Wrap-up 2023 - Unidemi

30.12.2023

The Department of Mechanical and Industrial Engineering and its research unit, UNIDEMI, are pleased to announce that in 2023, that the following students have completed their Doctoral Programs:

Belma Rizvanovic, Doctorate in Industrial Engineering, with the thesis: “Optimizing Product-Market Fit: A Digital Marketing Approach for Performance Measurement”

Abstract: In today’s digital economy, digital marketing has grown in scope and now acts as a liaison connecting digital interactions and key start-up activities. The low investment and dynamic elements of digital marketing favor the start-up environment and enable a flexible match between digital interactions and start-up growth. However, start-ups are usually unfamiliar with all the possibilities digital marketing offers and connect its influence solely with marketing and sales. Digital marketing, powered by data analytics, can support start-ups in different key activities such as testing and experimentation, customer education, and others. This thesis first explores how digital marketing impacts start-up development within a set of growth drivers supporting different key activities and associated areas. Through a systematic literature review, the Macro- dynamic Framework was developed to identify and connect fifteen growth drivers and digital marketing impact for achieving start-up growth in four different areas. Secondly, as Product-Market fit is identified as one of the key drivers of start-up growth, this research focused on investigating how can product-market fit be influenced through digital marketing. One of the major challenges start-ups have in this perspective is measuring digital marketing performance and interpreting the collected data. As Product-Market fit and performance measurement both largely rely on the provided digital marketing analytics and received feedback, the DM_Optima framework is developed to connect the touchpoints of both areas. Through multiple case study research with nine start-ups, the thesis demonstrates this Framework application. The DM_Optima Framework is developed with a Balanced Scorecard and enables start-ups to test their ability to optimize product-market fit through digital marketing and performance measurement. The results of DM_Optima help start-up founders and managers to achieve or maintain their product-market fit and establish efficient practices of digital marketing performance measurement from an internal organizational perspective.

Vítor Manuel Caetano Alcácer, Doctorate in Industrial Engineering, with the thesis: “Driving Manufacturing Systems for the Fourth Industrial Revolution”

Abstract: It has been a long way since the aroused of the Industry 4.0 and the companies’ reality is not already align with this new concept. Industry 4.0 is ongoing slowly as it was expected that its maturity level should be higher. The companies´ managers should have a different approach to the adoption of the industry 4.0 enabling technologies on their manufacturing systems to create smart nets along all production process with the connection of elements on the manu-facturing system such as machines, employees, and systems. These smart nets can control and make autonomous decisions efficiently. Moreover, in the industry 4.0 environment, companies can predict problems and failures along all production process and react sooner regarding maintenance or production changes for instance. The industry 4.0 environment is a challenging area because changes the relation between humans and machines. In this way, the scope of this thesis is to contribute to companies adopting the industry 4.0 enabling technologies in their manufacturing systems to improve their competitiveness to face the incoming future. For this purpose, this thesis integrates a research line oriented to i) the understanding of the industry 4.0 concepts, and its enabling technologies to perform the vision of the smart factory, ii) the analysis of the industry 4.0 maturity level on a regional industrial sector and to understand how companies are facing the digital transformation challenges and its barriers, iii) to analyze in deep the industry 4.0 adoption in a company and understand how this company can reach higher maturity levels, and iv) the development of strategic scenarios to help companies on the digital transition, proposing risk mitigations plans and a methodology to develop stra-tegic scenarios. This thesis highlights several barriers to industry 4.0 adoption and also brings new ones to academic and practitioner discussion. The companies’ perception related to these barriers Is also discussed in this thesis. The findings of this thesis are of significant interest to companies and managers as they can position themselves along this research line and take advantage of it using all phases of this thesis to perform a better knowledge of this industrial revolution, how to perform better industry 4.0 maturity levels and they can position themselves in the proposed strategic scenarios to take the necessary actions to better face this industrial revolution. In this way, it is proposed this research line for companies to accelerate their digital transformation.

Carlos Alberto do Rosário Silva Fortes, Doctorate in Mechanical Engineering, with the thesis: “Integrated Model for Improving Resilience in Meeting Delivery Deadlines in Engineering-To-Order Production Systems”

Abstract: In the competitive environment where companies operate, customers increasingly seek personalised products. Companies need to effectively meet these customer requirements in terms of functionality, timelines, and costs. There is significant competitive pressure to improve efficiency and effectiveness in industrial companies, particularly those operating with Engineer-To-Order (ETO) production systems. This pressure largely stems from the need to respond quickly and innovatively to the demands of an increasingly dynamic and demanding market. Based on this reality, the research aimed to improve ETO companies’ performance and increase the resilience of workflows, characterised by high complexity and process variability. As an initial approach to this research, current areas of concern and operational challenges of ETO companies were identified. For this, a Systematic Literature Review (SLR) was conducted to identify ETO companies’ general challenges and how they improve their ability to meet customer requirements and delivery deadlines. In the context of this work, it is crucial to use resources effectively and create efficient systems to manage complexity and variability in ETO companies. This aids managers in making better decisions at all stages of work. After analysing the criticality of the main problems detected and characterising their current solutions and weaknesses, a solution proposal was developed using Axiomatic Design Theory. This method was chosen for its ability to provide a structured framework that facilitates the approach to complex and variable issues. This structured framework makes decision-making more effective, especially in uncertain environments and multiple requirements. The aim is to strengthen the ability of ETO companies to handle uncertainties while promoting a higher level of performance. The developed solution is a model that improves operational excellence in ETO companies, aiming to increase resilience in meeting delivery deadlines. For this reason, it was named the E.OpEx Model. The E.OpEx Model is thus referred to as a roadmap for ETO company managers to make decisions to improve their operations excellence. This is because the E.OpEx Model proposes an innovative contribution: the integrated use of the CCPM – Critical Chain Project Management methodology with the POLCA – Paired-cell Overlapping Loops of Cards with Authorization technique, combined with IoT – Internet of Things technologies. The E.OpEx Model, with its integrated approach of using the CCPM methodology, the POLCA technique, and emerging technologies, proved effective in addressing typical problems ETO companies face. Its relevance was validated in a Focus Group session. During this session, it was also confirmed that implementing the E.OpEx Model will enhance the operational efficiency and resilience of ETO.