<p>Energy management has become a major concern in the past two decades with the increasing energy prices, overutilization of natural resources and increased carbon emissions. According to the department of Energy the industrial sector solely consumes 22.4% of the energy produced in the country [1]. This calls for an urgent need for the industries to design and implement energy efficient practices by analyzing the energy consumption, electricity data and making use of energy efficient equipment. Although, utility companies are providing incentives to consumer participating in Demand Response programs, there isn’t an active implementation of energy management principles from the consumer’s side. Technological advancements in controls, automation, optimization and big data can be harnessed to achieve this which in other words is referred to as “Smart Manufacturing”. In this research energy management techniques have been designed for two SEU (Significant Energy Use) equipment HVAC systems, Compressors and load shifting in manufacturing environments using control and optimization.</p> <p>The addressed energy management techniques associated with each of the SEUs are very generic in nature which make them applicable for most of the industries. Firstly, the loads or the energy consuming equipment has been categorized into flexible and non-flexible loads based on their priority level and flexibility in running schedule. For the flexible loads, an optimal load scheduler has been modelled using Mixed Integer Linear Programming (MILP) method that find carries out load shifting by using the predicted demand of the rest of the plant and scheduling the loads during the low demand periods. The cases of interruptible loads and non-interruptible have been solved to demonstrate load shifting. This essentially resulted in lowering the peak demand and hence cost savings for both “Time-of-Use” and Demand based price schemes. </p> <p>The compressor load sharing problem was next considered for optimal distribution of loads among VFD equipped compressors running in parallel to meet the demand. The model is based on MILP problem and case studies was carried out for heavy duty (>10HP) and light duty compressors (<=10HP). Using the compressor scheduler, there was about 16% energy and cost saving for the light duty compressors and 14.6% for the heavy duty compressors</p> <p>HVAC systems being one of the major energy consumer in manufacturing industries was modelled using the generic lumped parameter method. An Electroplating facility named Electro-Spec was modelled in Simulink and was validated using the real data that was collected from the facility. The Mean Absolute Error (MAE) was about 0.39 for the model which is suitable for implementing controllers for the purpose of energy management. MATLAB and Simulink were used to design and implement the state-of-the-art Model Predictive Control for the purpose of energy efficient control. The MPC was chosen due to its ability to easily handle Multi Input Multi Output Systems, system constraints and its optimal nature. The MPC resulted in a temperature response with a rise time of 10 minutes and a steady state error of less than 0.001. Also from the input response, it was observed that the MPC provided just enough input for the temperature to stay at the set point and as a result led to about 27.6% energy and cost savings. Thus this research has a potential of energy and cost savings and can be readily applied to most of the manufacturing industries that use HVAC, Compressors and machines as their primary energy consumer.</p><br>

In this new digital age, efficiency, quality and competition are all increasing rapidly as companies leverage the Industrial Internet of Things (IIoT). However, while industrial innovation moves at a faster and faster pace, educational institutions have lagged in the development of the curriculum and environment needed to support further development of the IIoT. To fully realize the potential of the IIoT in the manufacturing sector educational institutions must support the technological training and education rigor demanded to instill the skills and thought leadership to move the industry forward. The purpose of this research is to provide an IIoT core system in an educational factory environment. This system will assist in teaching basic principles of IIoT in the factory while simultaneously allowing for students to envision the manufacturing journey of any facility by implementing principles of IIoT. This will be accomplished by providing all the following capabilities together in a single data system: unified connectivity, role-based data display, real-time issue identification, data analytics, and augmented reality.

Export of heat treated steel goods has an important impact on the Swedish economy which brings performance demands and expectations on production to keep a competitive market position. Sustainability and efficiency are two important aspects in meeting these demands. This thesis studies how a data driven approach can be used to increase efficiency in manufacturing of rods produced for the mining industry.  The purpose of this thesis is to use a machine learning model suitable for classifying quality results for heat treated steel rods. This is done by comparing nine algorithms with the objective to tune and deploy the model best fitted while gaining insights in variables that have an impact on the quality output.  This thesis outset is a heat treatment process at Epirocs facility in Fagersta. Interviews are conducted to gain domain knowledge about important features and an AI pipeline is implemented to demonstrate its suitability for predicting quality given production and weather data in the form of time series and product-unique data points.  The result of the study shows that the machine learning algorithm random forest is indicated as most suitable among the analyzed. The study also shows that an AI pipeline with streaming data can be designed and efficiently implemented for quality improvement. Through this work, the authors have proved that machine learning can be used to improve the heat treatment process of rods, but the model still has room for improvement in feature selection and availability of larger and more detailed data at the facility.

