Academic literature on the topic 'Smart Manufacturing Systems'

The modern industry, production, and manufacturing core is developing based on smart manufacturing (SM) systems and digitalization. Smart manufacturing’s practical and meaningful design follows data, information, and operational technology through the blockchain, edge computing, and machine learning to develop and facilitate the smart manufacturing system. This process’s proposed smart manufacturing system considers the integration of blockchain, edge computing, and machine learning approaches. Edge computing makes the computational workload balanced and similarly provides a timely response for the devices. Blockchain technology utilizes the data transmission and the manufacturing system’s transactions, and the machine learning approach provides advanced data analysis for a huge manufacturing dataset. Regarding smart manufacturing systems’ computational environments, the model solves the problems using a swarm intelligence-based approach. The experimental results present the edge computing mechanism and similarly improve the processing time of a large number of tasks in the manufacturing system.

In a traditional system paradigm, an enterprise reference model provides the guide for practitioners to select manufacturing elements, configure elements into a manufacturing system, and model system options for evaluation and comparison of system solutions against given performance metrics. However, a smart manufacturing system aims to reconfigure different systems in achieving high-level smartness in its system lifecycle; moreover, each smart system is customized in terms of the constraints of manufacturing resources and the prioritized performance metrics to achieve system smartness. Few works were found on the development of systematic methodologies for the design of smart manufacturing systems. The novel contributions of the presented work are at two aspects: (1) unified definitions of digital functional elements and manufacturing systems have been proposed; they are generalized to have all digitized characteristics and they are customizable to any manufacturing system with specified manufacturing resources and goals of smartness and (2) a systematic design methodology has been proposed; it can serve as the guide for designs of smart manufacturing systems in specified applications. The presented work consists of two separated parts. In the first part of paper, a simplified definition of smart manufacturing (SM) is proposed to unify the diversified expectations and a newly developed concept digital triad (DT-II) is adopted to define a generic reference model to represent essential features of smart manufacturing systems. In the second part of the paper, the axiomatic design theory (ADT) is adopted and expanded as the generic design methodology for design, analysis, and assessment of smart manufacturing systems. Three case studies are reviewed to illustrate the applications of the proposed methodology, and the future research directions towards smart manufacturing are discussed as a summary in the second part.

In the Flexible Manufacturing System, automation and the ability to restructure the manufacturing facility is important. The development of a discretely working Smart Automated Guided Vehicle is the need of the hour. Hence the objective is to develop a compact unit load Smart Automated Guided Vehicle to increase efficiency and productivity & to overcome the problems of conventional material handling systems and improve the efficacy of manufacturing. Smart Automated Guided Vehicle is provided with navigation, weight sensing, obstacle detection systems with other auxiliary systems instrumental in zonal setup for the Smart Automated Guided Vehicle as well as adaptable for frequent changes. This model of Smart Automated Guided Vehicle is helpful for a small operational manufacturing unit for multipurpose applications at very low cost and high customizability. The objective is to provide a safe environment to the Smart Automated Guided Vehicle & its surroundings also, to reduce human dependency.

The world of industrial manufacturing has changed a lot during the past decades. It has gone from a labour-intensive process of manual control of machines to a fully connected and automated process. The next big leap in industrial manufacturing is known as industry 4.0 or smart manufacturing. With industry 4.0 comes increased integration between IT systems and the factory floor. This change has proven challenging to implement into existing factories many with the intended lifespan of several decades. One of the single most important parameters to measure is the operating hours of each machine. This information can help companies better utilize their resources and save huge amounts of money.  The goal is to develop a solution which can track the operating hours of the machines using image analysis and the signal lights already mounted on the machines. Using methods commonly used for traffic light recognition in autonomous cars, a system with an accuracy of over 99% during the specified conditions, has been developed. It is believed that if more diverse video data becomes available a system, with high reliability that generalizes well, could be developed using similar methodology.
