There are many contributing factors that influence the development, deployment, and use of lean manufacturing techniques. This study will focus on an automotive factory production system but will gather data across many areas. The concepts presented in the available research will then be related to lean manufacturing techniques at a union-based automotive factory. Several factors to focus on during this study are the Industry 4.0 movement, production systems, deployment and implementation strategies, lean manufacturing, persuasive technology, and manufacturing culture. These factors play a significant role in developing and implementing core techniques, which would lead to best in class metrics. The study will also experiment with different technologies and apply these finding to an assembly line. Two contributions that this research will add to the body of knowledge: 1. An action research deployment of instant feedback stations at operators’ workstations with results and analysis of quality outputs. 2. An action research deployment of instant feedback production check sheets from operators to management with results and analysis of quality outputs.

Ships and vessel conditions demand regular assessment to maintain their safety. In the traditional environment, their assessment was conducted using surveys and complex and time-consuming operations. But now, with the emergence of Industry 4.0 where intelligence and smart devices serve the imagery, drone-based, and many other alternative methods for inspection, the subject is obtaining considerable interest. The concept is highly effective with low cost and less disruption delivering a safer inspection approach. This study has examined Industry 4.0 technology as a quality inspection technique of a ship/vessel, examined drone-based ship inspection techniques for quality inspection of the ship/vessel without human involvement, to analyse robotic underwater surveillance methods for quality inspection of the ship/vessel, and to identify vision-based corrosion detection techniques for quality inspection of the ship/vessel. In the finding, it was revealed ship inspection through Industry 4.0 technology and other techniques can help the marine industries rely more on automated systems to gather the information that is required to be capable of authenticating process and product conformance also they can reduce human error, risks and uncover useful insights from the gathered vessel/ship data.

Background Manufacturers have to adopt modern technologies to compete at the top of their field. However, adopting new technologies can be expensive and difficult to validate prior to implementation. One technology that has difficulties receiving a wider application is bin picking. Bin picking uses vision technology to communicate with an industrial robot. Consequently, the technology enables robots to pick randomly sorted objects. Research finds that the difficulty in assessing its performance can explain the lacklustre application of bin picking. In addition, research on bin picking is primarily focused on its technical difficulties and neglects information that can be valuable for potential adopters.  Objectives This thesis aims to aid decision-makers in assessing the implications and opportunities of bin picking. Furthermore, the thesis desire to inspire potential adopters by analysing the viability of bin picking through feasibility and the tangible and intangible benefits in a real-world setting. Methods A utility function is developed and assigned categories based on interviews with suppliers and adopters of the technology and literature review. The utility function highlights the feasibility and the intangible benefits of a bin picking solution and enables ranking among alternatives. The highest scoring article is used to conduct a feasibility study in collaboration with suppliers of bin picking technology. Based on the feasibility studies, a DES is created to highlight the implications that may arise in a real manufacturing environment. Finally, financial calculations through NPV, IRR, PP and DPP are created to evaluate the solution. Results All NPV calculations (excluding a 12.5 \% discount rate) are positive with enough years. The IRR is positive when the time span exceeds 11 years. The PP is 10-11 years while the DPP is 12-13, 14-15, 19-20, 32-33 years at a discount rate of 2.5 \%, 5 \%, 7.5 \% respectively 10 \%. However, the investment is never recoupable at a discount rate of 12.5 \%. The categories of the utility function have a clear impact on the feasibility and intangible benefits of the technology in a real-world setting. Bin picking relieves MMH tasks for operators, frees up facility space and reduces the collision risk. However, there are several risks with a bin picking solution. Conclusions Bin picking can become financially viable through automating MMH processes. However, how much capital is released depends on the man-hours spent in the previous process. The feasibility of bin picking implementation is dependent on the geometric complexity of the article, the sorting method inside the bin, the surrounding environment and the time margin. Decision-makers need to account for these factors prior to implementation. The intangible benefits can incentivise decision-makers to implement bin picking, even if the financial calculations show a net loss on the investment. / Bakgrund Tillverkare måste anta moderna tekniker för att kunna konkurrera vid toppen av sin bransch. Det kan dock vara både dyrt och svårt att validera ny teknologi före implementation. En teknologi som har haft det svårt att nå en bredare tillämpning är bin picking. Bin picking använder visionteknik för att kommunicera med industrirobotar. Teknologin gör det möjligt för robotar att plocka slumpmässigt sorterade objekt. Den låga tillämpningen av teknologin kan enligt forskare bero på svårigheterna med att bedöma dess prestanda. Forskning fokuserar dessutom främst på de tekniska problemen med bin picking och försummar information som är värdefull för potentiella användare. Syftet Syftet med denna studie är att tillhandahålla underlag för beslutsfattare att bedöma konsekvenserna och möjligheterna med bin picking. Vidare avser studien att inspirera potentiella användare genom att analysera lönsamheten av bin picking via dess genomförbarhet och dess materiella samt immateriella förmåner i en verklig miljö. Metod En nyttofunktion utvecklas och tilldelas kategorier baserat på intervjuer med leverantörer och antagare av teknologin samt från tidigare litteratur. Nyttofunktionen lyfter fram genomförbarheten samt de immateriella förmånerna i en bin  picking lösning, dessutom möjliggör den rangording mellan alternativ. Artikeln som rankas högst används för att genomföra en förstudie tillsammans med leverantörer av bin picking teknologi. En DES som baseras på förstudierna skapas för att lyfta fram de implikationer som kan uppstå i en verklig produktionsmiljö. Slutligen utvärderas lösningen genom finansiella medel, innefattande NPV, IRR, PP och DPP. Resultat Alla NPV-beräkningar (exklusive vid en diskonteringsränta på 12,5 \%) är positiva efter tillräckligt många år. IRR är positivt när tiden överstiger 11 år. PP är 10-11 år, medans DPP är 12-13, 14-15, 19-20, 32-33 år med en diskonteringsränta på 2,5 \%, 5 \%, 7,5 \% respektive 10 \%. Investeringen är dock aldrig återbetalningsbar vid en diskonteringsränta på 12,5 \%. Nyttofunktionens kategorier har en tydlig påverkan gällande teknologins genomförbarhet och immateriella fördelar i en verklig produktionsmiljö. Bin picking avlastar operatörer från MMH-uppgifter, frigör fabriksyta och minskar kollisionsrisken. Det finns dock flera risker med en bin picking lösning. Slutsats Bin picking kan vara ekonomiskt lönsamt genom att automatisera MMH processer. Hur mycket kapital som frigörs beror dock på det antal arbetstimmar som spenderas vid den manuella processen. Genomförbarheten vid implementeringen av bin picking är beroende av artikelns geometriska komplexitet, sorteringsmetod, omgivningen och tidsmarginal. Detta är faktorer som beslutsfattare måste ta hänsyn till före implementation. De immateriella fördelarna kan ge beslutsfattare incitament att införa bin picking, även om de finansiella beräkningar visar en förlust vid en investering.

