In industrial design, additive manufacturing technology is one of the key technologies that have changed the way of producing metal component parts on short demand. Because of competitiveness among industries and the requirement to keep up with thegrowth of thesmart factory technology, the industries are pushed to step up and take further steps towards industry 4.0. Today the AM technology is used at prototype scale, but previous literature says that for the technology to reach the full capacity, it needs to be scaled up. Previous literature shows that improvements in the supply chain are necessary in order to scale up the industrial production and achieve high-scale adoption of the technology. As there are few sourcesin the literature about AM scalability or finding critical improvements in terms of lead times, costs and material consumptions, this study will fill that gap. The main objective of this research is to study small-scale 3D printing in the AM industries with two main industrial objectives in mind: 1 –Understanding the problem of optimization of a small-scale 3D printing operation in the industry and 2 –projecting a scenario regarding the scaling up of such facilities to reach full industrial production capacity. The method used for finding improvements in the additive manufacturing supply chain was optimization. I have developed the Overall Material Flow Effectiveness model (OMFE), which is an optimization model that takes into consideration the relevantparameters of the AM material flow regarding lead times, costs and material consumption. A literature review was conducted to determine the research design and what has and not been investigated. A sensitivity analysis was performed, which provided information aboutissues of scale, size and significance of optimizing a prototyping model,andalso aboutanalyzing the optimization model development in terms of evaluating the prototyping, making it better and scaling up to high-level production. The optimal material flow of the AM industry is a scaled-up production with implemented improvements regarding transport and cost. By comparing it with the current prototype production, it is possible to identifythat all of the OMFE related factors have higher percentages. The top losses within the current AM industry are related to non-human processes. The most significant optimization loss is the loss of transport, where the time from supplier to goods reception have a significant influence. The second largestlossis cost,generated bylabour management.

I dagens produktion blir effektivitet och minimering av slöserier allt större. Vid uppstart av en produktionsenhet är det mycket planering, testning och avgörande avvägningar. Vid installation och uppbyggnad av line, produktionsbalanser och stationer behövs olika scenarion ställas upp för att vara förberedd inför produktionsstart. Den mall som tagits fram under arbetet kommer ligga till grund för ett förbättringsarbete för en mer effektiv produktion hos Azelio i Uddevalla. Denna rapport består av en analys kring ett produktionstekniskt projekt för att analysera de påverkande faktorer på effektiviteten hos balanserna. Syftet med arbetet är att ta fram en modell som möjliggör en visualisering av de parametrar som kan störa framtida produktionsflöde utifrån risker och omständigheter runt produktionen. Med de kunskaper som tidigare samlats ihop under studietiden kunde dessa kunskaper tillämpas i detta arbete. Arbetet resulterade sedan i en mall som visuellt kunde redovisa huruvida effektiviteten var kring balanserna i produktionen. Inledningsvis genomfördes en nulägesanalys för produktionsflödet huruvida produkten är tänkt att färdas igenom produktionen och vad som skedde vid varje balans. Detta möjliggjorde en grundförståelse för produktionen och produkten. Sekundärdata som behövdes för arbetet var samlat i ett excellark där de data som företaget gjort uppskattningar och mätningar på har samlats. På detta vis kunde senare primärdata införskaffas och förvaltas. Slutligen lades sekundärdata till grund för utformningen av modellen som tagits fram under arbetets gång. Modellen resulterade i en visualisering av effektiviteten hos balanserna med hänsyn på sex olika parametrar som vid olika omständigheter kan komma att påverka effektiviteten i fabriken. Hur pass mycket de olika parametrarna kommer att påverka den slutliga produktionen är i nuläget svårt att fastställa, det kommer att utvärderas när produktionen är igång. / In today’s production, effectivity and minimizing the waste are increasing. When starting up a production unit, there is a lot of planning, testing and decisive considerations. When installing and building the line, production balances and stations, different scenarios are needed to be prepared for production start. The model developed during the work will form the basis for improvement work for a more efficient production at Azelio in Uddevalla. This study consists of an analysis of a production engineering project to analyse the influencing factors on the efficiency of the balances. The purpose of the work is to develop a model that allows visualization of the parameters that may interfere with future production flows based on risks and circumstances surrounding the production. With the knowledge previously gathered during the study period, these knowledges could be applied in this work. The work then resulted in a template that could visually show whether the efficiency was around the balances in the production. Initially, a current situation analysis for the production flow was carried out, whether the product is intended to pass through production and what happened at each balance. This enabled a basic understanding of the production and the product. Secondary data was collected in an Excell sheet where the data which the company made estimates and measurements were collected. Later in this way primary data could be obtained and managed. Finally, secondary data was used as the basis for the design of the model that was developed during the work. The model resulted in a visualization of the efficiency of the balances, taking into account six different parameters that may affect the efficiency of the factory in different circumstances. How much the various parameters will affect the final production is difficult to determine at present, but it will be evaluated when production is in progress.

