Ray Wyatt

To build or not to build? Assessing the strategic needs of construction industry clients and their stakeholders

The pre‐design stage of construction projects has become a focal point in design management research in the last decade, as it is primarily the source of problems such as rework, change orders, and contractual claims. In particular, it is widely acknowledged that client briefing is an intractable problem which many projects encounter. Primarily this is because little attention is given to assessing the needs of the client, stakeholders and those of the design team. With this in mind, this paper presents a soft system approach known as strategic needs analysis (SNA) to assist clients, stakeholders and their design teams in determining their strategic needs for a given project. The rationale for using a SNA approach during the early stages of the project development process and in relation to the strategic environment of the client organization is presented and discussed. The SNA process is described and applied to two case study projects. It is concluded that SNA can improve the strategic decision‐making process of a project, as clients are able to identify their strategic needs and thus improve the effectiveness of the briefing process.

A method for strategic client briefing

Strategic client briefing is now recognised as an essential component of best practice in facilities management. A number of different briefing approaches have evolved, or are being developed, and this paper presents strategic needs analysis (SNA). It has been applied within six project‐inception studies with real clients, for developing and choosing a strategic direction for the project being considered by all the stakeholders. Moreover, a survey of stakeholders was carried out after each study, in order to find out how well participants thought the workshop performed in terms of six key process characteristics. Such key characteristics were further divided into a total of 41 additionally assessed attributes considered as important within the client‐briefing process. Analysis of such assessments revealed some interesting positive and negative features. Consequently, the SNA approach was refined. Presents the major findings of the work carried out along with some observations about overall processes, and suggestions for further improving strategic client briefing using SNA or similar approaches.

A discrete simulation approach to spatial allocation of commodity production for revenue optimisation over a local region

A simulation approach is described for the spatial allocation of crops across a region in order to maximise total revenue. The model uses inputs from GIS-based land suitability analysis to provide data on yields for a range of commodities, where the land suitability for the crops can be determined by either biophysical models or multi-criteria analysis. The objective of the study was to gain some indication of the magnitude of improvement possible in revenue, based on the convergence results for the optimisation (subject to estimated production quantities and market prices). The basic structure of the model allows for scaling up to larger problems with additional inputs and finer cell resolution. The software produces a visualisation of crop spatial allocation across the region and is compatible with statistical uncertainty analysis. The results of model simulations revealed a significant increase in revenue is possible using this approach and, when projected over the full region, suggests the possibility of significant economic benefits.

Using Neural Networks for Generic Strategic Planning

This paper argues the scarcity of strategic planning software is due to Western philosophical traditions which see strategy hypothesising as a mysterious, intuitive process that resists analysis and computerisation. But progress is possible if one extracts, from the tactical planning literature, eight key, generic, strategy-evaluation criteria. An experiment then tests whether scores on such criteria can be used to power machine learning of overall strategy desirabilies, and whether such learning is better achieved using multiple regression analysis or a simulated neural network. Both methods were successful, but the neural network was clearly the most accurate. It therefore constitutes a promising basis for self-improving, strategic planning software.

A Planning Support System Toolkit Approach for Formulating and Evaluating Land-use Change Scenarios

Distributing land uses across space is the central problem addressed by the urban and regional planning discipline. Millions of words have been written about it, and there has been significant investment made by various planning agencies around the world trying to do it better. Nevertheless, from the time of the late-nineteenth century origins of town and country planning until around the mid-twentieth century, land-use planning was usually done in a non-analytical way, by architects and engineers who were simply content to tidy up the environment by such means as physically separating incompatible land uses and ensuring efficient servicing of them with properly distributed pipes, wires and transport arteries. After the Second World War, however, the nature of urban and regional planning changed forever. This was due to at least two phenomena. Firstly, urban and regional planning came to be seen as a field where practitioners could actually see and touch social change, and so many other sorts of professions became involved in the discipline as it became more socially and less physically oriented. Secondly, the Second World War had seen the development of computers, and it was not long before they were applied to the urban and regional planning discipline in a bid to better manage its new found complexity. The problem was that early computers were difficult to manage. Indeed, the old adage that computers are frustrating because they do what you tell them rather than what you want, still holds true today. Nor was the theory of urban and regional planning up to the task of making the massive, computerized models of urban and regional growth plausible. The result was a huge disillusionment with large-scale urban analysis and modelling, and it is commonly regarded as having begun with the publication Chapter 4 A Planning Support System Toolkit Approach for Formulating and Evaluating Land-use Change Scenarios

