represent typical design for manufacturing scenarios. The modeling method significantly increases the resolution of sustainable manufacturing metrics over conventional assessment techniques, and is one aspect of the overall decision support framework developed. Additionally, fixed sum importance weighting, weighted sum modeling, and scenario analysis were selected as easily employed and transparent design decision techniques to provide the remaining elements of the framework. The demonstration of the decision support framework for titanium component manufacturing illustrates that the sequential approach developed can assist engineers in developing more sustainable components and products. / Graduation date: 2012