A Hierarchical, Fuzzy Inference Approach to Data Filtration and Feature Prioritization in the Connected Manufacturing Enterprise

The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency or interpretability by the human data scientist or decision maker. This dissertation, contextualized in the connected manufacturing enterprise, presents an original Fuzzy Approach to Feature Reduction and Prioritization (FAFRAP) approach that is designed to assist the data scientist in filtering and prioritizing data for inclusion in supervised machine learning models. A set of sequential filters reduces the initial set of independent variables, and a fuzzy inference system outputs a crisp numeric value associated with each feature to rank order and prioritize for inclusion in model training. Additionally, the fuzzy inference system outputs a descriptive label to assist in the interpretation of the feature’s usefulness with respect to the problem of interest. Model testing is performed using three publicly available datasets from an online machine learning data repository and later applied to a case study in electronic assembly manufacture. Consistency of model results is experimentally verified using Fisher’s Exact Test, and results of filtered models are compared to results obtained by the unfiltered sets of features using a proposed novel metric of performance-size ratio (PSR)

A behavioural view of the decision for capability investments: the solar PV industry in Taiwan

This research examines the role of framing in the process of decision-making for new capability investments under conditions of policy and technological uncertainty. I argue that framing can explain the decision to exploit current capabilities, but is not sufficient to explain the decision to explore new capabilities. This research discriminates between “frames” and “framing” in the investigation: whereas “framing” is the process of constructing the meaning of the decision problem, “frame” refers to a specific perspective adopted by the decision makers. I develop a three-level research design: the industry-level analysis adopts the approach of eliciting heuristics to identify general patterns. The firm-level examines sources of variation and causal complexity by comparative case analysis. The decision-maker level investigates the influence of senior managers’ professional experience using a scenario evaluation approach. Three observations from the case study of Taiwanese solar PV firms: firstly, systematic patterns are found in the process of framing environmental uncertainty and attributing the causes of the decision problem of capability investments. Secondly, whilst differentiated framing exists and corresponds to selective attention; such a difference is not necessarily associated with different choice pattern. Finally, the loosely coupling framing and choices leads to the speculation that the role of deliberate practice, rather than framing has a stronger influence on the decision to explore. This research illustrates that the capabilities investment decision is not a single event but a complex process. While the stylised psychological principles explain the heuristic judgments, the influencing factors of an organisational decision are interdependent and temporally connected in the decision context. I argue that the problem of framing lies in prohibiting the alterative frame. Therefore exploration needs to be deliberately sought by the specially designed practice. This research contributes to understanding the relationship between behavioural view of descriptive analysis and prescriptive view of procedural rationality in the decision- making process

Knowledge discovery for moderating collaborative projects

In today's global market environment, enterprises are increasingly turning towards collaboration in projects to leverage their resources, skills and expertise, and simultaneously address the challenges posed in diverse and competitive markets. Moderators, which are knowledge based systems have successfully been used to support collaborative teams by raising awareness of problems or conflicts. However, the functioning of a moderator is limited to the knowledge it has about the team members. Knowledge acquisition, learning and updating of knowledge are the major challenges for a Moderator's implementation. To address these challenges a Knowledge discOvery And daTa minINg inteGrated (KOATING) framework is presented for Moderators to enable them to continuously learn from the operational databases of the company and semi-automatically update the corresponding expert module. The architecture for the Universal Knowledge Moderator (UKM) shows how the existing moderators can be extended to support global manufacturing. A method for designing and developing the knowledge acquisition module of the Moderator for manual and semi-automatic update of knowledge is documented using the Unified Modelling Language (UML). UML has been used to explore the static structure and dynamic behaviour, and describe the system analysis, system design and system development aspects of the proposed KOATING framework. The proof of design has been presented using a case study for a collaborative project in the form of construction project supply chain. It has been shown that Moderators can "learn" by extracting various kinds of knowledge from Post Project Reports (PPRs) using different types of text mining techniques. Furthermore, it also proposed that the knowledge discovery integrated moderators can be used to support and enhance collaboration by identifying appropriate business opportunities and identifying corresponding partners for creation of a virtual organization. A case study is presented in the context of a UK based SME. Finally, this thesis concludes by summarizing the thesis, outlining its novelties and contributions, and recommending future research

Big data, better solar : statistical modelling and optimisation of photovoltaic manufacturing data

Photovoltaic cells and modules can now be manufactured at very low prices, and this has largely been realised through improvements to the manufacturing system over many years. The rapid growth rate of the manufacturing base is such that aspects of the manufacturing execution have not kept pace with common practice in other comparable industries. The specific point of interest in the case of this thesis is the way data is used in the decision making and control of the manufacturing system. A modern photovoltaic manufacturing facility can produce millions of cells a day, but most of the time, most of the data associated with this is not used for any meaningful or value-adding purpose. To address this opportunity, this thesis establishes PV manufacturing analytics as an important and impactful research area in its own right and makes several founding contributions to the field. The biggest opportunities for better use of data are in improving quality and the understanding of variance in production. Multivariate analysis techniques are introduced and tailored to analysing the detailed sets of data collected by some manufacturers throughout the manufacturing sequence. Novel data-mining based techniques are also developed to partition sources of variance in commonly collected end-of-line data sets. New approaches are developed for the sorting of cells into modules as well as metrics to characterise and quantify module quality at the cell-to-module interface. With these analytical tools and techniques available for analysing production data, this thesis offers a picture of what photovoltaic manufacturing will look like. Manufacturers will have real metrics with which to measure and control product quality across different lines and different facilities. They will have the information with which to identify the quality issues as they arise. Manufacturers will be able to demonstrate the quality of their production to their customers and be able to differentiate their businesses on this basis, as happens in other modern manufacturing industries. The industry will have a product focus and it will be able to supply homogeneous products with high yield and a lower cost. As photovoltaic manufacturing approaches the eventual volume it needs to be able to provide for high levels of future renewable energy generation, the work in this thesis present a strong picture of how photovoltaic manufacturing will need to be executed to becomes a fully matured industry

Revisiting cooperation dynamics: implications for opportunism and value creation when firms compete and cooperate simultaneously.

Referring to simultaneous competition and cooperation between firms, coopetition is emerging in practice as a promising source of value creation. However, the scholarly literature is dominated by a widespread assumption that opportunism, a core behavioral assumption of transaction cost economics, hinders value creation and requires formal safeguards in coopetition. The assumption of heightened opportunism in coopetition is at odds with rising adoption in practice, which often proceeds without formal safeguards. This raises concerns about the utility of existing theory for explaining coopetition dynamics and their implications for value creation. Building from theories of competitive dynamics and the resource-based view, my research challenges the dominant assumption of heightened opportunism and develops an alternative explanation to better explain coopetition dynamics. I identify and test informal market-based safeguards which reduce opportunism in coopetition. This provides theoretical resolution for conflicting findings in the literature and develops a nuanced understanding of the factors affecting opportunism in coopetition at multiple levels. It addresses the failure of extant research to explain coopetition dynamics and establishes foundations for systematic analysis of coopetition benefits and costs in future research. For managers, my findings move beyond simplistic perceptions that have emphasized instability, knowledge leakage, and the resultant need for formal safeguards in coopetition. Instead, I identify an efficient and effective alternative for constraining opportunism. This indicates that establishing, maintaining, and ultimately achieving value creation in coopetition relationships may be less challenging and costly than the literature assumes. Given the benefits of coopetition for both firms and society, this has important economy-level implications