The world that organizations today are forced to navigate is volatile, uncertain, complex and ambiguous - a VUCA world. Their long-term survival is continuously endangered. A strong brand positioning is important to secure survival and deeply connect with customers. However, changes in consumer needs and expectations call for new ways to stay relevant and stay ahead of these changes. At the same time, it is important to look ahead and make informed predictions regarding the direction society and customers are moving. Thus, future studies - more specifically strategic foresight - has gained growing attention within both academia and industry. With the amount of data collected today, novel ways of analysing and predicting customer behaviour and trends are emerging. Artificial intelligence (AI) has recently gotten a new breakthrough due to its ability to gather, monitor and analyse these vast amounts of data. The intersection between the three - branding, strategic foresight and AI - holds great potential to create and maintain a strong brand positioning. Therefore, this thesis aims to investigate this main question; how can artificial intelligence and strategic foresight be applied to the improvement and/or maintenance a future-proof brand positioning? This project is done in collaboration with Deloitte Digital who consults a number of well-established brands. Thus, this project is relevant for Deloitte Digital as it can future-proof the brands of their clients. Through a literature review and eight expert interviews regarding the three areas and the relations between them it is discovered that there is potential in further focusing more specifically on (1) the emotional connection between brand and user, (2) the Generic Foresight Process and (3) natural language processing (NLP). These insights are translated into design requirements and a design vision which then guide the creation of a solution. The solution proposed is called MindMatch and falls into the category of social listening tools. The digital platform contains three main USPs; (1) an analysis of the match between brand identity and brand image, (2) sentiment analysis and emotion recognition and (3) digital co-creation connected to the analysis insights. An interactive prototype of both a marketing website and the digital platform was developed and iterated upon based on feedback from Deloitte colleagues. When finalized, the solution was validated through interviews with three Deloitte colleagues and one crown jewel client, DSM. MindMatch embodies the answer to the main question of this thesis. By applying NLP techniques (including topic modeling, sentiment analysis and emotion recognition) and the Generic Foresight Process (including the six steps of the process, weak signal detection and forecasting) to social media data, the solution assists Deloitte Digital in improving and/or maintaining the emotional connection and co-created meaning between brand and user - resulting in a future-proof brand positioning.

Reukema is the biggest non-ferrous trader in the Netherlands. They want to investigate the possibility of sorting aluminium in a robotic system. Aluminium is one of the most used materials worldwide. Demands for aluminium are ever increasing. Recycling scrap is needed to keep up with the demand. An important step in the recycling process is the separation into different alloys, as sorting aluminium creates added value. Currently, the only way to sort the scrap is to have it done manually in low-wage countries such as China, Pakistan or India. This thesis describes the analysis of the problems which may occur implementing such a system. Three idea directions were generated based on the analysis executed. It was found that using a robotic arm with a robotic gripper would unwantedly increase the complexity of the sorting system. Simply pushing the scrap off a conveyor belt was found to be the best design. Based on this finding three different concepts were created, of which one, the concept in which material is fed into the system in a line, was selected. A pusher, perpendicular to the conveyor belt, pushes the material off the belt. Material is classified using a camera and a line scanner. The scrap is stored in a bunker under the sorting installation. In the last phase of this project the gripper was detailed. It was important to maximize the quality of sorting. Besides this, the reliability of the complete system needed to be maximized, while the cost per tonne should be minimized. The final design is a gripper which gives the pieces of scrap a parabolic trajectory before they land in the bunker for storing. The gripper is constructed out of steel and is 250 by 125 [mm]. A rib of 100 [mm] was added to lift pieces of scrap off the conveyor belt and decrease friction.

This thesis presents an approach for 1) analyzing Human Machine Interfaces (HMI) for sub-sea robotic Remotely Operated Vehicles (ROV) 2) structurally redesigning its interface modules and 3) evaluating the resulting designs. The thesis exemplifies this process by redesigning the interface modules that pertain to the diagnosing of hardware failures of the propulsion systems of rock dumping ROVs. The redesign focused on providing the user with more situational awareness.