Key decision-making attributes for project inception

Purpose – The purpose of this paper is to focus on the intense research activity with regard to the project inception stage. The need to establish the project parameters and performance requirements is crucial to the success of any construction project. Many organizations have been developing approaches to assist everyone involved in this process. This study aims to provide some data on one approach used by the authors during these early stages.Design/methodology/approach – One approach to these early stages of the project is the use of a workshop‐based technique termed “strategic needs analysis”. Strategic needs analysis assists in these critical inception stages in the development of a project. Six action research studies based on these workshops were organized and analyzed by the authors. This has resulted in the development of a series of decision‐making attributes that capture the key characteristics relevant to the project inception stages.Findings – This paper analyzes and identifies on a two‐dimen...

Application of GIS-based computer modelling to planning for adaption to climate change in rural areas

A GIS-based computer modelling methodology was developed and applied to identify climate change adaptation issues arising in regional agricultural production systems (including forestry). Agricultural production in Australia is very susceptible to the adverse impacts of climate change due to projected shifts in rainfall and temperature. The methodology integrates land suitability analysis with uncertainty analysis and spatial (regional) optimisation to determine optimal agricultural land use at a regional scale for current and possible future climatic conditions. The approach can be used to recognise regions under threat of productivity decline, identify alternative cropping systems that may be better adapted to likely future conditions, and investigate implementation actions to improve the sub-optimal situations created by climate change. An example of how the methodology may be used is outlined through a case study involving the South West Region of Victoria, Australia. The case study provides information on the tools available to support the formulation of a regional adaptation strategy.

Evaluating Strategies by Means of an Artificial Neural Network

In this paper, advice-giving software which uses various strategy-evaluation criteria is described. Ultimately the software will make use of an artificial neural network to connect the scores of strategies on the evaluation criteria with their overall desirability scores, and such ‘learning’ will, in theory, enable the software to give better and better advice the more it is used. To test this, a preliminary experiment was conducted to see whether simulated neural networks can actually mimic relationships between strategy-evaluation criterion scores and overall scores. The results suggest that artificial neural networks might be more accurate than conventional statistical methods at predicting overall strategy scores.

An optimisation model for spatially allocating commodity production and forecasting its impact on regional productivity

The prototype Spatial Optimizer 1.0 program for allocation of commodity production is demonstrated to show its potential as a decision support aid for policy making. The case study involves a set of agricultural commodities in south eastern Australia and the program uses estimates of each grid cell’s soil suitability for each of the eight crops, both in the year 2000 and in the year 2050, by which time soil characteristics are expected to have been affected by environmental change. We first predict how much total regional production will result from a judicious re-location of commodity types, both under conditions of complete flexibility and when constrained by more realistic, upper and lower limits on production, and we compare such predictions with current production levels. We also estimate potential total regional revenue, both in the short term when current prices are assumed to remain static and in the long term when prices are assumed to change according to how much of each commodity is produced compared to its current output level, and we compare these results with current agricultural revenue. Our long-term estimates are based on year 2000 soil-suitability values and then on year 2050 soil-suitability values in order to gauge the probable impacts of environmental change. Finally, we run the 2050 simulation twice more, with one of the recommended, dominant crops removed in each instance. This generates maps of some localised concentrations of other commodities that will become necessary in the future if maximum revenue is to be retained after one of the more lucrative crops is discontinued.

Reverse Engineering It

Norman and Tina trawled their system’s databanks to find that traditional, human-driven stakeholder workshops might entrench attitudinal differences rather than foster greater consensus like computer-driven workshops do. They also found tentative evidence for hypotheses such as older people preferring easy plans, Westerners preferring plans that are correct and effective and people from the East favouring plans that are efficient and acceptable. In addition, they uncovered similarities in the planning priorities of students from places as far apart as Brazil and Sardinia, and they mapped people’s attitudes within different types of neighbourhood.