Creating value, not wasting resources: sustainable innovation strategies

It is well-accepted in academic and public debate that society has overused natural resources. Business managers in consequence face a normative framework where products need to become more ‘sustainable’. The paper characterises the mechanisms and logic that make ‘[environmentally] sustainable innovation strategies’. Those mechanisms highlight multiple value creation and sustaining value beyond the original new product lifecycle. They yield as much utility as possible from the embedded natural resources. And they avoid creating waste. ‘Multiple value creation’ asks managers to revaluate the attrite product or to make customers change their use patterns. The paper then demonstrates how to extend the ‘old’ logic of innovation with a phase of revaluation: a phase promoting further use of the product and/or material. Our concept is empirically illustrated by two industry case examples. Namely, the copier industry and the emerging automotive lithium-ion batteries industry. We provide a patent analysis in order to demonstrate the assessment of extended life cycles, for the case of ‘recovery of raw materials from disposed products’.

Probabilistic Graphical Modelling for Software Product Lines: A Frameweork for Modeling and Reasoning under Uncertainty

This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance.