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With the increase in food consumption, new ideas, and technologies began to be developed. Inaddition, the developments generated by Industry 4.0 technologies have started to be applied tothe entire manufacturing sector and the indoor farming industry, which is currently trending.Many studies and articles have been prepared on this subject, and the main goal of each study isto produce quality products and to ensure continuity in production to cover the nonendingincrease in demand.

This paper discusses how simulation technology, which is one of the industry 4.0 technologies,can be used in the production system development of the indoor farming industry. According tomany researchers, the biggest obstacle for the vertical farming industry is start-up cost, andsimulation technologies can be the solution for this since it allows future production systems tobe analyzed without any investment. To have a clear vision of how these technologies can beadapted in the indoor farming industry, this paper will find the answers to these questions, RQ1:How can simulation facilitate production system development and Industry 4.0 projects withinthe indoor farming industry? RQ2: What are the benefits and challenges when using simulationas a tool for production system development within the indoor farming industry? To reach thegoal of this paper, the case study method was used, and an indoor farming company was selectedto get more realistic data about the vertical farming system. BlueRedGold AB is a start-upcompany in the indoor farming industry, and it has a huge growth potential since they aim totransform its current production lines to be fully automated. Many articles and studies were usedto approach the solution of the research questions from a more technical and academic point ofview, and the analysis of these articles was carried out with the structured literature review method.

After conducting this research, answers have been obtained for the research questions. Theauthors' solution to the layout issue, one of the case company's main challenges as indicated inthis study, was developed after extensive simulation model testing. As highlighted in this paper,it has been stated by many researchers, there are several simulation approaches to follow.However, the authors have developed a simulation modeling approach to be followed in theindoor vertical farming industry to overcome the complexity of these systems as well as thesimulation program complexity. In addition, several challenges and benefits have beenhighlighted in this paper such as the lack of ready models of the equipment used in indoorfarming which requires a knowledge of a programing language to overcome. Finally, despitechallenges, simulation technology can provide an applicable solution for production systemproblems of vertical farming companies/organizations to obtain continuous improvementphilosophy which is the main principle of Lean thinking. The generated simulation model in thisthesis project was successfully implemented, demonstrating how this technology might be aneffective solution for complex production systems as in the indoor farming sector. 

Recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a stakeholder proposes a new requirement, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. Several NLP approaches for similarity computation between requirements are available. However, there is little empirical evidence on their effectiveness for code retrieval. This study compares different NLP approaches, from lexical ones to semantic, deep-learning techniques, and correlates the similarity among requirements with the similarity of their associated software. The evaluation is conducted on real-world requirements from two industrial projects from a railway company. Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models. Practitioners confirm that requirements similarity is generally regarded as a proxy for software similarity. However, they also highlight that additional aspect comes into play when deciding software reuse, e.g., domain/project knowledge, information coming from test cases, and trace links. Our work is among the first ones to explore the relationship between requirements and software similarity from a quantitative and qualitative standpoint. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and change impact analysis.

Requirements prioritization plays an important role in driving project success during software development. Literature reveals that existing requirements prioritization approaches ignore vital factors such as interdependency between requirements. Existing requirements prioritization approaches are also generally time-consuming and involve substantial manual effort. Besides, these approaches show substantial limitations in terms of the number of requirements under consideration. There is some evidence suggesting that models could have a useful role in the analysis of requirements interdependency and their visualization, contributing towards the improvement of the overall requirements prioritization process. However, to date, just a handful of studies are focused on model-based strategies for requirements prioritization, considering only conflict-free functional requirements. This paper uses a meta-model-based approach to help the requirements analyst to model the requirements, stakeholders, and inter-dependencies between requirements. The model instance is then processed by our modified PageRank algorithm to prioritize the given requirements. An experiment was conducted, comparing our modified PageRank algorithm’s efficiency and accuracy with five existing requirements prioritization methods. Besides, we also compared our results with a baseline prioritized list of 104 requirements prepared by 28 graduate students. Our results show that our modified PageRank algorithm was able to prioritize the requirements more effectively and efficiently than the other prioritization methods.

Over the years, a range of Internet of Vehicles services has emerged, along with improved quality parameters. However, the field still faces several limitations, including resource constraints and the time response requirement. This paper extracts cost, energy, processing power, service management, and resource allocation parameters. Mathematical equations are then defined based on these parameters. To simplify the process complexity and ensure scalability, we propose an algorithm that uses the genetic algorithm for fault and cost management during resource allocation to services. The main concept is to pick resources for services using a genetic algorithm. We discuss the processing and energy costs associated with this function, which is the algorithm's objective function and is created to optimize cost. Our approach goes beyond the conventional genetic algorithm in two stages. In the first step, services are prioritized, and resources are allocated in accordance with those priorities; in the second step, load balancing in message transmission paths is ensured, and message failures are avoided. The algorithm's performance is evaluated using various parameters, and it was shown to outperform other metaheuristic algorithms like the classic genetic algorithm, particle swarm, and mathematical models. Different scenarios with various nodes and service variables are defined in various system states, including fault occurrences to various percentages of 10, 20, and 30. To compare methods, we consider different parameters, the most significant being performance success rate. Moreover, the cost optimization has a good convergence after iterations, and the rate of improvement in the big scenario has slowed down after 150 iterations. Besides, it provides acceptable performance in response time for services.