Smart manufacturing – smart factories creating smart products – is a topic which has arisen in the academic as well as business community. This thesis covers smart manufacturing in the context of lifecycle management. The thesis investigated how the standard Product Life Cycle Support (PLCS) could be used to support smart manufacturing and mainly how to develop the underlying system and information infrastructure. Standards, reports and specifications for smart manufacturing were investigated. Several information models were created from these publications which could be used for implementing a proposed solution for the infrastructure.The implementation concerned a use case in the ongoing research project DigIn, and used the developed models to implement a proposed solution in the product lifecycle management software ShareAspace. This was done in order to evaluate how to use the functionality of PLCS and ShareAspace to utilize the solution to support smart manufacturing and update the digital twin. In parallel to this thesis, a sub-project part of the DigIn project was conducted which connected the database to other software in the system as well as to the factory shop floor. The solution used the plant service bus Kafka and REST APIs in order to establish the connection. The functionality of the system regarding the specified required functionality in the publications was then investigated.The solution was found to meet most of the requirements of the publications regarding, among others, lifecycle management, service oriented architecture, non-hierarchical structures and communication capabilities. / Smart tillverkning – smarta fabriker som skapar smarta produkter – är ett ämne som inom det akademiska och affärsmässiga området förekommer alltmer frekvent. Denna uppsats behandlar smart tillverkning i kontexten av Product Life Cycle Support (PLCS). Uppsatsen undersökte hur PLCS kunde utnyttjas för att möjliggöra smart tillverkning, med huvudsakligt fokus på möjliggörandet av den bakomliggande system- och informationsinfrastrukturen för smart tillverkning. Standarder, rapporter och specifikationer för smart tillverkning undersöktes. Flertalet informationsmodeller skapades utifrån dessa publikationer vilka kunde användas för att implementera ett förslag för infrastrukturen.Implementationen hade sin bas i det pågående forskningsprojektet DigIn, och använde de utvecklade modellerna för att implementera en föreslagen lösning i produktlivscykel-mjukvaran ShareAspace. Detta gjordes för att utvärdera hur funktionaliteten i ShareAspace och PLCS skulle kunna användas för att stödja smart tillverkning och uppdatera den digitala tvillingen. Parallellt med denna implementation genomfördes i DigIn ett projekt vilka kopplade samman databasen med annan mjukvara i systemet samt fabriksgolvet. Lösningen använde en Plant Service Bus (Kafka) och REST APIer för att koppla samman dessa. Funktionaliteten av systemet rörande specificerade krav som återfanns i publikationerna undersöktes sedan.Lösningen fanns möta de flesta av de krav som lades fram i de undersökta publikationerna rörande, bland annat, livscykelshantering, tjänsteorienterad arkitektur, icke-hierarkiska strukturer samt kommunikationsmöjligheter.