Industriell tillverkning har förändrats mycket under de senaste decennierna. Det har gått från en process som krävt mycket manuellt arbete till en process som är nästan helt uppkopplad och automatiserad. Nästa stora steg inom industriell tillverkning går under benämningen industri 4.0 eller smart tillverkning. Med industri 4.0 kommer en ökad integration mellan IT-system och fabriksgolvet. Denna förändring har visat sig vara särskilt svår att implementera i redan existerande fabriker som kan ha en förväntad livstid på flera årtionden. En av de viktigaste parametrarna att mäta inom industriell tillverkning är varje maskins operativa timmar. Denna information kan hjälpa företag att bättre utnyttja tillgängliga resurser och därigenom spara stora summor pengar. Målet är att utveckla en lösning som, med hjälp av bildanalys och de signalljus som maskinerna kommer utrustade med, kan mäta maskinernas operativa timmar. Med hjälp av metoder som vanligen används för trafikljusigenkänning i autonoma fordon har ett system med en träffsäkerhet på över 99% under de förutsättningar som presenteras i rapporten utvecklats. Om mer video med större variation blir tillgänglig är det mycket troligt att det går att utveckla ett system som har hög pålitlighet i de flesta produktionsmiljöer.

This thesis is devoted to the study of the optimisation-based control techniques for the design of control strategies that contribute to improve the energy efficiency of smart manufacturing systems. Currently, manufacturing industry is suffering a transformation towards smart, flexible, and energy-efficient manufacturing systems. This transformation requires modularised and reconfigurable manufacturing systems to respond to changes in productions programs and to the time-varying pieces demand while keeping an energy-efficient operation. Thus, suitable control systems should be designed to satisfy the requirements of this transformation while minimising the energy consumption and maximising the plant profit. In this regard, optimisation-based controllers are suitable for the design of control systems that minimise the total energy consumption of such systems while remaining their productivity taking into account the operational conditions and the factors that affect them. First, this dissertation presents how optimisation-based control techniques can contribute to face the challenges of the smart manufacturing systems. Based on this review, manufacturing industry is classified by levels, i.e., machine, process line, and plant levels, for the design of optimisation-based controllers. Besides, to design control strategies that do not affect plant productivity, manufacturing systems are also classified according to the operations performed. Based on these classifications, control strategies are proposed to minimise either the total energy consumption of manufacturing systems or the energy costs related to the operation of such systems. At both machine and process line levels, control strategies are designed based on model predictive control approach to minimise their energy consumption. The underlying idea behind the proposed control strategies consists of managing independently those devices (or systems) that are not directly involved in the machining operations. Thus, energy consumption models are required to predict the total energy consumption profile of manufacturing systems and, based on this, to select the activation/deactivation instants of the manipulated devices that minimise their energy consumption and guarantee their proper operation. Next, due to at the process line level the size and complexity of manufacturing systems increases, a control strategy based on two control modes is proposed to reduce the computational burden of such controllers by switching from a control mode based on online optimisation to an autonomous control mode without solving an optimisation problem. Since the need for flexible and reconfigurable manufacturing systems, non-centralised control strategies are proposed at higher industrial levels to minimise their energy consumption. Thus, both cooperative and non-cooperative local controllers are designed considering a fixed system partitioning and using alternative direction methods of multipliers to solve the optimisations problems in a distributed fashion. Besides, due to the nature of the proposed control objectives, a way to define the consensus stage among the local controllers with coupled dynamics is proposed. Finally, the control strategies designed at plant level are based on the economic model predictive control approach to maximise the plant profit and minimise the operational costs related to the plant operation. At this level, control objectives are focused on determining the economic-optimal production programming of the plant that the control strategies at lower levels should follow. In this regard, the production programming of the plant is determined taking into account the pieces demand, the energy consumption of manufacturing systems, and the current energy market and their fluctuations. All control strategies proposed in this thesis are tested in simulation considering different scenarios designed based on the real operation of an automotive part manufacturing plant.