Industrial cybersecurity has risen as an important topic of research nowadays. The heavy connectivity by the Internet of Things (IoT) and the growth of cyberattacks against industrial assets cause this risen and attract attention to the cybersecurity field. While fostering current software applications and use-cases, the ubiquitous access to the Internet has also exposed operational technologies to new and challenging security threats that need to be addressed. As the number of attacks increases, their visibility decreases. An attack can modify the Cyber-Physical Systems (CPSs) quality to avoid proper quality assessment. They can disrupt the system design process and adversely affect a product’s design purpose. This working progress paper presents our approach to modeling, analyzing, and mitigating cyberattacks in CPS. We model the normal behavior of the application as well as cyberattacks with the help of Microsoft Security Development Lifecycle (SDL) and threat modeling approach (STRIDE). Then verify the application and attacks model using a model checking tool and propose mitigation strategies to decrease the risk of vulnerabilities. The results can be used to improve the system design to overcome the vulnerabilities.

This article presents a runtime verification tool for embedded software executing under the open source real-time operating system FreeRTOS. The tool detects and diagnoses concurrency bugs such as deadlock, starvation, and suspension based-locking. The tool finds concurrency bugs at runtime without debugging and tracing the source code. The tool uses the Tracealyzer tool for logging relevant events. Analysing the logs, our tool can detect the concurrency bugs by applying algorithms for diagnosing each concurrency bug type individually. In this paper, we present the implementation of the tool, as well as its functional architecture, together with illustration of its use. The tool can be used during program testing to gain interesting information about embedded software executions. We present initial results of running the tool on some classical bug examples running on an AVR 32-bit board SAM4S. 

Non-invasive monitoring of cardiovascular health through photoplethysmography (PPG) waveforms has emerged as a crucial area of research. The Windkessel 4-Element (WK4) model is a mathematical approach used to estimate key physiological parameters related to cardiovascular health, including arterial compliance, peripheral resistance, inertance, and total arterial resistance. This study aimed to evaluate key physiological parameters associated with cardiovascular health using the WK4 model, leveraging real-life PPG waveform data obtained from volunteers across three distinct age groups. To achieve this, an algorithm was developed to automatically determine optimal parameter values for each volunteer. The results revealed a mean correlation coefficient of 0.96 between the automatically generated waveforms by the algorithm and the actual real-life PPG waveforms, indicating robust agreement. Notably, only the total arterial resistance parameter exhibited significant differences among the age groups, suggesting that the algorithm holds promise for detecting agerelated changes in cardiovascular health. These findings emphasize the potential for the development of a non-invasive tool to assess cardiovascular health status and enhance healthcare outcomes. Furthermore, they underscore the capability of the developed algorithm as a non-invasive means to evaluate various aspects of cardiovascular physiology. Additionally, the versatility of this algorithm opens doors for its application in educational settings, promoting knowledge advancement, empowering research endeavors, and facilitating advancements in the field.

Accurate bladder monitoring is critical in the management of conditions such as urinary incontinence, voiding dysfunction, and spinal cord injuries. Electrical bioimpedance (EBI) has emerged as a cost-effective and non-invasive approach to monitoring bladder activity in daily life, with particular relevance to patient groups who require measurement of bladder urine volume (BUV) to prevent urinary leakage. However, the impact of activities in daily living (ADLs) on EBI measurements remains incompletely characterized. In this study, we investigated the impact of normal ADLs such as sitting, standing, and walking on EBI measurements using the MAX30009evkit system with four electrodes placed on the lower abdominal area. We developed an algorithm to identify artifacts caused by the different activities from the EBI signals. Our findings demonstrate that various physical activities clearly affected the EBI measurements, indicating the necessity of considering them during bladder monitoring with EBI technology performed during physical activity (or normal ADLs). We also observed that several specific activities could be distinguished based on their impedance values and waveform shapes. Thus, our results provide a better understanding of the impact of physical activity on EBI measurements and highlight the importance of considering such physical activities during EBI measurements in order to enhance the reliability and effectiveness of EBI technology for bladder monitoring.

Bladder monitoring, including urinary incontinence management and bladder urinary volume monitoring, is a vital part of urological care. Urinary incontinence is a common medical condition affecting the quality of life of more than 420 million people worldwide, and bladder urinary volume is an important indicator to evaluate the function and health of the bladder. Previous studies on non-invasive techniques for urinary incontinence management technology, bladder activity and bladder urine volume monitoring have been conducted. This scoping review outlines the prevalence of bladder monitoring with a focus on recent developments in smart incontinence care wearable devices and the latest technologies for non-invasive bladder urine volume monitoring using ultrasound, optical and electrical bioimpedance techniques. The results found are promising and their application will improve the well-being of the population suffering from neurogenic dysfunction of the bladder and the management of urinary incontinence. The latest research advances in bladder urinary volume monitoring and urinary incontinence management have significantly improved existing market products and solutions and will enable the development of more effective future solutions.

Development in the field of smart wearable products for monitoring daily life health status is beginning to spread in society. Textile electronic methods are improving and facilitating the manufacturing of sensorized garments. This paper evaluates a newly developed t-shirt incorporating electronic sensing and interconnecting elements integrated into the T-shirt with textile-friendly techniques sensorized with a Movesense device for monitoring ECG and HR and activity. The measurement results obtained from the t-shirt are entirely in agreement with the measurements obtained with other textile garments and encourage us for a near future where wearable sensors are just textile garments sensorized seamlessly without suboptimal textile-electronic integrated elements.

The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications.

The aim of the study is to assist the management of Ica Skrapan Västerås with promotional suggestions of innovation and creativity, as well as to analyze and address the current lack of engagement and motivation of the employees working in the grocery store. A qualitative and semi- structured research method has been conducted to collect data for the study. Using the data collected, the study has been able to analyze the current lack of engagement and motivation to see if innovative and creative suggestions can improve employee’s existence at work. The results have shown that both the management has a willingness to implement their employees ‘views, and the employees have a willingness to be involved in more creative and innovative proposals within the organization.

