ARCOM

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Bassanino, M N (1999) The perception of computer generated architectural images, Unpublished PhD Thesis, , The University of Liverpool (United Kingdom).

Savva, C (2023) Lessons that can be learnt using action research strategies within TfL, Unpublished PhD Thesis, , The University of Liverpool (United Kingdom).

Tomlinson, J (1998) A premises occupancy cost forecasting model, Unpublished PhD Thesis, , The University of Liverpool.

• Type: Thesis
• Keywords: taxonomy; occupancy; benchmarking; building design; cleaning; costs in use; estimating; facilities management; forecasting; case study
• ISBN/ISSN:
• URL: https://www.proquest.com/docview/1780168903
• Abstract:
  Whenthis research began in 1993 facilities management wasa relatively new discipline. The thesis begins by examining the backgroundand contributory factors to the evolution offacilities management,before identifying and discussing its four pivotal features of Property, People, Organisations and Change. The author examinesthe function androle offacilities managementandidentifies a numberof criteria by which its status as a new profession can be judged.Thethesis sets out a rigorousliterature review of the subject of Occupancy Costs and Costs in Use in which previous works are examined andtheir theories, opinions and models assessed in the light of current industrial practice and requirements. The results of a survey in which the author assessed the current practices used by organisations to collect and analyse their property related expenditure are set out.This thesis tests a hypothesis which emanated from the author’s belief that the costs associated with occupying premises are as much a product of the peculiarities of the occupying organisation as they are the physical characteristics of the buildingitself. The author examinedthe useofexisting cost benchmarking and comparison techniques which rely on mean or yardstick costs derived from samplesofexisting property, but found them of only limited use. This led to the hypothesis that an occupancy cost model which adopts a ‘bottom-up’ approach by forecasting individual items of expenditure from first principles could be at least as effective as existing techniques and provide additional modelling benefits.This thesis examines the variability of occupancy costs and the influence of the type of business enterprise, location, tenure, organisation, building design and time. The principle hurdles in structuring cost data for the management phase of a building’s life stem from the need to be able to equate levels of expenditure to aspects of the performanceorutility achieved, and the need to represent the physical components, systems, services and furniture which comprise the building and its support services. The Occupancy Cost Data Model (O.C.D.M.) is a universal taxonomy capable of classifying expenditure against the physical characteristics of the building and the performance requirements of the organisation. Adoption of the O.C.D.M. should promote consistency in the analysis of cost data, facilitate meaningful cost comparisons and benchmarking, and encourage dialogue and communication.The O.C.D.M.was devised to assist the developmentofthe Occupancy Cost Forecasting Model (O.C.F.M.). The O.C.F.M. adopts a ‘bottom-up’ approach to occupancy cost prediction for office buildings by using a computer spreadsheet application to simulate from first principles each item of expenditure. This allows forecasts of expenditure to reflect the physical characteristics of the building and its systems, and the performance requirements of the organisation. The development and structure of the O.C.F.M.is described in detail.The O.C.F.M. wastested and validated to ensure that it was adequateforits purpose and to allow those who useit to have confidence in its results and appreciate its limitations. Seven case study buildings and six other occupancy cost services were used to validate the model, and judgements made about the results based on the inherentdifficulty in estimating each cost, the degree of variability likely, and the nature and magnitude of each cost. The model’s forecasting ability for Window Cleaning and Internal Cleaning was particularly good (typically less than +15% error), whilst the inherentdifficulties associated with forecasting Electricity and Gas costs led to errors of up to 50%. No conclusions were made for Decoration or Relamping costs. The O.C.F.M. consistently outperformed the six other services. The O.C.F.M. was used to perform a sensitivity analysis which highlighted the financial significance of a numberof physical, constructional and operational parameters. The O.C.F.M. requires the user to enter detailed information about the building, as suchit s not a replacementfor the other techniques, most of which require only that the user knowthe building’s floor area. However, where such an elementary analysis is insufficient, the author has shown that the O.C.F.M. can produce results which are as goodif not better than those of existing techniques.