Andrew Flitman

Can critical realism enable a journey from description to understanding in operations and supply chain management

Purpose – The current state of theory-building in the field of operations and supply chain management (OSCM) is in a strong need of rigorous, empirically based theories that enhance understanding of the causal relationships between the structural elements and properties of the business processes. In this research note the authors propose the critical realism (CR) philosophy of science as a particularly suitable philosophical position (not to the exclusion of others) to review the mechanisms of OSCM knowledge generation and to provide philosophical grounding and methodological guidance for both OSCM theory building and testing. Design/methodology/approach – To demonstrate potential benefits of CR-based structured approach to knowledge generation in OSCM research, this conceptual paper uses a case study that illustrates the adoption of one of the OSCM theories – i.e. the theory of swift, even flow. Findings – CR interprets the accumulated empirical information about OSCM phenomena as observable manifestatio...

Towards meaningful benchmarking of software development team productivity

Software development projects are known for inaccuracies associated with elapsed time and total cost estimates. Attempts have previously been made to provide tools to facilitate estimation of just how much effort will be required. One such tool is the estimation of project size (and therefore effort and time required) using function point counts. This benchmarking tool facilitates measures of productivity relating this size to the person‐hours required. The problem with this is that such relative productivity measures assume labour hours to be homogenous and that the only measure of output is the size of the project. This paper investigates the use of data envelopment analysis as a method of benchmarking which overcomes these issues. The end result is a set of simple tools that can be used to determine whether a given project or project plan is efficient.

Towards probabilistic footy tipping: a hybrid approach utilising genetically defined neural networks and linear programming

Using readily available data from the 1992-1995 Australian Football League season, we have developed a model that will readily predict the winner of a game, together with the probability of that win. This model has been developed using a genetically modified neural network to calculate the likely winner, combined with a linear program optimisation to determine the probability of that occurring in the context of the tipping competition scoring regime. This model has then been tested against 484 tippers in a probabilistic tipping competition for the 2002 season. We have found that the performance of the combined neural network, linear program model compared most favorably with other model based tipping programs and human tippers.

Neural and machine learning to the surface defect investigation in sheet metal forming

Surface defects such as wrinkling and buckling are a serious quality problem in the sheet metal-forming industry. This paper presents using information processing techniques (artificial neural networks and machine learning approaches) to study the geometrical influence on the formation of wrinkling for automobile components.

Value-Focused Business Process Engineering : a Systems Approach

One of the keys to successful business process engineering is tight alignment of processes with organisational goals and values. Historically, however, it has always been difficult to relate different levels of organizational processes to the strategic and operational objectives of a complex organization with many interrelated and interdependent processes and goals. This lack of integration is especially well recognized within the Human Resource Management (HRM) discipline, where there is a clearly defined need for greater alignment of HRM processes with the overall organizational objectives.Value-Focused Business Process Engineering is a monograph that combines and extends the best on offer in Information Systems and Operations Research/Decision Sciences modelling paradigms to facilitate gains in both business efficiency and business effectiveness.The first part of the book examines a wide range of research issues in modelling business processes and objectives by expanding the focus of business process engineering from creating more efficient business processes to also making them more effective. The second part of the book presents methodology and tools for a holistic approach to business process engineering within the HRM context. The original contribution of the value-focused process engineering methodology lies in the integration of the best aspects of event-process driven chains and the value-focused thinking within a single model, in a way that preserves the strengths of the respective models while facilitating the emergence of new properties that satisfy goal-oriented business process modelling requirements.The result is an up-to-date expository monograph that provides industry practitioners and researchers in OR/MS and IS domains, as well as those concerned with the practice of HRM, with a framework and implementation guidelines for ensuring that day-to-day business activities are congruent with organizational values and strategic objectives.

A Qualitative Method for Identifying Factors that Influence User Satisfaction

Understanding what influences users’ satisfaction, or otherwise, with computer systems is a topic of importance to many within the field of information systems. Research into this question has typically relied on quantitative methods; in the course of a formal research project, a new method was developed which investigates the question using qualitative techniques. The method, along with the practical and theoretical context that gives it relevance, is described in this paper. For clarity, the method will be referred to herein as MIFIUS-QI (a Method for the Identification of Factors Influencing User Satisfaction — Qualitative).

Investigating neural network modeling decisions for the australian all-ordinaries index

Estimating stock market output depends mainly on identifying non-linear relationships of input variables. To forecast such systems a non-linear modeling tool is required. This paper describes the experimental approaches for developing an Artificial Neural Network for the purpose of modeling the Australian All Ordinaries Index movement over a prediction horizon of 1 year. Network parameters such as network architectures, input data sizing and periodicity are considered in the development of the network. The evaluation criterion for the Neural Network output is the R Square Statistic.