In today's modern work environment, the demand for innovation and creativity is crucial for the survival and success of organizations. The role of leadership is central in creating an environment that promotes creativity and idea generation among employees. This study aims to investigate the role of transformational leadership in creating an environment that promotes creativity and idea generation among employees in technology companies. The method section carefully describes the research methodology and the selection of respondents, as well as the conduct of the interviews and the J&D test. In the study, three different respondent groups were interviewed: two employees, two managers and an informant, in order to answer the research question. The results of these interviews provided insights into how transformative leadership promotes creativity and idea generation in the workplace. Furthermore, issues related to research ethics and how these have been handled and taken into account during the research process are also discussed. In the theory section, the concept of leadership is defined and three different leadership styles are discussed: transformative, transactional and servant leadership. Furthermore, the concept of creativity is explored, including individual, group and organizational creativity, as well as innovation and idea generation. In the empirical section, the results of the interviews and the J&D test are presented. The interviews have been analyzed using the thematic analysis method and organized under different themes, each of which is linked to a specific overarching theme. The analysis from the interviews shows that transformative leaders create an environment that inspires and engages employees, while challenging traditional thought patterns and encouraging divergent thinking. The results of the J&D test provide further insight into the creative abilities of the participants and show that these technology companies with a transformative leadership have a favorable work environment for individual creativity and idea generation. The conclusion is that transformative leadership is crucial for fostering creativity and idea generation within technology companies, and such a leadership style is a key factor for successful innovation processes.

In this paper, we present a hierarchical model for time predictable task scheduling in edge-cloud computing architecture for industrial cyber-physical systems. Regarding the scheduling problem, we also investigate the common problem-solving approaches and discuss our preliminary plan to realize the proposed architecture. Furthermore, an Integer linear programming (ILP) model is proposed for task scheduling problem in the cloud layer. The model considers timing and security requirements of applications and the objective is to minimize the financial cost of their execution.

The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs.

A hybrid cloud is an efficient solution to deal with the problem of insufficient resources of a private cloud when computing demands increase beyond its resource capacities. Cost-efficient workflow scheduling, considering security requirements and data dependency among tasks, is a prominent issue in the hybrid cloud. To address this problem, we propose a mathematical model that minimizes the monetary cost of executing a workflow and satisfies the security requirements of tasks under a deadline. The proposed model fulfills data dependency among tasks, and data transmission time is formulated with exact mathematical expressions. The derived model is a Mixed-integer linear programming problem. We evaluate the proposed model with real-world workflows over changes in the input variables of the model, such as the deadline and security requirements. This paper also presents a post-optimality analysis that investigates the stability of the assignment problem. The experimental results show that the proposed model minimizes the cost by decreasing inter-cloud communications for dependent tasks. However, the optimal solutions are affected by the limitations that are imposed by the problem constraints. 

Task offloading is a prominent problem in edge−cloud computing, as it aims to utilize the limited capacityof fog servers and cloud resources to satisfy the QoS requirements of tasks, such as meeting their deadlines.This paper formulates the task offloading problem as a nonlinear mathematical programming model to maximizethe number of independent IoT tasks that meet their deadlines and to minimize the deadline violationtime of tasks that cannot meet their deadlines. This paper proposes two Q-learning algorithms to solve theformulated problem. The performance of the proposed algorithms is experimentally evaluated with respect toseveral algorithms. The evaluation results demonstrate that the proposed Q-learning algorithms perform wellin meeting task deadlines and reducing the total deadline violation time.

In today's ever-automating world, businesses are increasingly focused on optimizing network operations, including configuration, management, and troubleshooting. This study scrutinizes the comparison between manual and automated methodologies within the framework of the cloud-based Cisco Meraki platform. Our objective is to establish decisive criteria for identifying the most advantageous circumstances for automation, particularly in network configuration. To achieve this, we constructed a network consisting of three Meraki devices. Automation of network configuration was facilitated through the development of Python scripts leveraging the Meraki API library in conjunction with configuration files. The configuration files were constructed in various file formats–Excel, CSV, JSON, and YAML– in order to assess their impact on automation effectiveness. Moreover, manual configuration tasks were undertaken by three network technicians with varying levels of Meraki proficiency. A comprehensive series of tests, encompassing both automated and manual methods, was subsequently conducted, focusing on metrics such as execution time and keystroke count. Through meticulous analysis, we provided valuable insights into the effectiveness of automation in network configuration processes. Results demonstrated that the use of JSON files in the automation process yielded the shortest completion time and required the fewest keystrokes compared to other automation methods. Moreover, manual configuration via the Meraki dashboard GUI exhibited comparable efficiency to automation methods in networks with a small number of devices. Nonetheless, owing to the capacity to reuse configuration files with minor adjustments for similar setups, the true advantage of automation over manual configuration becomes apparent as the device count increases.

Date :  4th June 2023

Level : Masters thesis in industrial engineering and management, advanced level, 30 ECTS

Institution : School of Innovation, Design and Engineering, Mälardalens University  

Authors :

  Afnan Abdul Amir

9 Maj 1998Onel Nabeel Polus

24 November 1998

Title: How to identify Adaptability within SMEs: A case Study

Supervisor: Mikael Johnsson

Keywords: Change Management, Adaptability, SMEs

Research Question : How can consult agencies identify the adaptability within SMEs?.

Purpose: The purpose of this study was to develop a process for the identification of  SMEs adaptability.

Method: This study adopted a qualitative research methodology using a abductive approach. This method facilitated an iterative exchange between the theoretical framework and empirical observations. The theoretical framework was based on scientific articles and the study was conducted as a case study to help the company identify the adaptability within its clients. The empirical data was collected through structured and semi-structured interviews divided between the different clients. The empirical data was structured on 7 interviews with project managers, team leaders and CEOs.