A manufacturer would love to see progress in the optimization of a production system to maximize profit while maintaining the environmental regulations enacted by a government or society. Thus, a key performance indicator is needed to indicate how the system production is performing in monetary as well as environmental aspects. However, the manufacturer and the regulating body may not be aware of the environmental impacts associated with the production system as a key performance indicator like Overall Equipment Effectiveness (OEE) only implies the monetary aspects of the process. Therefore, there is a need to quantify the carbon emissions of the process and use it as a parameter that indicates profitability and environmental sustainability at the same time. From the public and policy-making body's point of view, they need a proper scale that can be used to track the compliance of the manufacturers with the environmental regulations. In this study, this carbon-based indicator (COEE) aims to discover a way of monitoring the progress of a process with environmental considerations. This study raises key questions that are constructive to each other to build a comparison of the ordinary OEE and environmental COEE. To do so a case study about the plastic process using an injection molding machine is conducted. The data used in this study was provided by the company named Good Solutions. Based on the data provided, the ordinary OEE of the machine for each shift is used to contrast with the result of the new modeled COEE. The RMSE for the given OEE of the machine was 13.424 while for COEE is 12.695. The RMSE of both OEEs indicates that the COEE can be used as an indicator for economic as well as environmental assessments.

The purpose of this study is to evaluate the use of OEE as a key performance indicator in the sawmill industry to achieve a reliable plant that is cost effective and resource efficient. By examining how OEE is used in sawmills, and what underlying factors affect OEE, an understanding is given of how the use of OEE can be improved. The overall question in this work is thus the following: How has the sawmill, in this case Moelven Valåsen, implemented OEE? What are the shortcomings and how can these be remedied? The study has shown that the maintenance function should utilize leading and lagging key performance indicators and with these key performance indicators as a basis, overall equipment effectiveness (OEE) can be evaluated and used as a driver for continuous improvement. It is emphasized that when measuring OEE it is important to measure all its input parameters correctly to get maximum value out of OEE. Furthermore, it is stated that low values of OEE need not necessarily be negative, instead the key performance indicator should be used as a benchmark. However, a stable value of OEE is a prerequisite for work with continuous improvements. Thus, the maintenance function must strive to minimize sporadic OEE values to provide a good basis for improvement activities. The use of the key performance indicators that measure maintenance efficiency and production efficiency are deemed to be relevant in the sawmill industry and are a prerequisite for a reliable plant. This in turn is a starting point for the future implementation of Industry 4.0 in the sawmill industry. / Syftet med detta arbete är att utvärdera användningen av OEE som nyckeltal i sågverksindustrin för att uppnå en pålitlig och driftsäker anläggning som är kostnads- och resurseffektiv. Genom att undersöka hur OEE används i sågverk, samt vilka underliggande faktorer som påverkar OEE, ges förståelse om hur användning kan förbättras. Den övergripande frågeställningen i detta arbete är således följande: Hur har sågverk, i detta fall Moelven Valåsen, implementerat OEE? Vilka brister finns och hur kan dessa åtgärdas? Arbetet har visat att underhållsfunktionen bör förhålla sig till ledande och släpande nyckeltal och med dessa nyckeltal som grund kan overall equipment effectiveness (OEE) utvärderas och användas som drivare till ständiga förbättringar. Det understryks att vid mätning av OEE är det viktigt att mäta alla dess ingående parametrar på ett korrekt vis för att få ut maximalt värde ur OEE. Vidare konstateras det att låga värden på OEE inte nödvändigtvis behöver vara negativt, istället bör nyckeltalet användas som ett referensvärde. Dock är ett stabilt värde på OEE en förutsättning för arbete med ständiga förbättringar. Således behöver underhållsfunktionen sträva efter att minimera sporadiska OEE-värden för att ge god grund till förbättringsaktiviteter. Användning av nyckeltal som mäter underhållseffektivitet och produktionseffektivitet är relevanta i sågverksindustrin och en förutsättning för en pålitlig och driftsäker anläggning, vilket i sin tur är en utgångspunkt för framtidens implementering av industri 4.0 i sågverksindustrin.