Background: The use of technologies and digitization has become unavoidable in recent years to improve organizational efficiency and competitiveness. Even though companies are still adopting Industry 4.0 technologies, the next industrial revolution is approaching. Researchers argue for the importance of human-centricity in industries to provide benefits for all stakeholders involved. However, in literature, it is unexplored how human-centricity can be achieved with Industry 4.0 technologies, especially in a supply chain context.  Purpose: The purpose of this study is to explore how Industry 4.0 technologies can support a human-centric supply chain. Method: An exploratory, qualitative study was conducted to gain an in-depth understanding of the research phenomenon. A holistic multiple case study provided empirical findings through semi-structured interviews. An inductive thematic analysis has been performed, which enabled the findings to emerge from the data. Conclusion: The human-centric focus is being acknowledged and the case companies are actively focusing on technological implementations to improve it. Thecreation of a safe, creative, and attractive work environment, where workers are actively involved in implementation processes, and heavy labor and repetitive tasks are replaced, will result in a positive cycle towards human-centricity and technological innovations. Companies will first have to overcome implementation barriers and focus on technological innovations that support human-centricity within the boundaries of the firms. This can then be followed by enhanced supply chain collaborations.

To meet the demand of future building requirements, and to improve productivity and competitiveness, there is a need to modernize and revise the current practices in the wooden single-family house industry. In several other sectors, intensive work is being done to adapt to the anticipated fourth industrial revolution. The manufacturing industry has already begun its transformation with concepts such as smart manufacturing and Industry 4.0. So far, smart manufacturing has not been discussed to any significant extent for the wooden single-family house industry, even though it might be a way for this industry to improve productivity and competitiveness. The research presented in this thesis aims at increased knowledge about what smart manufacturing means for the wooden single-family house industry. This requires investigating what smart wooden house manufacturingis, what challenges that might be associated with it, and how smart wooden house manufacturing can be realized. At the core of this thesis is the conceptualization of smart wooden house manufacturing—when realized, it is expected to contribute to improve the competitiveness of the wooden single family house industry. The findings presented here are based on three Research Studies. Two studies were case studies within the wooden single-family house industry. The third study was a traditional literature review. The findings revealed two definitions and 26 components of smart wooden house manufacturing. At large, smart wooden house manufacturing emphasizes digital transformation with a focus on digital information flow, how to add information, information compilation, and information distribution between systems/programs and departments. Some of the challenges associated with smart wooden house manufacturing are, e.g. culture, competence and manual transfer of information between systems. The findings indicate similarities of smart wooden house manufacturing within certain components of industrialized house building and Industry 4.0, these components could enable the realization of smart wooden house manufacturing. / För att möta efterfrågan på framtida byggkrav och för att förbättra produktiviteten och konkurrenskraften finns det ett behov av att modernisera och revidera nuvarande tillvägagångssätt inom träsmåhusindustrin. I flera andra sektorer arbetas det intensivt med att anpassa sig till den förväntade fjärde industriella revolutionen. Tillverkningsindustrin har redan påbörjat sin omvandling med koncept som smart manufacturing och Industry 4.0. Hittills har smart manufacturing inte diskuterats i någon större utsträckning för träsmåhusindustrin, även om det kan vara ett sätt för denna industri att förbättra produktiviteten och konkurrenskraften. Forskningen som presenteras i denna avhandling syftar till ökad kunskap om vad smart manufacturing innebär för träsmåhusindustrin. Detta kräver undersökning av vad smart trähustillverkning är, vilka utmaningar som kan vara förknippade med det och hur smart trähustillverkning kan realiseras. Kärnan i denna uppsats är begreppsframställningen av smart trähustillverkning—när det realiserats förväntas det bidra till att förbättra konkurrenskraften för träsmåhusindustrin. Resultaten som presenteras här är baserat på tre forskningsstudier. Två studier var fallstudier inom träsmåhusindustrin. Den tredje studien var en traditionell litteraturstudie. Resultaten avslöjade två definitioner och 26 komponenter av smart träshustillverkning. Sammanfattningsvis betonar smart trähustillverkning digital transformation med fokus på digitalt informationsflöde, hur man lägger till information, sammanställning av information och informationsfördelning mellan system / program och avdelningar. Några av utmaningarna associerade med smart trähustillverkning är t.ex. kultur, kompetens och manuell överföring av information mellan system. Resultaten indikerar likheter mellan smart träshustillverkning inom vissa komponenter av industriellt husbyggande och Industry 4.0, dessa komponenter skulle kunna möjliggöra realiseringen av smart trähustillverkning.

yes / Applications of autonomous systems are becoming increasingly common across the field of engineered systems from cars, drones, manufacturing systems and medical devices, addressing prevailing societal changes, and, increasingly, consumer demand. Autonomous systems are expected to self-manage and self-certify against risks affecting the mission, safety and asset integrity. While significant progress has been achieved in relation to the modelling of safety and safety assurance of autonomous systems, no similar approach is available for resilience that integrates coherently across the cyber and physical parts. This paper presents a comprehensive discussion of resilience in the context of robotic autonomous systems, covering both resilience by design and resilience by reaction, and proposes a conceptual model of a system of learning for resilience assurance in a continuous product development framework. The resilience assurance model is proposed as a composable digital artefact, underpinned by a rigorous model-based resilience analysis at the system design stage, and dynamically monitored and continuously updated at run time in the system operation stage, with machine learning based knowledge extraction and validation.

Yes / Today’s factories are considered as smart ecosystems with humans, machines and devices interacting with each other for efficient manufacturing of products. Industry 4.0 is a suite of enabler technologies for such smart ecosystems that allow transformation of industrial processes. When implemented, Industry 4.0 technologies have a huge impact on efficiency, productivity and profitability of businesses. The adoption and implementation of Industry 4.0, however, require to overcome a number of practical challenges, in most cases, due to the lack of modernisation and automation in place with traditional manufacturers. This paper presents a first of its kind case study for moving a traditional food manufacturer, still using the machinery more than one hundred years old, a common occurrence for small- and medium-sized businesses, to adopt the Industry 4.0 technologies. The paper reports the challenges we have encountered during the transformation process and in the development stage. The paper also presents a smart production control system that we have developed by utilising AI, machine learning, Internet of things, big data analytics, cyber-physical systems and cloud computing technologies. The system provides novel data collection, information extraction and intelligent monitoring services, enabling improved efficiency and consistency as well as reduced operational cost. The platform has been developed in real-world settings offered by an Innovate UK-funded project and has been integrated into the company’s existing production facilities. In this way, the company has not been required to replace old machinery outright, but rather adapted the existing machinery to an entirely new way of operating. The proposed approach and the lessons outlined can benefit similar food manufacturing industries and other SME industries. / Innovate UK—Knowledge Transfer Partnerships (KTP010551)