Conclusion: The conclusion that can be drawn from this study is that the adaptability to various market changes can be identified when a company analyzes the following three key factors: Leadership, Communication, and Knowledge.

Cardiovascular diseases (CVDs) are a leading cause of death worldwide, and hypertension is a major risk factor for acquiring CVDs. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. In this study, a linear SVM machine learning model was used to classify subjects as normal or at different stages of hypertension. The features combined statistical parameters derived from the acceleration plethysmography waveforms and clinical parameters extracted from a publicly available dataset. The model achieved an overall accuracy of 87.50% on the validation dataset and 95.35% on the test dataset. The model's true positive rate and positive predictivity was high in all classes, indicating a high accuracy, and precision. This study represents the first attempt to classify cardiovascular conditions using a combination of acceleration photoplethysmogram (APG) features and clinical parameters The study demonstrates the potential of APG analysis as a valuable tool for early detection of hypertension.

The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task. Various feature extraction methods have been proposed in the literature. In this study, we present a novel fiducial point extraction algorithm to detect c and d points from the acceleration photoplethysmogram (APG), namely “CnD”. The algorithm allows for the application of various pre-processing techniques, such as filtering, smoothing, and removing baseline drift; the possibility of calculating first, second, and third photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting APG fiducial points. An evaluation of the CnD indicated a high level of accuracy in the algorithm’s ability to identify fiducial points. Out of 438 APG fiducial c and d points, the algorithm accurately identified 434 points, resulting in an accuracy rate of 99%. This level of accuracy was consistent across all the test cases, with low error rates. These findings indicate that the algorithm has a high potential for use in practical applications as a reliable method for detecting fiducial points. Thereby, it provides a valuable new resource for researchers and healthcare professionals working in the analysis of photoplethysmography signals.

Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat’s performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.

In recent years, the field of prosthetics has made significant progress towards creating prosthetic devices that are more functional, comfortable, and user-friendly. However, achieving intuitive control over prosthetic hand movements remains a significant challenge, especially for individuals with limb loss who rely on prosthetics for independent daily activities. To address this challenge, researchers have explored the potential of non-invasive techniques as electromyography (EMG) for prosthetic control. This paper aims to investigate the potential of using EMG and the electrical impedance myography (EIMG) techniques jointly for the measurement of hand movements. The study involved recording and comparing EMG and EIMG signals from a cohort of healthy individuals. These signals were captured during four distinct hand gestures: opening and closing the hand, as well as extending and flexing it, under varying time conditions, allowing for categorization into low and high-intensity movements. Data collection employed the Open BCI and ZRPI devices. The analysis of these signal waveforms revealed compelling results. Brachioradialis activity in EMG 2 exhibited an increase during open hand (0.015mV) and extension hand (0.009mV in low and 0.013mV in high intensity) gestures, accompanied by increased EIMG activity (56mV and 52mV respectively). Additionally, close hand (0.0018mV in low and 0.05mV in high intensity) and flexion hand (0.0075 in low intensity and 0.002 in high intensity) gestures exhibited heightened flexor carpi ulnaris activity with raised EIMG activity (57mV and 45mV respectively). These results proved to be consistent, acceptable, and aligned with existing literature. The findings of this paper indicate that both EMG and EIMG techniques could be used together to control custom-made hand prosthetics, demonstrating a significant development that could lead to more intuitive and easier-to-control prosthetics. Also, the results obtained could be valuable to researchers and engineers working in the prosthetics field, as it provides insights into the potential of non-invasive techniques for prosthetic control.

Administering the medications and fluids intravenously is a frequent practice in modern medical procedures, which plays a vital role in the treatment of certain acute conditions which require immediate action by drugs or fluids. This paper covers the design of a low-cost, wireless drip monitoring system for use in the hospital environment. The device is equipped with the Bluetooth low energy based battery-operated microcontroller, an infrared based drops counting system and a digital servo motor to control the drip flow rate, and it is attached to an existing intravenous stand. A LabView graphical user interface has also been developed to provide sets of input to the system to calculate the desired drip rate and the amount of pressure that digital servo motor must apply to achieve it. The system shows an average accuracy of 96% when compared with the measured and calculated values. This allows accurate computation of the level of the drip.

Cardiovascular diseases (CVDs) are a leading cause of death worldwide, with hypertension emerging as a significant risk factor. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. This work proposes a novel approach employing features extracted from the acceleration photoplethysmography (APG) waveform, alongside clinical parameters, to estimate different stages of hypertension. The current study used a publicly available dataset and a novel feature extraction algorithm to extract APG waveform features. Three distinct supervised machine learning algorithms were employed in the classification task, namely: Decision Tree (DT), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). Results indicate that the DT model achieved exceptional training accuracy of 100% during cross-validation and maintained a high accuracy of 96.87% on the test dataset. The LDA model demonstrated competitive performance, yielding 85.02% accuracy during cross-validation and 84.37% on the test dataset. Meanwhile, the LSVM model exhibited robust accuracy, achieving 88.77% during cross-validation and 93.75% on the test dataset. These findings underscore the potential of APG analysis as a valuable tool for clinicians in estimating hypertension stages, supporting the need for early detection and intervention. This investigation not only advances hypertension risk assessment but also advocates for enhanced cardiovascular healthcare outcomes.