Digitaliseringen har fått en ökad uppmärksamhet i samhället och i olika industrier som följd av framtagandet av ny teknologi som driver utvecklingen framåt. Dagens teknik har idag en inverkan i det vardagliga levnadssättet och kommer även att påverka företag som verkar i alla möjliga branscher, men i synnerhet tillverkningsindustrin vars bransch genomgår en digital transformation som kallas för Industri 4.0. Rapporten har som syfte att undersöka de utmaningar och möjligheter som finns med introducerandet samt implementerandet av 5G teknologin i tillverkande industrier. Metoden i denna studie är en kvalitativ studie där fyra intervjuer har hållits med två företag från användarsidan, Scania och AstraZeneca, samt två företag från utvecklarsidan, Ericsson och H&amp;D Wireless. Utöver detta gjordes en litteratursökning för att skapa en teoretisk grund samt en observation på Scania Smart Factory för att undersöka utvecklarmiljön. Resultaten från intervjuerna och litteratursökningen visade att det finns möjlighet till stabilare tillgänglighet med cellulär teknik. Tekniken förväntas leverera en hög och jämn nivå, och är således mer driftsäkert jämfört med Wi-Fi som kan ha höga toppar och låga bottnar. Vidare finns möjlighet till ett bättre dataflöde i form av högre hastighet samt en större mängd data som kan hanteras, det förväntas bli renare i fabrikerna eftersom kabelledningar kan ersättas med nätverksdosor som sitter i taket samt en ökad flexibilitet i produktionen. Gällande RTLS applikationen som H&amp;D Wireless har utvecklat finns möjlighet till förbättrad precisionslokalisering med 5G. De intervjuade ser liknande utmaningar när det kommer till utveckling och implementation, bland annat anpassandet av utrustning och verktyg, ett kunskapsgap mellan utvecklare och användare samt framtagandet av en 5G standard. Resultaten visar att företag som utvecklar tjänster och applikationer är långt fram i värdekedjan och är beroende av företags investeringar i tekniken och infrastruktur, samtidigt som dessa inte är med och framställer 5G standarden. Vidare innebär en förbättrad tillgänglighet att tekniken har stor användning vid monitorering av utrustning och kritiska processer, men kan komma att konkurreras med nya versioner av Wi-Fi om tekniken får prestandaförbättringar. / Digitalization has gained increased attention in society as well as in various industries as a consequence of the development of new technology that drives growth ahead. Today’s technology has an impact in people’s everyday life and will also affect corporations that operate in all sorts of industries, but especially the manufacturing industry whose industry is undergoing a digital transformation called Industry 4.0. This paper investigates the challenges and opportunities that exist with the introduction and implementation of 5G technologies in manufacturing industries. The method in this study is a qualitative study where four interviews have been held with two companies form the user side, Scania and AstraZeneca,and two companies from the development side, Ericsson and H&amp;D Wireless. In addition to this, a literature search was made to develop a theoretical foundation as well as an observation at Scania Smart Factory Lab to examine the development environment and to see the potential use of the technology. The results from the qualitative study and the literature search showed that there is an opportunity of a more stable availability with cellular technology. It is expected to deliver a high and even level, and is thus more reliable compared to Wi-Fi, which can have high peaks and low bottoms. Furthermore, there is the possibility of a better data flow in form of higher speed as well as a larger amount of data that can be handled. For the RTLS application that H&amp;D Wireless has developed, there is a possibility of improved precision location with 5G. The interviewees see similar challenges when it comes to development and implementation, including the adaption of equipment and tools, a knowledge gap between developers and users, and the development of a 5G standard. The results show that companies that develop services and applications are far ahead in the value chain and are dependent on the users investments in the technology and infrastructure, while these are not involved in constructing the 5G standard. In addition, improved availability means that the technology has great use in monitoring equipment and critical processes, but may be competing with new versions of Wi-Fi if the technology acquires performance improvements.

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.