Esta tesis se basa en el estudio de las técnicas de control basadas en optimización para el diseño de estrategias de control que mejoren la eficiencia energética de los sistemas de manufactura inteligentes. La industria de manufactura se está transformando hacia sistemas de manufactura inteligentes, flexibles y eficientes energéticamente, que requiere de estructuras modulares y reconfigurables para poder responder a los cambios en la programación de la producción y la demanda de piezas. Así, se deben diseñar sistemas de control que cumplan los requerimientos de dicha transformación mientras minimizan el consumo de energía y maximizan la rentabilidad de la planta. En este sentido, los controladores basados en optimización son adecuados para el diseño de sistemas de control que minimicen el consumo de energía de dichos sistemas mientras mantienen su productividad teniendo en cuenta los factores que los afectan. Primero, se presentan como las técnicas de control basadas en optimización pueden contribuir a hacer frente a los desafíos impuestos por la industria de manufactura. Con base en esta revisión, la industria manufacturera se clasifica por niveles, nivel de máquina, línea de proceso, y planta, para el diseño de controladores basados en optimización. Además, para diseñar estrategias de control que no afecten la productividad de la planta, se propone una clasificación para estos sistemas en función de las operaciones realizadas. Con base en estas clasificaciones, se diseñan estrategias de control que minimicen el consumo de energía de los sistemas de manufactura o los costos asociados a dicho consumo. A los niveles de maquina y línea, se diseñaron estrategias de control para minimizar el consumo de energía de los sistemas de manufactura con base en el enfoque de control predictivo basado en modelo. Las estrategias propuestas se basan en la gestión independiente de aquellos dispositivos que no están directamente relacionados con las operaciones de mecanizado. Por lo tanto, modelos de consumo de energía fueron necesarios para predecir el perfil del consumo de energía de estos sistemas y, a partir de esto, seleccionar los instantes de activación/desactivación de los dispositivos manipulados que minimicen el consumo de energía y garanticen el correcto funcionamiento de dichos sistemas. Dado que al nivel de línea el tamaño y la complejidad de estos sistemas aumenta, se propone a una estrategia de control basada en dos modos de control para reducir la carga computacional mediante la conmutación de un modo de control basado en optimización a un modo autónomo que no requiere optimización. Dada la necesidad de sistemas de manufactura flexibles y reconfigurables, estrategias de control no centralizadas se proponen para minimizar el consumo de dichos sistemas a los niveles más altos. Para este fin, los sistemas de manufactura se dividieron en subsistemas, y se diseñaron controladores locales de tipo cooperativo y no cooperativo usando métodos alternativos de dirección de multiplicadores para resolver los problemas de optimización. Además, debido a la naturaleza de los objetivos de control propuesto, se propuso una forma de establecer el consenso entre los controladores locales con dinámicas acopladas. Finalmente, a nivel de planta, se diseñan estrategias de control con base en el enfoque control predictivo basado en modelo económico para maximizar la rentabilidad de la planta. A este nivel, los objetivos de control se centran en determinar la programación de la producción óptima que deberán seguir las estrategias de control diseñadas a niveles más bajos. Así, la programación de la producción de la planta se determina teniendo en cuenta la demanda de piezas, el consumo de energía total, y el mercado energético con sus fluctuaciones. Las estrategias de control propuestas en esta tesis se probaron en simulación considerando diferentes escenarios diseñados con base en la operación real de una planta de fabricación de piezas automotrices.
Aquesta tesi es centra principalment en l’estudi de les tècniques de control basades en optimització per al disseny d’estratègies que contribueixin a millorar l’eficiència energètica dels sistemes de manufactura intel·ligents. Actualment, la indústria manufacturera està experimentant una transformació cap a sistemes de manufactura intel·ligents, flexibles i eficients energèticament, impulsada pels avenços en dispositius de mesura, gestió de dades i eines de comunicació i connectivitat. Aquesta transformació requereix que els sistemes de manufactura siguin modulars i reconfigurables per poder respondre als canvis en la programació de la producció i de la demanda i disseny de les peces mentre continuen operant de manera eficient i sostenible. Per tant, per tal d’assolir una indústria de manufactura m’és intel·ligent, s’han de dissenyar sistemes de control adequats que permetin complir els requeriments d’aquesta transformació, així com també minimitzar el consum d’energia i maximitzar la rendibilitat de la planta. En aquest sentit, els controladors basats en optimització i les arquitectures de control no centralitzat podrien ser adequats per al disseny de sistemes de control que contribueixin a minimitzar el consum d’energia total d’aquests sistemes mentre mantenen la seva productivitat