This study explores the potential of artificial intelligence (AI) in enhancing the diagnosis of brain tumours, specifically through a comparative analysis of two advanced deep learning (DL) models, ResNet50 and YOLOv8, applied to detect and classify brain tumours in MRI images. The study addresses the critical need for rapid and accurate diagnostic tools in the medical field, given the complexity and diversity of brain tumours. The research was motivated by the potential benefits AI could offer to medical diagnostics, particularly in terms of speed and accuracy, which are crucial for effective patient treatment and outcomes. The performance of the ResNet50 and YOLOv8 models was evaluated on a dataset of 7023 MRI images across four tumour types. Key metrics used were accuracy, precision, recall, specificity, F1-score, and processing time, to identify which model performs better in detecting and classifying brain tumours. The findings demonstrates that although both models exhibit high performance, YOLOv8 surpasses ResNet50 in most metrics, particularly showing advantages in speed. The findings highlight the effectiveness advanced DL models in medical image analysis, providing a significant advancement in brain tumour diagnosis. By offering a thorough comparative analysis of two commonly used DL models, aligning with ongoing approaches to integrate AI into practical medical application, and highlighting their potential uses, this study advances the area of medical AI providing insight into the knowledge required for the deployment of future AI diagnostic tools.

The massive increase in 5G usage for video streaming can cause short but intense spikes in data transfer rates, potentially causing network congestion. Congestion detection and prediction are essential for ensuring user satisfaction and enhancing the performance of real-time applications. By leveraging historical network measurement data such as Round Trip Time (RTT), Jitter, and packet loss ratio, machine learning algorithms can analyze traffic patterns, predict congestion based on the attributes, and provide adaptive solutions. We have focused primarily on ML models due to their adaptability to learn from historical patterns, a capability where traditional rule-based methods may fall short. Furthermore, past researchers faced difficulties in obtaining measurement data for fully comprehensive real-world networks. We have collected real-world network measurement datasets over a six-month period for a complete network. Using the datasets and topology information, we trained Support Vector Regression (SVR), Long Short-TermMemory (LSTM), and XGBRegressor (Extreme Gradient Boosting) to forecast the RTT values of each router. To predict network anomalies based on packet loss and high RTT values, Random Forest and Support Vector Classifier (SVC) models have been developed. By analyzing the outputs of these two models, a method is proposed to predict network congestion. While this study is limited by data quantity and the use of simpler ML models, it offers a comprehensive solution and a solid foundation for predicting network congestion symptoms. Network congestion prediction can be further improved by exploring areas such as model ensembling, explainability in deep learning models, transfer learning approaches, and integrating multi-source data.

Detta examensarbete är på grundnivå som har genomförts under en period av 20 veckor undervåren 2023 och motsvarar 15 högskolepoäng. Projektet har haft som syfte att utveckla ett konceptför en kontaktor och tillämpa additiv tillverkningsteknik som en tillverkningsmetod för konceptet.Uppdragsgivaren för projektet har varit ABB Control Products i Västerås och de har identifieratbehovet av en lämplig lösning för limproblemet i släckpaketet för kontaktorer av storlek nio. Dennuvarande användningen av lim i kontaktorerna ansågs vara ohälsosam och uppfyllde intemiljökraven.

Inom ramen för projektet genomfördes en konceptutveckling för att lösa det identifieradeproblemet. Dessutom undersöktes möjligheten att tillämpa additiv tillverkningsteknik ochgenomföra en förenklad kostnadsbedömning för att visa skillnaden i kostnad mellan den nuvarandetillverkningsmetoden och additiv tillverkningsteknik. Produktutvecklingsmetoder användes för attgenomföra projektet, vilket resulterade i ett fungerande teoretiskt koncept. För att sålla ut idéeroch koncept användes en prioriteringsmatris och ett poängsystem. Det resulterande konceptetrepresenteras av CAD-modeller som består av metallplåtar och två delar av det utveckladekonceptet. Konceptet möjliggör en ny design utan användning av lim vid montering. Det har ocksåvisat sig att det är möjligt att ändra tillverkningsmetoden genom att använda additivatillverkningstekniker. Även om prototyperna ännu inte har testats, anses de teoretiska lösningarnavara fungerande.

Tests are important artifacts in the software development process. Testing activities such as test automation, test maintenance, and test suite optimization mainly rely on an in-depth understanding of test specifications. The manual process of writing testspecifications in natural language can create many different quality issues such as ambiguous, incomplete, redundant, or inconsistent test cases. Nowadays, the concept of test smells is proposed by several researchers to be used as indicators of low-qualityattributes in test specifications. Quality assurance processes for test specifications often rely on manual reviews to detect these smells. The manual process of detecting these smells is considered time consuming and costly. However, there is currently no work that implements a comprehensive quality model that classifies and identifies these smells by using a systematic strategy. As a result, there is a need for machine-supported analytical measures that decrease the time and effort needed to detect these smells manually, especially when it comes to reviewing and validating large test specifications.This study aims to investigate which natural language smell metrics implemented in NALABS can be found in test specifications and to measure the sufficiency of those smellmetrics. It also aims to extend these smell metrics by exploring, proposing, or combining with new bad smell metrics to cover more aspects of natural language test quality. The results of the study show that the smell metrics exists in real-world test specifications and can uncover many potential quality issues by assisting test designers in identifying certain types of quality issues pertaining to for example the understandability and complexity of test specifications. Moreover, the results show thatthe list of smell metrics implemented in NALABS is incomplete and can be extended to cover more aspects of test quality.