With the development towards Industry 5.0, manufacturing companies are developing towards smart production. In smart production, data is used as a resource to interconnect different elements in the production system to learn and adapt to changing production conditions. Common objectives include human-centricity, resource-efficiency, and sustainable production. To enable these desired benefits of smart production, there is a need to use digital technologies to create and manage the entire flow of data. To enable smart production, it is essential to deploy digital technologies in a way so that collected raw data is converted into useful data that can be applied by equipment or humans to generate value or reduce waste in production. This requires consideration to the data flow within the production system, i.e., the entire process of converting raw data into useful data which includes data management aspects such as the collection, analysis, and visualization of data. To enable a good data flow, there is a need to combine several digital technologies. However, many manufacturing companies are facing challenges when selecting suitable digital technologies for their specific production system. Common challenges are related to the overwhelming number of advanced digital technologies available on the market, and the complexity of production system and digital technologies. This makes it a complex task to understand what digital technologies to select and the recourses and actions needed to integrate them in the production system. Against this background, the purpose of this licentiate thesis is to examine the selection and integration of digital technologies to enable smart production within manufacturing companies. More specifically, this licentiate thesis examines the challenges and critical factors of selecting and integrating digital technologies for smart production. This was accomplished by performing a qualitative-based multiple case study involving manufacturing companies within different industries and of different sizes. The findings show that identified challenges and critical factors are related to the different phases of the data value chain: data sources and collection, data communication, data processing and storage, and data visualisation and usage. General challenges and critical factors that were related to all phases of the data value chain were also identified. Moreover, the challenges and critical factors were related to people, process, and technology aspects. This shows that there is a need for holistic perspective on the entire data value chain and different production system elements when digital technologies are selected and integrated. Furthermore, there is a need to define a structured process for the selection and integration of digital technologies, where both management and operational level are involved. / Med utvecklingen mot Industri 5.0 utvecklas tillverkningsföretag mot smart produktion. I smart produktion används data som en resurs för att koppla samman olika element i produktionssystemet i syfte att lära sig om och anpassa sig efter förändrade produktionsförhållanden. Vanliga mål för smart produktion inkluderar resurseffektivitet, och en hållbar produktion anpassad utifrån människan. För att åstadkomma dessa önskade fördelar, behöver tillverkningsföretag använda digitala teknologier för att skapa och hantera hela dataflödet. För att möjliggöra smart produktion är det viktigt att implementera digitala teknologier på ett sätt så att insamlad rådata omvandlas till användbar data som kan tillämpas av maskiner eller människor för att skapa värde eller minska slöseri i produktionen. Detta kräver hänsyn till dataflödet inom produktionssystemet, det vill säga hela processen att omvandla rådata till användbar data som inkluderar datahanteringsaspekter som exempelvis insamling, analys och visualisering av data. För att möjliggöra ett bra dataflöde krävs det att flera digitala teknologier kombineras. Många tillverkningsföretag står dock inför flera utmaningar när de ska välja lämpliga digitala teknologier för sitt specifika produktionssystem. Vanliga utmaningar är relaterade till det överväldigande antalet avancerade digitala teknologier som finns på marknaden, samt komplexiteten hos produktionssystem och digitala teknologier. Detta gör det till en komplex uppgift att förstå vilka digitala tekniker som ska väljas och vilka resurser och åtgärder som behövs för att integrera dem i produktionssystemet. Mot denna bakgrund är syftet med denna licentiatuppsats att undersöka hur tillverkningsföretag ska välja och integrera digitala teknologier för att uppnå smart produktion. Mer specifikt så undersöker denna licentiatuppsats vilka utmaningar och kritiska faktorer som finns för att välja och integrera digitala teknologier för att uppnå smart produktion. Detta uppnåddes genom en kvalitativ multipel fallstudie med tillverkningsföretag inom olika branscher och av olika storlekar. Resultaten visar att identifierade utmaningar och kritiska faktorer är relaterade till de olika faserna av datavärdekedjan: datakällor och insamling, datakommunikation, databearbetning och lagring samt datavisualisering och användning. Generella utmaningar och kritiska faktorer som var relaterade till alla faser av datavärdekedjan identifierades också. Dessutom var utmaningarna och kritiska faktorerna relaterade till människa, process och tekniska aspekter. Detta visar att det finns ett behov av helhetsperspektiv på hela datavärdekedjan och olika element i produktionssystemet när digitala teknologier väljs och integreras. Dessutom finns det ett behov av att definiera en strukturerad process för val och integration av digital teknik, där både ledning och operativ nivå är involverade.

