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’.

A Scalable Design Framework for Variability Management in Large-Scale Software Product Lines

Variability management is one of the major challenges in software product line adoption, since it needs to be efficiently managed at various levels of the software product line development process (e.g., requirement analysis, design, implementation, etc.). One of the main challenges within variability management is the handling and effective visualization of large-scale (industry-size) models, which in many projects, can reach the order of thousands, along with the dependency relationships that exist among them. These have raised many concerns regarding the scalability of current variability management tools and techniques and their lack of industrial adoption. To address the scalability issues, this work employed a combination of quantitative and qualitative research methods to identify the reasons behind the limited scalability of existing variability management tools and techniques. In addition to producing a comprehensive catalogue of existing tools, the outcome form this stage helped understand the major limitations of existing tools. Based on the findings, a novel approach was created for managing variability that employed two main principles for supporting scalability. First, the separation-of-concerns principle was employed by creating multiple views of variability models to alleviate information overload. Second, hyperbolic trees were used to visualise models (compared to Euclidian space trees traditionally used). The result was an approach that can represent models encompassing hundreds of variability points and complex relationships. These concepts were demonstrated by implementing them in an existing variability management tool and using it to model a real-life product line with over a thousand variability points. Finally, in order to assess the work, an evaluation framework was designed based on various established usability assessment best practices and standards. The framework was then used with several case studies to benchmark the performance of this work against other existing tools.