This thesis first and foremost investigates how entrepreneurial teachers today work with teachingcreativity to young entrepreneurs, at upper secondary level in Sweden. To then come toconclusions on how to improve and developed teaching of creativity in young entrepreneurs. Forthat purpose, this thesis, has the following research questions.Research question 1; How UF (entrepreneurial teachers) use creative tools today?Research question 2; How the learning of creative tools can develop in the future?The method of investigation used in this thesis is a “semi-structured interview” where sixdifferent respondents where interview two at time, separately, the process took a month to becompleted. These six respondents are teachers working in three different high schools in theMälar region of Sweden. The duration of each interview was between 20-30 minutes, theseinterviews were then analyzed, transcribed, and categorized.The answers were then analyzed by the authors and categorized with connection to the theoriesthat have formed the basis of this work. The empirical evidence could then be fed into a tablethat showed the similarities and differences in the working methods between the variousinterviewed respondents. This is to make it easier for the reader.The results of this work show that UF teachers today use creativity as a tool during the businessidea phase of UF entrepreneurship. After this phase, entrepreneurial teachers no longer see anypurpose in teaching creativity to entrepreneurial students. The authors of this thesis concludedthat creativity does not lose its value or utility after the idea generation phase ofentrepreneurship. The current way of teaching creativity creates an environment where creativityis "strangled" and there is no free space left for innovative thinking. The authors associate thislack of innovation to a lack of communication between the UF teachers and therefore the authorscall on the UF teachers to collaborate 3 times per business year.The examiner for this work is Bengt Köping Olsson, researcher in creativity, innovation anddesign at Mälardalen University. 

In order to stimulate innovation and sustainable development in a business environment that is always changing, it is important to support successful collaboration between academia, industry, and society. This project is focused on creating and implementing MDU's Creative Lab, an innovation lab based on the Quintuple Helix Model, in order to promote collaboration between students, academic institutions, and business partners and to meet the needs of all parties. The study was conducted utilizing a methodology that included a review of the literature, interviews, and data analysis. The primary issues that need to be resolved have been highlighted through the interviews as the absence of support systems, the requirement to create clear objectives at the outset of the project, and the significance of ensuring projects and authors maintain continuity.The results of the research have shown that better communication between parties, a clear connection between projects and courses, and opportunities for students to obtain knowledge and experience in the real world are all necessary. In addition, major motivators such as the desire for a social impact, interdisciplinary teamwork, and satisfaction in one's job have been highlighted. As a result, a business model has been developed that takes into account the information gleaned from the presentations and aims to facilitate collaboration and communication between academic institutions, students, and business partners, guarantee the quality of collaborations, offer mentoring and guidance to students, and plan workshops and events that encourage creativity, innovation, and entrepreneurship.This project provides a thorough analysis of how to create and implement a student-driven innovation lab that adheres to the Quintuple Helix Model and is geared toward sustainable growth in the direction of Industry 5.0. By encouraging student cooperation and contentment, it fosters an environment that fosters the development of creative ideas and the development of a team-oriented workforce that will progress society and business.

Today, well-established hazard analysis techniques are available and widely used to identify hazards for single systems in various industries. However, hazard analysis techniques for a System of Systems (SoS) are not properly investigated. SoS is a complex system where multiple systems work together to achieve a common goal. However, the interaction between systems may lead to unforeseen interactions and interdependencies between systems. This increases the difficulty of identifying and assessing system failures and potential safety hazards. In this paper, we explore whether Hazard Ontology (HO) can be applied to an SoS and whether it can identify emergent hazards, their causes, sources, and consequences. To conduct our exploration, we apply the HO to a quarry automation site (an SoS) from the construction equipment domain. The results indicate that the HO is a promising technique that facilitates the identification of emergent hazards and their components. 

While ontology comparison and alignment have been extensively researched in the last decade, there are still some challenges to these disciplines, such as incomplete ontologies, those that cover only a portion of a domain, and differences in domain modeling due to varying viewpoints. Although the literature has compared ontological concepts from the same domain, comparisons of concepts from different domains (e.g., security and safety) remain unexplored. To compare the concepts of security and safety domains, a security ontology must first be created to bridge the gap between these domains. Therefore, this paper presents a Combined Security Ontology (CSO) based on the Unified Foundational Ontology (UFO) that could be compared to or aligned with other ontologies. This CSO includes the core ontological concepts and their respective relationships that had been extracted through a previous systematic literature review. The CSO concepts and their relationships were mapped to the UFO to get a common terminology that facilitates to bridge the gap between the security and safety domains. Since the proposed CSO is based on the UFO, it could be compared to or aligned with other ontologies from different domains.

Safety and security ontologies quickly become essential support for integrating heterogeneous knowledge from various sources. Today, there is little standardization of ontologies and almost no discussion of how to compare concepts and their relationships, establish a general approach to create relationships or model them in general. However, concepts with similar names are not semantically similar or compatible in some cases. In this case, the problem of correspondence arises among the concepts and relationships found in the ontologies. To solve this problem, a comparison between the Hazard Ontology (HO) and the Combined Security Ontology (CSO) is proposed, in which the value of equivalence between their concepts and their relationships was extracted and analyzed. Although the HO covers the concepts related to the safety domain and the CSO includes securityrelated concepts, both are based on the Unified Foundational Ontology (UFO). For this study, HO and CSO were compared, and the results were summarized in the form of comparison tables. Our main contribution involves the comparisons among the concepts in HO and CSO to identify equivalences and differences between the two. Due to the increasing number of ontologies, their mapping, merging, and alignment are primary challenges in bridging the gaps that exist between the safety and security domains. 