Suppliers of the next generation mobile network, 5G, promises increased performance which can ensure smart manufacturing. Smart manufacturing entails systems of wireless and connected sensors, robots and other devices that together are optimizing the manufacturing process with data. At the same time, 5G has become one of the major technological trends in manufacturing. Globally, efforts are being made to optimize assembly lines through smart manufacturing, but there is no evidence of 5G's promised performance in practice. This study addresses how an industrial company that offers smart tools should capture the value of 5G in the business model. This is done by examining if and what unique features 5G is providing, whether customers are ready for this technology and how 5G will affect the business model. The study is based on a case study at Atlas Copco AB, a Swedish industrial company that operates on a global scale. The collaboration was made with Atlas Copco's business area within Industrial Technique and with the divisions General Industry and Motor Vehicle Industry. Data was collected through interviews both internally at Atlas Copco and externally with their customers and were then combined with a literature study. The findings from this study can be summarized as follows: • 5G enables connectivity that is required for smart manufacturing processes.• Customers are in different phases of 5G adoption.• There is a difference in what characteristics customers value, and therefore 5G will not provide the same value for all customers.• The business model will need to be changed to a more service-oriented model when offering 5G, to be able to fully capture the value of 5G. At the end of the study, a summary is given of the study's conceptual and empirical contributions, as well as suggestions for future work. / Leverantörer av nästa generations mobilnät, 5G, utlovar ökad prestanda som kan garantera smart manufacturing. Smart manufacturing innebär system av trådlösa och anslutna sensorer, robotar och andra enheter som tillsammans optimerar tillverkningsprocessen med hjälp av data. Samtidigt har 5G blivit en av de stora teknologiska trenderna inom tillverkning. Globalt görs ansträngningar för att optimera monteringslinor genom smart manufacturing, men det finns ännu inga bevis för 5G:s utlovade prestanda i praktiken. Denna studie behandlar hur ett industriföretag som erbjuder smarta verktyg ska fånga värdet av 5G i affärsmodellen. Detta görs genom att undersöka om och i sådant fall vilka unika funktioner 5G tillhandahåller, om kunderna är redo för denna teknik och hur 5G kommer att påverka affärsmodellen. Studien grundar sig i en fallstudie på Atlas Copco AB, ett svenskt industriföretag med global närvaro. Studien gjordes i samarbete med Atlas Copcos affärsområde för Industriteknik, inom divisionerna för General Industry och Motor Vehicle Industry. Data samlades in genom intervjuer, både internt på Atlas Copco och externt med deras kunder och kombinerades sedan med en litteraturstudie. Resultaten från denna studie kan sammanfattas enligt följande: • 5G möjliggör anslutning som krävs för processer som är kopplade till smarta manufacturing.• Kunderna är i olika faser av 5G-adoptionen.• Det finns en skillnad i vilka egenskaper kunderna värderar och därför kommer 5G inte att ge samma värde till alla kunder. • Affärsmodellen måste sannolikt ändras till en mer serviceinriktad modell när man erbjuder 5G för att fullt ut kunna fånga värdet av 5G. I slutet av studien ges en sammanfattning av studiens konceptuella och empiriska bidrag samt förslag på framtida arbete.

This project was initiated by Postnord who wanted to develop their own large frame FDM 3D printer, mainly for two reasons. The first reason was to be able to use the collaboration between Postnord and KTH to present how Postnord are promoting domestic production in the same time as portraying themselves as leaders in the field of additive manufacturing in Sweden. The second reason was to get a machine with the ability to print both small- and large-scale prototypes and products to be used in an industrial environment. The targeted goals and desired outcome of the PP3D (PostPaper3D - project name) was to construct a large frame FDM 3D printer, with a build area of 1 square meter and (if possible) a printing volume of 1 cubic meter, capable of printing parts for industrial applications. This would be achieved by using industrial components and state-of-the-art open source 3D printing control systems. Sensors for filament run-out detection and automatic printer bed levelling was also desired. On top of these goals KTH-IIP wanted the project work to focus on the construction of large frame FDM 3D printers, what challenges appear in scaling up the technology, to further the internal vision of developing strategic competencies in the field of additive manufacturing - as requested by the industry. The result of the project was a FDM 3D printer with a build volume of 1000x1000x950 [mm] that comes with dual independent extruders - meaning it may either print two copies of the same part simultaneously or utilize both printer heads to work on a single component. The top tested speed (printing) was 100 [mm/s] and the top tested movement speed was 250 [mm/s]. The theoretical accuracy of the machine is 50 [μm] but this has not been tested in this project. In the scope of the master thesis all prototype-symptoms were not eliminated, where the most considerable