Software project economics: A roadmap

The objective of this paper is to consider research progress in the field of software project economics with a view to identifying important challenges and promising research directions. I argue that this is an important sub-discipline since this will underpin any cost-benefit analysis used to justify the resourcing, or otherwise, of a software project. To accomplish this I conducted a bibliometric analysis of peer reviewed research articles to identify major areas of activity. My results indicate that the primary goal of more accurate cost prediction systems remains largely unachieved. However, there are a number of new and promising avenues of research including: how we can combine results from primary studies, integration of multiple predictions and applying greater emphasis upon the human aspects of prediction tasks. I conclude that the field is likely to remain very challenging due to the people-centric nature of software engineering, since it is in essence a design task. Nevertheless the need for good economic models will grow rather than diminish as software becomes increasingly ubiquitous

Building a boundaryless manufacturing organisation through HITOP method

There is little empirical research to support the allegation that ‘leagile’ manufacturing organisations thrive in hostile environments, nor has it been demonstrated that organisation processes (referred to as enablers) actually support ‘leagile’ performance. This study tests the statistical significance of five selected HITOP (highly integrated technology, organisation and people) ‘leagile’ enablers. This was accomplished by using a mail survey instrument to measure the presence of ‘leagile enablers’ in a sample of companies taken from best factory award winners in UK, US and Japan. [Continues.

Innovation landscape and challenges of smart technologies and systems - a European perspective

Latest developments in smart sensor and actuator technologies are expected to lead to a revolution in future manufacturing systems’ abilities and efficiency, often referred to as Industry 4.0. Smart technologies with higher degrees of autonomy will be essential to achieve the next breakthrough in both agility and productivity. However, the technologies will also bring substantial design and integration challenges and novelty risks to manufacturing businesses. The aim of this paper is to analyse the current landscape and to identify the challenges for introducing smart technologies into manufacturing systems in Europe. Expert knowledge from both industrial and academic practitioners in the field was extracted using an online survey. Feedback from a workshop was used to triangulate and extend the survey results. The findings indicate three main challenges for the ubiquitous implementation of smart technologies in manufacturing are: i) the perceived risk of novel technologies, ii) the complexity of integration, and iii) the consideration of human factors. Recommendations are made based on these findings to transform the landscape for smart manufacturing

Managing digital trasformation in the context of SMEs: The relevance of collaboration.

Industry 4.0 technologies are shaping the drivers of the competitive advantage. Firms, especially SMEs, are adopting these technologies facing barriers and difficulties toward the digitalization process. A strategy to overcome these barriers is to leverage on the collaboration with firm’s network of actors, in which a key role is represented by KIBS and technology providers. This thesis is focused on the impacts of Industry 4.0 for SMEs, the relevancy of collaboration and key role of technology providers and KIBS, analyzing the geographical dimension of these relationships, from both theoretical and empirical perspectives.Industry 4.0 technologies are shaping the drivers of the competitive advantage. Firms, especially SMEs, are adopting these technologies facing barriers and difficulties toward the digitalization process. A strategy to overcome these barriers is to leverage on the collaboration with firm’s network of actors, in which a key role is represented by KIBS and technology providers. This thesis is focused on the impacts of Industry 4.0 for SMEs, the relevancy of collaboration and key role of technology providers and KIBS, analyzing the geographical dimension of these relationships, from both theoretical and empirical perspectives

Deriving Goal-oriented Performance Models by Systematic Experimentation

Performance modelling can require substantial effort when creating and maintaining performance models for software systems that are based on existing software. Therefore, this thesis addresses the challenge of performance prediction in such scenarios. It proposes a novel goal-oriented method for experimental, measurement-based performance modelling. We validated the approach in a number of case studies including standard industry benchmarks as well as a real development scenario at SAP

Water Quality Modelling of Buffalo Pound Lake

The highly variable climate of the Canadian Prairies causes high economic losses from floods and droughts. Prairie waterbodies can be ice covered over half of the year impacting water transfer capacities and influencing aquatic processes and water quality. Available winter studies show ice cover has a wide ranging influence on physical, chemical and biological processes. Water quality models are an emerging tool in the Prairies for understanding complex ecosystem responses. Water quality modelling has traditionally been focussed on open water periods with under-ice processes largely ignored during calibration and model simulations. Management plans based on model results applicable to just four or five months of the year will overlook water quality issues under ice that can be informed by modelling. This thesis presents the first application of a complex hydrological-ecological model CE-QUAL-W2 to Buffalo Pound Lake an impounded, cold polymictic, natural lake supplying the water needs of approximately 25% of the Saskatchewan population. Three research themes investigate if 1) water quality is driven more by the lake’s catchment area or by internal lake processes, 2) how future flow management and climate change will affect the water quality of the lake, and 3) how under-ice processes can be successfully represented in the CE-QUAL-W2 model. Five water quality variables are simulated: Chlorophyll-a, ammonia (ammonium, NH4+-N), nitrate (NO3-N), dissolved oxygen, and phosphate (PO4-P). This thesis is written in manuscript format. The first manuscript improves the predictive capabilities of the zero-order sediment compartment and adapts the model code to read a variable sediment oxygen demand rate in place of the existing fixed coefficient. A semi-automated calibration method finds an annual pattern between chlorophyll-a, summer oxygen demand and rate of winter decay. The second manuscript looks to improve the under-ice heat and light environment in the model by modifying the ice algorithm to incorporate a variable albedo rate. Simulated ice-off dates are found to be highly sensitive to the ending albedo value. Improvements to water quality predictions are limited by the connection of the ice and eutrophication modules in CE-QUAL-W2. A targeted monitoring program is suggested to reduce uncertainty with boundary data. The third manuscript tests the sensitivity of the model to catchment and in-lake boundary conditions. All five water quality variables are found to be most sensitive to modelled inflow discharge. The final chapter summarises the findings of the three manuscripts and presents a scenario based flow management analysis for discussion. This research finds the Buffalo Pound Lake model is most sensitive to catchment boundary data. Water quality in the lake may be impacted by changing inflows resulting from lake management decisions and climate change

Plugin practice: recasting modularity for architects

Contemporary digital design practice is reframing a creative dialogue between design and making. Empowered by an increasingly seamless interface between data and material, the domain of the architect is expanding to engage diverse processes across design and fabrication. New practices of prototyping are emerging in which architects creatively extend opportunities for custom production, exploring relationships of form, material, fabrication, and aspects of performance. This research is driven by project work spanning such a broad domain across design and fabrication, through which I have developed a series of prototypes. In these projects I have created, used and appropriated numerous tools and techniques. In this dissertation, I focus on the ways in which I engage with such a diverse toolset, addressing the workflows of projects in order to frame a modularity of process. This modularity operates across multiple scales, from simple functions to more complex systems, and to varying degrees, from discrete elements to fuzzier arrangements. It is not derived from formulas for design but is instead grounded in expertise and experience. It emerges in response to specific demands for resilience and flexibility and frames a practice in which we plug together diverse processes to enable design and prototyping for architecture. The first contribution of this doctorate is to demonstrate a modularity of process and highlighting its role at multiple scales through a set of diagrams. Furthermore, I frame a series of implications of this modularity of process for architecture practice. Modularity is here more than just a means of organisation across design and fabrication. Nor is it employed to improve efficiency, as it is in some areas. Rather this modularity of process is important to enabling the generation and control differentiation, collaboration across fields of knowledge, and exploration of interdependent design criteria. These underpin a plugin practice in which designers can interrogate the ways we calibrate process and outcome, and create and reuse diverse forms of knowledg