Security ontologies have been developed to facilitate the organization and management of security knowledge. A comparison and evaluation of how these ontologies relate to one another is challenging due to their structure, size, complexity, and level of expressiveness. Differences between ontologies can be found on both the ontological and linguistic levels, resulting in errors and inconsistencies (i.e., different concept hierarchies, types of concepts, definitions) when comparing and aligning them. Moreover, many concepts related to security ontologies have not been thoroughly explored and do not fully meet security standards. By using standards, we can ensure that concepts and definitions are unified and coherent. In this study, we address these deficiencies by reviewing existing security ontologies to identify core concepts and relationships. The primary objective of the systematic literature review is to identify core concepts and relationships that are used to describe security issues. We further analyse and map these core concepts and relationships to five security standards (i.e., NIST SP 800-160, NIST SP 800-30 rev.1, NIST SP 800-27 rev.A, ISO/IEC 27001 and NISTIR 8053). As a contribution, this paper provides a set of core concepts and relationships that comply with the standards mentioned above and allow for a new security ontology to be developed.

The increasing complexity of systems and the demand for increasing implemented functionalit-ies within the systems necessitate a paradigm shift in development approaches. While traditionalsystems engineering relies heavily on textual documentation, the future calls for a more stream-lined, digital method such as Model-Based Systems Engineering (MBSE). MBSE utilises models tostore data, mitigating textual ambiguity and providing stakeholders with a unified source of inform-ation. Despite its potential, the adoption of MBSE presents challenges because of the need of newpractitioner skill-sets.

The thesis investigates the feasibility of implementing an easily adoptable MBSE, using a limitedset of SysML elements and diagrams, approach within the space industry. Through the developmentof guidelines, steps, and goals, a model of an industry-related system is constructed. The createdmodel serves as a demonstration to industry engineers, evaluating the suitability of the proposedguidelines.

The thesis concludes the need for maturity of MBSE, its tools, and the need for harmonisationwithin the space industry to establish acceptance among the practitioners. These are the crucialpoints where development and refinements are needed to gain the trust and interest of the end usersof the modelling activities and adopting the proposed methods.i   

Den här studien ger en djupgående utforskning av hur olika offentliga organisationer engagerar sig i innovationsledning, och beskriver de specifika utmaningar de står inför och de strategier de använder för att navigera i dessa frågor. 

Forskningen lägger stor vikt vid de systematiska aspekterna av innovationsledning inom den offentliga sektorn och undersöker kritiska faktorer som resursallokering, kompetensutveckling och kompetens att hantera innovation. Studien syftar till att belysa de mångfacetterade utmaningar som möter i processen för systematisk innovationsledning och erbjuder en övergripande titt på hur dessa utmaningar hanteras och mildras. Dessutom fördjupar den i olika verktyg och metoder som offentliga organisationer använder för att ta itu med och övervinna dessa hinder. Genom att erbjuda insikter i den praktiska hanteringen av utmaningar inom innovationsledning, vill denna studie bidra till en djupare förståelse för effektiva metoder och lösningar som kan förbättra innovation inom offentliga organisationer.

In enterprise-scale companies, where processing complex test results is often a time-consuming task for testers and developers, the need for an Application Programming Interface (API) testing visualization tool is vital. Furthermore, as various stakeholders require insights into the API testing results, a clear dashboard can facilitate effective communication among them. 

This thesis aims to fulfill the need for a generic API testing visualization tool within the mobile network industry through the implementation of TestUp, a user-friendly solution. TestUp simplifies the analysis of complex API test results for various stakeholders, including testers, developers, managers, and product owners. To accomplish the objectives of this thesis, an industrial case study was conducted at Nokia, beginning with a pre-study phase to explore industry needs and challenges. TestUp was designed based on the Agile methodology, evolving through feedback from industry and academia. Additionally, a post-tool questionnaire was employed to gather data on the tool’s effectiveness and limitations. Subsequently, the data analysis was conducted using the thematic analysis approach. 

The thesis demonstrates that TestUp provides valuable benefits to stakeholders by effectively addressing critical issues found in existing tools. TestUp is compatible with a range of test automation frameworks and serves as a user-friendly tool for visualizing test coverage. These facts fulfill the company's requirements. All in all, this thesis improves the analysing of API testing results within the mobile network management sector. Additionally, future work can involve enhancing the tool by adding extra features, such as export functionality and the ability to indicate timeout requests. Also, integrating TestUp with the continuous integration pipelines is a potential area for future work.

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. 

With the development towards Industry 5.0, manufacturing companies are developing towards Smart Production, i.e., using data as a resource to interconnect the elements in the production system to learn and adapt accordingly for a more resource-efficient and sustainable production. This requires selecting and integrating digital technologies for the entire data lifecycle, also referred to as the data value chain. However, manufacturing companies are facing many challenges related to building data value chains to achieve the desired benefits of Smart Production. Therefore, the purpose of this paper is to identify and analyze the critical factors of selecting and integrating digital technologies for efficiently benefiting data value chains for Smart Production. This paper employed a qualitative-based multiple case study design involving manufacturing companies within different industries and of different sizes. The paper also analyses two Smart Production cases in detail by mapping the data flow using a technology selection and integration framework to propose solutions to the existing challenges. By analyzing the two in-depth studies and additionally two reference cases, 13 themes of critical factors for selecting and integrating digital technologies were identified.

To stay competitive, manufacturing companies are developing towards Smart Production which requires the use of digital technologies. However, there is a lack of guidance supporting manufacturing companies in selecting and integrating a combination of suitable digital technologies, which is required for Smart Production. To address this gap, the purpose of this paper is twofold: (i) to identify the main challenges of selecting and integrating digital technologies for Smart Production, and (ii) to propose a holistic concept to support manufacturing companies in mitigating identified challenges in order to select and integrate a combination of digital technologies for Smart Production. This is accomplished by using a qualitative-based multiple case study design. This paper identifies current challenges related to selection and integration of digital technologies. To overcome these challenges and achieve Smart production, the concept of data value chain was proposed, i.e., a holistic approach to systematically map and improve data flows within the production system. © 2022, IFIP International Federation for Information Processing.