i tenen en compte les restriccions operatives i els factors externs que afecten aquests sistemes. Per tant, mitjançant l’ús d’estratègies de control avançat, els sistemes de control poden ser degudament actualitzats per incloure la informació sobre els canvis en l’operació dels sistemes de manufactura, així com també la variació del mercat energètic per minimitzar els costos d’energia durant l’operació de la planta. Primer, en aquesta tesi, es presenten i discuteixen les estratègies actualment implementades en la indústria manufacturera per millorar la seva eficiència energètica. En base a aquesta revisió, s’identifiquen les principals bretxes de recerca en aquest camp i es discuteix com les tècniques de control basades en optimització poden contribuir a fer front als desafiaments imposats per la nova era de la indústria manufacturera (Industry 4.0). Recolzant-se en la revisió de la literatura, es proposa classificar la indústria manufacturera per nivells, considerant el nivell de màquina, línia de procés i planta, per al disseny de controladors basats en optimització. A més, per tal de dissenyar estratègies de control que no afectin la productivitat de la planta, és a dir, el nombre de peces processades per unitat de temps, els elements constitutius dels sistemes de manufactura també es classifiquen en dispositius de mecanitzat i perifèrics en funció de les operacions realitzades. Els elements de la primera classe corresponen a aquells que estan directament involucrats en les operacions de mecanitzat, mentre que els de la segona classe són aquells que s’encarreguen de proveir els recursos requerits pels dispositius de mecanitzat. Després, en base a aquesta classificació, es proposen estratègies de control en cada nivell per minimitzar el seu consum d’energia o els costos associats a aquest consum. Per als nivells de màquina i línia de procés, es dissenyen estratègies de control per minimitzar el consum d’energia dels sistemes de manufactura en base a l’enfocament de control predictiu basat en model. Les estratègies proposades es basen en la idea de gestionar de manera independent els dispositius (o sistemes) perifèrics per tal de no afectar el temps de processament de les màquines tot mantenint l’operació dels dispositius de mecanitzat. Per tant, calen models de consum d’energia per a predir el perfil de consum d’energia dels sistemes de manufactura i, en base a aquesta predicció, seleccionar els instants d’activació / desactivació per als dispositius manipulats a partir dels quals es minimitzi el consum d’energia total i es pugui garantir el correcte funcionament d’aquests sistemes. D’altra banda, atès que al nivell de línia de procés la mida i la complexitat dels sistemes de manufactura augmenta, es proposa una estratègia de control basada en dos modes de control per tal de reduir la càrrega computacional i dissenyar controladors que puguin ser implementats en temps real. En aquest sentit, tenint en compte que els sistemes de manufactura presenten un comportament diari, es proposa un algoritme per detectar la periodicitat d’aquests sistemes i, després, commutar a un mode de control autònom que no requereixi resoldre un problema d’optimització en línia. D’altra banda, donada la necessitat de sistemes de manufactura flexibles i reconfigurables, es proposen estratègies de control no centralitzades per minimitzar el consum d’energia dels sistemes de fabricació als nivells més alts. Amb aquesta finalitat, els sistemes de manufactura es divideixen en subsistemes, i es dissenyen controladors locals de tipus cooperatiu i no cooperatiu utilitzant mètodes alternatius de direcció de multiplicadors per resoldre els problemes d’optimització de manera distribuïda. A més, a causa de la naturalesa de l’objectiu de control proposat, el qual està enfocat en minimitzar el consum d’energia dels sistemes de manufactura, es proposa una forma d’establir el consens entre els controladors locals amb dinàmiques acoblades. Després, les estratègies de control proposades són extrapolades al nivell de planta usant objectius de tipus econòmic, i es comparen les arquitectures de control centralitzat i no centralitzat pel que fa al seu acompliment en llac¸ tancat i la càrrega computacional requerida per trobar una solució. Finalment, a nivell de planta, es dissenyen estratègies de control en base a l’enfocament de control predictiu basat en model econòmic per tal de maximitzar la rendibilitat de la planta i minimitzar els costos associats a la seva operació. Per tant, a aquest nivell, els objectius de control se centren a determinar la programació de la producció òptima de la planta que hauran de seguir les estratègies de control dissenyades als nivells més baixos. En aquest sentit, la programació de la producció de la planta és determinada tenint en compte la demanda actual de peces, el consum d’energia dels sistemes de manufactura i el mercat energètic amb les seves fluctuacions. Totes les estratègies de control proposades en aquesta tesi es proven en simulació considerant diferents escenaris basats en l’operació real d’una planta de fabricació de peces automotrius.

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, Engineering and Management Program, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 115-122).