issue being the motors occasionally skipping steps (and losing their location) during rapid accelerations and changes in velocity. When this happens, it will most likely result in a failed print. The proposed solution for this is to further adjust the firmware to allow for finer, more regulated accelerations and speeds. Another possible solution is to replace the motors with stronger ones. In delivery the machine operates using state of the art components and software, from prominent Swedish and international producers. An interview of Isak Emericks alongside the printer can be seen in Appendix B, in the form of a newsletter. / Det här projektet initierades av Postnord som ville utveckla en egen storskalig FDM 3D printer, huvudsakligen på grund av två anledningar. Den första för att kunna använda samarbetet med KTH för att visa hur Postnord främjar inhemsk produktion samtidigt som de själva är ledare och initiativtagare inom additiv tillverkning i Sverige. Den andra anledningen var för att få tag på en maskin som har möjligheten att skriva ut stora- och småskaliga prototyper och produkter som kan användas i en industriell miljö. De uppsatta målen och önskvärda resultatet med PP3D (PostPapper3D - projektnamn) var att konstruera en storskalig FDM 3D skrivare, men en byggarea på 1 kvadratmeter och (om möjligt) en byggvolym på 1 kubikmeter, kapabel att skriva ut delar för industriella tillämpningar. Det här skulle uppnås genom att använda industriella komponenter och toppmoderna kontrollsystem för 3D skrivare. Sensorer för att upptäcka när utskriftsmaterialet var på väg att ta slut och automatisk utjämning av byggytan var också önskvärt. Förutom dessa målsättningar så ville KTH-IIP att arbetet skulle fokusera på konstruktionen av en storskalig FDM 3D skrivare, vilka utmaningar och problem som uppstår när tekniken skalas upp, för att fortsätta den interna visionen om att utveckla strategiska kompetenser inom additiva tillverkningsmetoder - vilket industrin efterfrågade. Resultatet av projektet var en 3D skrivare med en byggvolym på 1000x1000x950 [mm] som kommer utrustad med två (individuellt styrda) utskriftshuvuden - som antingen kan skriva ut två identiska kopior av samma objekt eller som kan arbeta tillsammans för att bygga upp en komponent mer effektivt. Den högsta testade utskriftshastigheten var 100 [mm/s] och den högsta testade hastigheten för rörelse var 250 [mm/s]. Den teoretiska upplösningen hos maskinen är 50 [μm] men det här har inte kontrollerats i det här projektet. Inom omfattningen av ett examensarbete (civilingenjör) så hann inte alla prototyp-symptom elimineras, där det mest betydande problemet var att motorerna bitvis missar steg (och förlorar sin positionering) under hastiga accelerationer och förändringar i rörelseriktning. När detta händer så resulterar det oftast i misslyckade utskrifter. Den presenterade lösningen för det här är att fortsätta justera mjukvaruinställningarna tills finare och mer kontrollerade rörelsemönster uppnås. En annan tänkbar lösning är att byta ut motorerna mot starkare varianter. Vid leverans så nyttjar maskinen toppmoderna komponenter och mjukvara, från framstående svenska och internationella producenter. En intervju med Isak Emericks tillsammans med 3D skrivaren hittas i Bilaga B, i formen av ett nyhetsbrev.

The so-called Industry 4.0 is by its agitators commonly denoted as the fourth industrial revolution and promises to turn the manufacturing sector on its head. However, everything that glimmers is not gold and in the backwash of hefty consultant fees questions arises: What are the drivers behind Industry 4.0? Which barriers exists? How does one prepare its manufacturing procedures in anticipation of the (if ever) coming era? What is the internet of things and what file sizes’ is characterised as big data? To answer these questions, this thesis aims to resolve the ambiguity surrounding the definitions of Industry 4.0, as well as clarify the fuzziness of a data-driven manufacturing approach. Ergo, the comprehensive usage of data, including collection and storage, quality control, and analysis. In order to do so, this thesis was carried out as a case study at GKN Aerospace Sweden (GAS). Through interviews and observations, as well as a literature review of the subject, the thesis examined different process’ data-driven needs from a quality management perspective. The findings of this thesis show that the collection of quality data at GAS is mainly concerned with explicitly stated customer requirements. As such, the data available for the examined processes is proven inadequate for multivariate analytics. The transition towards a data-driven state of manufacturing involves a five-stage process wherein data collection through sensors is seen as a key enabler for multivariate analytics and a deepened process knowledge. Together, these efforts form the prerequisites for Industry 4.0. In order to effectively start transition towards Industry 4.0, near-time recommendations for GAS includes: capture all data, with emphasize on process data; improve the accessibility of data; and ultimately taking advantage of advanced analytics. Collectively, these undertakings pave the way for the actual improvements of Industry 4.0, such as digital twins, machine cognition, and process self-optimization. Finally, due to the delimitations of the case study, the findings are but generalized for companies with similar characteristics, i.e. complex processes with low volumes.