In this thesis work, our main goal is to improve IoT communications of an edge platform by selecting suitable solutions to work with the MQTT protocol. Our primary focus in this thesis is to investigate two quality attributes: data throughput and latency of message brokers.There are two research questions that are to be answered: how do protocols like MQTT and their architecture contribute to messaging in IoT communications? andHow to Enhance MQTT Architecture to Match the Requirements for ABB IoT Edge Platform?.  This thesis work comprises a mixed-methods research methodology that includes both quantitative and qualitative phases. The quantitative phase involves experimental work on examining data throughput and latency of a message broker, while the qualitative phase includes interviews with stakeholders and literature reviews. According to the qualitative and quantitative data, we found that event-driven architecture, publisher-subscriber design pattern, and NATS message broker are suitable choices to work with the MQTT-based architecture of IoT edge platforms. Since NATS showed a decent result in terms of high throughput and low latency in comparison to other message brokers. The results of this thesis work identify the best practices of message brokers in an MQTT-based IoT platform, for software architects, specifically the ABB IoT edge platform.

Utvecklingsmetoder är olika arbetsprocesser som företag kan använda för att förbättra och effektivisera sitt arbete. Idag är de agila utvecklingsmetoderna Scrum och Kanban två av de mer använda metoderna inom företag som utvecklar mjukvara. I mitt arbete undersöks det hur nära små utvecklingsteam inom mindre företag förhåller sig till Scrum eller Kanban i verkligheten. Undersökningen görs för att se om det finns förbättringar som mindre företag med små utvecklingsteam kan ta till sig från litteratur för att efterlikna hur teorin är beskriven, samt kunna diskutera kring delar relevans som inte efterlevs.

Syftet med detta arbete är att identifiera hur nära agila utvecklingsmetoder som Scrum eller Kanban efterlevs i praktiken, hos mindre företag med små utvecklingsteam, enligt hur metoderna är teoretiskt beskrivna. Målet i detta arbete är se om det finns förbättringar som små utvecklingsteam kan ta till sig från litteratur om utvecklingsmetoderna, Scrum eller Kanban, för att effektivisera sin arbetsprocess genom att efterleva mer enligt hur dessa utvecklingsmetoder beskrivs i litteraturen.

För att uppnå arbetets syfte och mål undersökas tre frågeställningar. Ena frågeställningen är om hur litteratur beskriver metoderna för agil mjukvaruutveckling för små utvecklingsteam i mindre företag, vilket besvarades genom en litteraturstudie. Genom intervjuer besvarades frågeställningen om hur utvecklare i små utvecklingsteam inom mindre företag beskriver sin utvecklingsmetod. Tillsammans med intervjuerna och en observationsstudie besvarades den sista frågeställningen angående hur små utvecklingsteam i mindre företag arbetar med utveckling av mjukvara i praktiken.

Resultatet visade att utvecklarna från det medverkande företaget i praktiken endast följde vissa delar från Scrum men inte alla centrala delar, trots att Scrum enligt litteratur är anpassad till att användas inom små utvecklingsteam. Genom att inte följa Scrum centrala delar går det inte att säga att de använder Scrum, och därmed riskerar att inte leva upp till alla de fördelar Scrum kan bidra med.

Purpose: This research will study how hyperspectral sensoring can assess the moisture content of lettuce by monitoring its growth in indoor vertical farming.

Research questions:

“What accuracy can be achieved when using hyperspectral sensoring for assessing the moisture content of lettuce leaves grown in vertical farming?”

“How can vertical farming contribute to sustainability in conjunction with integration of NIR spectroscopy?”

Methodology: This study is an experimental study with a deductive approach in which experiments have been performed using the hyperspectral technologies singlespot sensor and the hyperspectral camera Specim FX17 to collect spectral data. To analyze the data from the experiments two regression models were used and trained to make it possible to predict future moisture content values in lettuce. In order to get a better understanding and analyze the results from the experiments, a literature review was also conducted on how hyperspectral imaging has been applied to assess the quality of food products.

Conclusion: The achieved accuracies were 58.24 % and 65.54 % for the PLS regression model and the Neural Network model respectively. Employing hyperspectral sensoring as a non-destructive technique to assess the quality of food products grown and harvested in vertical farming systems, contributes to sustainability from several aspects such as reducing food waste, minimizing costs and detecting different quality attributes that affect the food products.

Wireless sensors and networks are used only occasionally in current control loops in the process industry. With rapid developments in embedded and highperformance computing, wireless communication, and cloud technology, drastic changes in the architecture and operation of industrial automation systems seem more likely than ever. These changes are driven by ever-growing demands on production quality and flexibility. However, as discussed in "Summary," there are several research obstacles to overcome. The radio communication environment in the process industry is often troublesome, as the environment is frequently cluttered with large metal objects, moving machines and vehicles, and processes emitting radio disturbances [1], [2]. The successful deployment of a wireless control system in such an environment requires careful design of communication links and network protocols as well as robust and reconfigurable control algorithms.

In the literature there is no consensus regarding the meaning of the term digital transformation. Therefore, the purpose of this paper is to explore the differing views of the meaning of digital transformation. A case study has been conducted in collaboration with four Swedish manufacturing companies. The results shows that digital transformation can have different meaning within a company and the main challenge when performing digital transformation is knowledge. This study is the first investigation in a research project focusing on coordination of digital transformation. Therefore, is the underlaying goal to identify how the participating companies in the research project describe digital transformation in comparison with the literature. The research intention is not to define digital transformation rather to explore differing views of digital transformation and highlighting similarities and difference in comparison with the literature reviewed. The findings are practically relevant for manufacturing companies by highlighting differing views of digital transformation and in the creation of a common language within a company.