The manufacturing industry has, recently, been facing tremendous challenges, including cost efficiency, system safety, and process automation, and manufacturing companies are required to adopt new technologies to keep themselves sustainable in the fast-changing world of technology. This research focuses, in particular, on how to prevent cutting tool failures and catastrophic accidents in Computerized Numerically Controlled (CNC) machining processes by using a predictive model based on the cutting sound data. With advances in machine learning algorithms and predictive analytics techniques, it becomes possible to create a noise-robust predictive model from an unstructured dataset of sound data. It is an obviously desirable decision to make use of every technology as required and benefit from it. The predictive model introduced in this research uses cutting sound data rather than acoustic emission or force/torque sensor data, which have been widely used for machine failure detection but have shown some limitations. The model is an important stepping stone for realizing an unmanned and fully automated manufacturing system, the so-called "smart factory," and it would be a meaningful movement for the government side as well, taking into account government's responsibility to keep people safe in the workplace. In this research, several experiments were carried out to collect sound data in the CNC machining center in Korea, and particular features were extracted from the analog waveform signals, using the unstructured data to make the predictive model using various advanced data analytics techniques and cutting-edge machine learning algorithms. Then, several analysis methods with systems thinking were used to explore potential impacts of the predictive model on the manufacturing system because the systems thinking approach is the most effective way to analyze a wide range of potential impacts from a holistic perspective. Specifically, the impact analysis was successfully conducted by using a "Causal Analysis based on STAMP (CAST)," which is a system safety analysis method. Also used was "system dynamics modeling," which is generally employed to identify dynamic behaviors in a complex system. Finally, a "complete value template" was constructed to portray how the new system delivers value to its stakeholders from a system architecture perspective.
by Hyunsoo Jeong.
S.M. in Engineering and Management

The recent development of smart connected devices, in this paper defined as cyberphysical systems (CPS), within the context of Industry 4.0 presents an opportunity for the manufacturing industry to reach new levels of efficiency. The introduction of novel technologies onto the factory floor will however affect the manufacturing execution systems (MES) currently deployed. There exists an uncertainty whether the existing MES are able to be integrated with these new technologies. This paper aims to identify and investigate the main areas affected by the integration of CPS and MES. The results, gained through scientific studies as well as interviews with segments of concerned industry and researchers, allowed us to identify six areas that will be affected. Out of the six areas, two have evolved as to be of most interest, namely architectural integration of systems and the human resource area, since change within the other four areas depend on them. We found that an integration of CPS and MES will not be possible unless two key factors are considered, a standardization of communication between systems and a knowledgeable, open minded and inter-communicating workforce.
Den senaste tidens utveckling av smarta uppkopplade enheter, i texten benämnda cyberphysical systems, inom vad som kallas Industri 4.0 medför en möjlighet för effektivisering inom tillverkningsindustrin. Introduktionen av ny teknik inom tillverkningsindustrin kommer dock att påverka de befintliga produktionsstyrningssystemen och det råder idag en osäkerhet kring om en integration är möjlig. I denna uppsats är målet att identifiera och undersöka de områden som påverkas när smarta uppkopplade enheter introduceras i fabriker. Genom litteraturstudier, såväl som intervjuer med aktörer inom berörd industri och forskare, har vi identifierat sex påverkade områden. Utav dessa sex områden anser vi att två är utav störst intresse. Detta då utvecklingen inom dessa två områden kan ses som en förutsättning för utveckling inom de övriga områdena. De två områdena är systemintegration och personalresurser. Vi fann att en integration mellan smarta uppkopplade enheter och produktionsstyrningssystem endast är möjlig om två nyckelfaktorer beaktas, en standardisering av kommunikation mellan system och en välutbildad, öppen, arbetsstyrka.

With the advent of the fourth industrial revolution, information technology and manufacturing systems are merging to form what is now known as Smart Manufacturing. However, with this newer technology being integrated with newer pieces of machining equipment, companies with legacy equipment occasionally are in a bind since these machines were not designed or built with the fundamental components of smart manufacturing systems: unified connectivity, real-time monitoring, and issue identification. The purpose of this research is to provide a solution for converting manual machining equipment into smart systems with these fundamental components of smart manufacturing. The pieces of equipment that were the subjects of this experimentation were an HJ-1100 Kingston lathe and four ACER Vertical Turret Milling machines. None of these machines had any of these capabilities at the inception of this project.These machines were successfully converted into smart systems with varying degrees of reliability between the lathe and the four mills in the case of real-time monitoring and issue identification. The setups and configurations to achieve these three smart components are described and provided.

