Industry 4.0 Technologies and Customer-centricity for Digital Supply Chains

The last few years have experienced the emergence of Industry 4.0 (I4.0), ultra-customization, and the explosion of demand for ethical, fair trade, and sustainable consumption. Organizations have therefore started a digital transformation of their SCs and of their production. They have placed consumers at the center of their strategic agenda. In the context of ultra-customization, mass customization (MC) is gaining momentum, especially with the arrival of digital technologies like additive manufacturing (AM), also known as 3D-printing). Due to technological advances, AM is now very popular for the final part production in series, and on a large scale. SC performance is, among others, driven by operational excellence, information sharing, and trust between the different SC stakeholders. Looking at the second trend, which is focused on new consumption patterns, organizations are now encouraged to evaluate the potential of adopting new digital I4.0 technologies. The combination of Blockchain (BC) with the Internet-of-Things (IoT) seems promising to improve SC performance and meet customer demand. Despite the recognized potential of I4.0 technologies, and the transition toward digital SCs, organizations are struggling to adapt to the two trends. This is mainly due to the lack of decision support tools for the adoption of these new technologies and to the lack of user-centric approaches. In this thesis, we develop user-centric approaches from which we model and analyze the impact of three I4.0 digital technologies on the SC. First, we develop a new demand model, the HLB model, taking into account the individual demand of heterogeneous customers. This model is the first, to our knowledge, to model both the heterogeneity of customers and the evolution of their purchasing behavior over time. It couples the Bass diffusion and the Hotelling-Lancaster models. Then, building on the HLB model, we analyze, across the PLC, marketing and operations decisions that result from technology-switching scenarios. We formulate and solve an optimization problem by jointly deciding on: technology-switching times, inventory, production quantity, pricing, and product variety strategies. The goal is to maximize a manufacturer's profit while addressing the individual and evolving needs of customers. We use a sample average approximation for the numerical solutions of our non-convex optimization problem. Based on an adaptive inventory policy, we derive a closed-form solution for the pro