Manufacturing management and operations place heavy emphasis on monitoring and improving production performance. This supervision is accomplished through strategies of manufacturing performance management, a set of measurements and methods used to monitor production conditions. Over the last thirty years the most prevalent measurement of traditional performance management has been overall equipment effectiveness, a percentile summary metric of a machine's utilization. The technologies encapsulated by Industry 4.0 have expanded the ability to gather, process, and store vast quantities of data, creating opportunity to innovate on how performance is measured. A new method of managing manufacturing performance utilizing Industry 4.0 technologies has been proposed by McKinsey & Company and software tools have been developed by PTC Inc. to aid in performing what they both call digital performance management. To evaluate this new approach, the digital performance management tool was deployed on a Festo Cyber-Physical Lab, an educational mock production environment, and compared to a digitally enabled traditional performance management solution. Results from a multi-day production period displayed an increased level of detail in both the data presented to the user and the insights gained from the digital performance management solution as compared to the traditional approach. The time unit measurements presented by digital performance management paint a clear picture of what and where losses are occurring during production and the impact of those losses. This is contrasted by the single summary metric of a traditional performance management approach, which easily obfuscates the constituent data and requires further investigation to determine what and where production losses are occurring.

The purpose of this study is to investigate and analyze stops that occur in the painting process of trucks; specifically related to the buffer system after the top coat process. Based on this investigation, improvements are presented in order to eliminate stops and improve overall assembly time. With this scope in mind, the following issues have been raised:   1.     What are the root causes for stops in the buffer system? 2.     How much can corrective maintenance be reduced? 3.     What potential savings are achieved by reducing or eliminating stops in the buffer system? 4.     How would reducing stops increase the availability for the top coat paint shop?   A case study has been performed where historical data has been analyzed and staff from the production, maintenance and IT departments have been interviewed. The analyzed data along with a theoretical framework are the basis for the suggested actions that will improve flows through the top coat production. The result indicates that a great part of the delays related to the buffer system are caused by soft losses. The stops are the results of: Lack of a distinct set of rules for how data and software may be used Lack of communication between the workshops The implications of these delays are: increased costs and reduced efficiency of the production line. The purposed actions presented in the study gives the company opportunity to achieve full capacity, eliminate delays and increase overall efficiency. The study is limited to the top coat paint shop and does not include the primary paint shop or final assembly. The results, analysis and suggestions presented in the study includes the soft losses that causes stop in the buffer system. / Syftet med följande studie är att undersöka och analysera stopp och dess rotorsaker som uppkommer i buffertsystemet på ett täcklacksmåleri. Utifrån denna utredning presenteras sedan åtgärdsförslag i syfte att eliminera undersökta stopp. För att kunna uppfylla syftet med studien har följande frågeställningar formulerats:   Vad finns det för stopp relaterade till buffertsystemet och vad är rotorsakerna till dessa? Hur mycket kan det akuta underhållet reduceras? Hur stora besparingar skulle företaget kunna göra genom reducering eller eliminering av stoppen? På vilket sätt kan en minskning av stoppen öka tillgängligheten för täcklacksmåleriet?   En fallstudie har genomförts där historisk data har analyserats och personal från avdelningar som produktion, underhåll och IT har intervjuats. Den empiri som samlats in tillsammans med det teoretiska ramverket som presenteras i rapporten ligger till grund för resultatet, analysen och åtgärdsförslagen.   Resultatet från studien visar att en stor andel av de stopp som sker i buffertsystemet orsakas av mjuka förluster. Dessa stopp är en konsekvens av bristfällig hantering av data och mjukvara samt bristfällig kommunikation mellan verkstäderna. Konsekvenserna av dessa stopp resulterar i ökade kostnader samt en nedsatt effektivitet för täcklacksmåleriet. Åtgärdsförslagen som presenteras ger företaget goda möjligheter att uppnå sin fulla kapacitet, eliminera stoppen och därmed öka effektiviteten.   Studien är avgränsad till måleriprocessen och omfattas inte av grundmåleriet eller monteringen. De resultat, analyser och åtgärdsförslag som presenteras är avgränsade till de mjuka förlusterna som orsakar stopp i buffertsystemet.