Background: Service industries have grown extensively over the past few decades on the back of globalized business trends. With increasing competition, product firms are struggling on product sales alone. Hence, both products and services are being bundled into what is known as offerings. Moreover, firms are looking into how they can improve their offerings to meet customer needs with the help of smart products. Smart products are described as products able to communicate and interact with other electronic devices as well as being self aware. One of these examples is conditional monitoring whereby the product is houses built in sensors to communicate with a back end ERP system providing the supplier a transparent view and real-time update into the status and service needs for both the product and customer. Purpose: The aim of this thesis is to explore how smart products can help leverage services for product firms moving towards a service focus. Method: In addressing the purpose a case study strategy was applied. An inductive approach was used, and interviews were conducted with two Swedish manufacturers, SKF and Atlas Copco. SAP, a software provider was also interviewed. Lastly, a qualitative approach was used and secondary data was collected through annual reports, as well as public company information. Conclusions: Smart products show the capability of being able to record, transmit and act upon their behavior and usage. One major finding from the thesis is that smart products enable product firms to extend their service portfolios from a transactional to a relational standpoint through real time information feeds. This includes asset maintenance as well as monitoring and visibility into client operations. In addition, traditional product firms help product firm’s move towards a service strategy. Another finding of the thesis is that information visibility shows a positive co-relation with the service provider’s ability to take on more risk increasing service revenues and customer lock in and increase value co-creation. On the other hand smart products show to be challenging to product firms new to service development. These challenges include increasing initial infrastructure costs and high level of maintenance and complexity of the smart products.

This thesis set out to investigate what motivates an established manufacturing firm to add smart and connectivity features into its products and what considerations are made when doing so. Such a firm was used as a case study object, a firm that is considering digitalization in one of its product areas but has not yet finalized the strategy on how to move forward. The qualitative study was made using semi-structured interviews which included mostly respondents from this organization, but also respondents representing the customers as small company entrepreneurs.  A literature study was conducted to better define the subject of product digitalization. Previous research on smart and connected products as well as related business and market theories were identified as relevant, as well as literature on digital transformation. Based on the literature study, a theoretical framework was formulated in the form of an iterative model on product digitalization, where the business and market mechanics are included. A qualitative case study was then conducted in the form of semi-structured interviews, with topics considered relevant based on the literature study. The empirical data shows many similarities compared to the current literature on the topic. The literature study has identified these aspects as relevant to be considered by an established manufacturing firm and compared them with the case study firm: ·       Digital vision – with clear intent and outcome for both the business and the customer ·       Customer intimacy – services related to smart and connected products will increase the intimacy to the end customer ·       Business model – product sales business model will be transformed by servitization ·       Technology content – smart and connected products need integration of new technologies, such as hardware, software and data management ·       Capabilities – necessary capabilities and the ability to integrate and coordinate these ·       Competitive strategy – using digital technology to gain competitive advantage.  ·       Technology acceptance –what technologies are accepted by the customers and why The case study shows that the firm have identified several key areas affected by product digitalization. Many of these aspects are considered when making strategic decisions, and the members of the organization are including most of them when building scenarios. One area of concern is the ability to integrate and coordinate all necessary functions, both for technology and services. Another is the switch in mindset going from a predictable product business model to a innovate digital one. Offering services will bring them into more direct contact with the customers, and possibly change the role of the dealership network.

Purpose: The purpose of this paper is to identify the opportunities that Industry 4.0 brings within the framework of applying Cyber Physical Systems in an environment of a Smart Factory. This paper shall identify the changes within daily business processes and the impact of these changes on the daily business life. Design/Methodology/Approach: The research is carried out as a case study research. Due to a qualitative approach for this case study interviews are conducted and the results are analyzed and discussed. Findings: Industry 4.0 will change the way we are working today and influence businesses and business processes in many ways. Data handling, processes and efficiency will change and the way we perceive manufacturing will change in a long term view. Further Research: It would be recommended to expand this research by conducting more research in this particular field as well as impacts on the employee should be studied more in detail.