With the developments of industry 4.0, data analytics solutions and their applications have become more prevalent in the manufacturing industry. Currently, the typical software architecture supporting these solutions is modular, using separate software for data collection, storage, analytics, and visualization. The integration and maintenance of such a solution requires the expertise of an information technology team, making implementation more challenging for small manufacturing enterprises. To allow small manufacturing enterprises to more easily obtain the benefits of industry 4.0 data analytics, a full-stack data analytics framework is presented and its performance evaluated as applied in the common industrial analytics scenario of predictive maintenance. The predictive maintenance approach was achieved by using a full-stack data analytics framework, comprised of the PTC Thingworx software suite. When deployed on a lab-scale factory, there was a significant increase in factory uptime in comparison with both preventative and reactive maintenance approaches. The predictive maintenance approach simultaneously eliminated unexpected breakdowns and extended the uptime periods of the lab-scale factory. This research concluded that similar or better results may be obtained in actual factory settings, since the only source of error on predictions would not be present in real world scenarios.

With the advances of industry 4.0, augmented reality (AR) devices are being deployed across the manufacturing sector to enhance worker perception and efficiency. AR is often used to deliver spatially relevant work instructions on mobile devices for maintenance procedures on the factory floor. In these situations, workers use their mobile devices to view instructions in the form of 3D animations and annotations that directly overlay the equipment being maintained. Workers then follow the AR instructions and must ultimately rely on their own judgement and knowledge of the procedure as they progress from step to step. An AR assistant that could validate each stage of the procedure in real time and provide the worker with feedback on any observed errors would ensure that each maintenance procedure is completed successfully. This work presents a mobile, quality inspection system for AR maintenance procedures that is capable of assessing the maintenance task in real time. The system is designed for deployment on handheld mobile devices and can thus manage the challenges inherent to performing quality inspection with a non-fixed vision system. This work enumerates four essential qualities of mobile quality inspection tools and outlines some of the challenges encountered during the development of such a system. In the end, testing established that the system could provide adequate assistance for capturing inspection images, accurately process the captured images using machine vision, and generate detailed feedback from the quality inspection in a timely manner.

The manufacturing industry is continuously evolving as new practices and technology are adopted to improve productivity and remain competitive. There have been three well established manufacturing revolutions in recent history and some say that the fourth is occurring currently by the name of Smart Manufacturing, Indusrie 4.0, and others. This latest manufacturing revolution is highly dependent on industrial connectivity. This research aims to gage the ability of Industrial Innovation Platforms (IIPs) to reduce complexity of implementing base-line industrial connectivity for small and medium-sized enterprises (SMEs). The results of this study would be especially relevant to decision makers in industrial SMEs who are considering implementing industrial connectivity as well as providing insights into approaches for establishing base-line industrial connectivity. The research methodology consists of three main steps: 1) creation of IIP and non-IIP connectivity solutions that enable connectivity of the vast amount of industrial equipment, 2) Gathering measures from solutions in accordance with metrics identified for complexity evaluation, 3) discussion and interpretation of data To have a more complete analysis, quantitative and qualitative data was used and evaluated to address the varying elements of the broad task of establishing industrial connectivity. The research showed that IIPs can reduce complexity for select industrial equipment. Some industrial equipment have robust and streamlined connectivity solutions provided by the IIP. In these cases, the IIP almost certainly will reduce the complexity of establishing connectivity. Other industrial equipment have a solution provided by the IIP which requires piecing together and some component modifications. In these cases, the IIPs reduce complexity of establishing connectivity dependent on circumstances. Lastly, when no form of solution is available through the IIP for the industrial equipment, the IIP's has no ability to reduce complexity other than hosting the server used in connectivity. These findings open additional avenues of research which could improve the understanding of benefits IIPs may provide to SMEs.