Made available in DSpace on 2016-06-23T12:15:51Z (GMT). No. of bitstreams: 2 12.pdf: 3444028 bytes, checksum: 73c0e8984bffa4bb547494ec30407d6f (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2013 / Made available in DSpace on 2016-07-05T22:37:59Z (GMT). No. of bitstreams: 3 12.pdf.txt: 204813 bytes, checksum: 9eae4f603bf69f1c3e4907b919ce4332 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) 12.pdf: 3444028 bytes, checksum: 73c0e8984bffa4bb547494ec30407d6f (MD5) Previous issue date: 2013 / A concorrência no setor farmacêutico é intensa e os produtos farmacêuticos possuem tempo de ciclo reduzido quando comparado com os grandes investimentos de P&D. Para concorrer neste setor, as indústrias estão sempre com foco em melhorar a eficiência dos processos de manufatura utilizando muitas vezes equipamentos complexos, modernos e automatizados com foco na produção para atingir as metas. Neste contexto, o objetivo deste trabalho é demonstrar o melhor controle da produção utilizando como base a metodologia de Manutenção Produtiva Total da Produção buscando a maximização da eficiência dos equipamentos e recursos disponíveis, utilizando várias ferramentas dentre elas a Overall Equipment Effectiveness (OEE) conhecida como eficiência global dos equipamentos de produção. Esta ferramenta permite monitorar o desempenho da linha de produção, identificar os principais gargalos, monitorar os índices de OEE, sugerir ações de melhoria no processo. Na análise de resultados do OEE ficou evidenciado que os setups, troca de máquina, representavam grandes desperdícios ao processo produtivo sendo então utilizado o SMED para redução destes tempos e conversão em tempos reais de produção. Também a utilização e implementação de outras ferramentas de apoio à implementação, manutenção e sustentação da produtividade como: trabalho padrão, análises de causa-raiz, guia de solução de problemas, criação de um conselho de OEE, educação e treinamento, Gemba, Kaizen, 5 s, trabalho padrão para liderança, controles visuais, reuniões de níveis de responsabilidade baseadas no sistema lean de gerenciamento de produção (Tier 1, Tier 2, Tier 3 e Tier 4). O conjunto destes resultados acaba aumentando o lucro, a produtividade e qualidade, reduzindo assim os custos para o setor farmacêutico possibilitando maior competitividade. Neste estudo ficou possível observar que com a implementação destas ferramentas de gerenciamento da produção transformou-se desperdícios envolvidos no processo em tempo real de produção. / The competition in the pharmaceutical industry is intense and the products have reduced life cycles when compared to the large investments in R&D. To compete in this sector is necessary to improve the efficiency of the manufacture processes often using complex, equipment, modern and automated with focus in the production to reach the goals. In this context, the objective of this work is to demonstrate better control of production basis in the use of Total Productive Maintenance seeking to maximize the efficiency of equipments using various tools among them the Overall Equipment Effectiveness. This tooling allows you to improve the acting of the production line, to identify the bottle mouths, monitor OEE index, and suggesting improve actions in the process. Related the results of OEE analysis have been demonstrated that setup represented major wast in the production process so that the tooling SMED was used to reduce the time of change on the machine and convertion on the productive time. Beyond use of OEE another tools are used to support the implementation, maintenance and support the productivity as standard work, root cause analysis, troubleshooting, creating of OEE Council, education & training, gemba, Kaizen, 5 S, leader standard work, visual control, tier accountability based on the Lean Methodology (Tier 1, Tier 2, Tier 3 and Tier 4). The group of these results ends up increasing the profit to the productivity, quality, and cost reduction to the pharmaceutical industry enabling greater competitiveness. In this study was possible to observe the implementation of managament production tooling that’s convert waste time in an production real time.

Diplomová práce se zabývá problematikou zvyšování produktivity pomocí metody štíhlé výroby zvané Lean. V dnešní době je potřeba udržet si přízeň zákazníka a finanční prosperitu firmy. Právě štíhlá výroba se zaměřuje na spokojenost zákazníka a na zvýšení finančního přínosu firmě, tím, že se snaží odstranit plýtvání, která se vyskytují v procesu. Lean Six Sigma je metoda, která se zaměřuje na spokojenost zákazníka (Lean) a na zdokonalování procesů (Six Sigma). Jejím cílem je zvýšit kvalitu a zároveň snížit náklady a dobu dodání. První část diplomové práce se zabývá nástroji štíhlé výroby a detailně popisuje metody, které budou použity v praxi. V praktické části diplomové práce byly využity nástroje Lean Six Sigma především metoda DMAIC, která je označována za systematický přístup k řešení problémů. Skládá se z pěti částí: definování, měření, analýza, zlepšení a kontrola.

The Master’s Thesis is focused to introduction MES systems and factory information systems. Describes main advantages of applying MES systems and describes concept of Six Big Losses, which characterise main losses in production. For quantification of losses are used indicators like OEE. Next part is simulation data of Filler Machine and producing via OPC Server. Main application made in software SIMATIC IT by Siemens is collecting, processing and archiving data to PPA database due to rules and setups. Presentation of data is powered by Data Report which provides overview of efficiency and main losses in production.

The theoretical part of this master’s thesis introduces to the reader problems of production management tools, and optimization of the manufacturing process. It explains basic concepts, principles and methods that has aim to clarify systems and methods used in the practical part to the reader. The practical part describes the implementation of these methods and optimization of specific part of the manufacturing process. The result of optimization is evaluated from the perspective of time and finance.