Idag pratas det mycket om smart teknik och man säger att den fjärde industriella revolutionen är på väg. Revolutionen kallas för Industri 4.0 och innebär två tekniska förbättringar, Internet of Things (IoT) och Cyber-Physical Systems (CPS). IoT låter fysiska enheter sammankopplas i ett system med andra enheter med hjälp av elektromagnetiska vågor och CPS ger möjligheten till att få in information från omvärlden och implementera informationen i digital form. När det kommer till implementering i tillverkningsindustrin används begreppen Industrial Internet of Things och Cyber-Physical Production Systems. Arbetet består av en djupgående litteraturstudie och undersöker vad implementering av IoT och CPS i personbilsbranschens tillverkningssystem kan leda till och hur de fungerar i praktiken. Teorin utgår från vetenskapliga artiklar, tidskrifter och journaler samt en studie från Atlas Copco. Eftersom att smart teknik är ett brett ämne och vi behövde förhålla oss till en tidsgräns på 18 veckor avgränsades arbetet till endast IoT och CPS i tillverkande personbilsföretag. Branschen för personbilar valdes för att i jämförelse med andra branscher är både kvaliteten och kvantiteten avgörande. Samtidigt som det produceras många personbilar måste varje personbil uppfylla en rad olika krav och varje enhet utgör en betydande del av kapitalet i företaget.Resultatet visar hur IoT och CPS fungerar som helhet och vad för positiva och negativa konsekvenser implementering av begreppen ger. Av resultatet framgår också att faktorerna produktion, ekonomi och människa ska analyseras som en helhet och inte enskilt för att implementeringen ska vara framgångsrik i tillverkande personbilsföretag.Möjligheterna som IoT och CPS medför är snabbare och exaktare beslut, systemövervakning och insamling, utbyte och analysering av data för personbilsbranschens företag. Den största utmaningen som implementeringen av begreppen medför är datahantering. Det finns en risk att oönskade mottagare får tillgång till konfidentiell information genom bland annat dataläckage och dataintrång. Således bör fokus ligga på att förebygga detta för att få ut fördelarna och samtidigt reducera nackdelarna. Slutsatsen som kan dras av resultatet är att en kombination av IoT och CPS i personbils-branschens tillverkningssystem skapar ett kommunikationsnätverk bland heterogena enheter som gör att system kan kommunicera och utbyta data med varandra på ett effektivt sätt.Implementering av begreppen leder till minskning av defekter, introduktionskostnader, energianvändning och upplärning för arbetare samt ökad verktygsdrift och produktivitet.
Today, there is much talk about smart technology and it is said that the fourth industrial revolution is on its way. The revolution is called Industry 4.0 and involves two technical improvements, the Internet of Things (IoT) and Cyber-Physical Systems (CPS). IoT allows physical devices to be interconnected in a system with other devices using electromagnetic waves and CPS provides the opportunity to get information from the outside world and implement the information in digital form. When it comes to implementation in the manufacturing industry, the concepts Industrial Internet of Things and Cyber-Physical Production Systems are used.The thesis consists of an in-depth literature study and investigates what implementation of IoT and CPS in the automotive industry's manufacturing system can lead to and how they work in practice. The theory is based on scientific articles, paper and journals, and a study by Atlas Copco. Because smart technology is a broad topic and we needed to relate to a time limit of 18 weeks, the work was limited to IoT and CPS only in manufacturing passenger car companies. The industry for passenger cars was chosen so that, in comparison with other industries, both the quality and the quantity are decisive. While many passenger cars are being produced, each passenger car must meet a variety of requirements and each unit constitutes a significant part of the capital of the company.The result shows how IoT and CPS work as a whole and what positive and negative consequences the implementation of the concepts gives. The result also shows that the factors of production, economy and humanity should be analysed as a whole and not individually in order for the implementation to be successful in manufacturing passenger car companies.The opportunities that IoT and CPS entail are faster and more precise decisions, system monitoring and collection, exchange and analysis of data for the automotive industry's companies. The biggest challenge that the implementation of the concepts entails is data management. There is a risk that unwanted recipients will have access to confidential information through, among other things, data leakage and hacking. Thus, the focus should be on preventing this in order to get the benefits and at the same time reduce the disadvantages.The conclusion that can be drawn from the result is that IoT and CPS in the automotive industry's manufacturing system create a communication network among heterogeneous units that enable systems to communicate and exchange data with each other in an efficient manner. Implementation of the concepts leads to a reduction of defects, introduction costs, energy use and training for workers, as well as increased tool operation and productivity.