Optimising the use of Materials for Construction MSMEs: Building a Comprehensive Framework for Decision-Making and Resource Allocation through an Analytic Hierarchy Process


  • Sohrab Donyavi ACE Department, University of East London, University Way, London E16 2RD, UK,
  • Roger Flanagan School of Construction Management and Engineering, University of Reading, Reading, UK,
  • Arya Assadi-Langroudi ACE Department, University of East London, University Way, London E16 2RD, UK,
  • Luana Parisi ACE Department, University of East London, University Way, London E16 2RD, UK


Construction MSMEs; Productivity; Supply Chain Management; Analytic Hierarchy Process; Decision-Making; Resource Allocation.


The efficiency, governance, and compliance with environmental ideals in construction is made possible thanks to a decision support system that ensures Materials, Models, and Methods (3Ms) are adaptable and integrated. Recent advances in Information Technology (IT), for instance, facilitate the visualisation of sequences and production stages in construction. Yet, this falls short in giving compatibility among the 3Ms, their suitability and workability, and their financial and legislative viability. To this end, this manuscript rethinks the concept of productivity, and lays the foundation for a new decision support system that is simple, affordable, and portable enough to attract large enterprises and MSMEs. Ideally, an efficient construction project has good flow of workstreams, is least complex, cost minimised but with added value, timely and in symbiosis with natural health provisions of the ecosystem. A mixed methodology based on gathering data from document reviews, semi-structured interviews, and observations of selected construction MSMEs, will allow to carry out longitudinal research and then code, group, link and analyse the collected raw data through the Analytic Hierarchy Process (AHP) Multi Criteria Decision Making technique. This technique is chosen to develop overall priorities for ranking the alternatives, measure, and monetise the impacting factors to draw out the main impediments to achieve good levels of efficiency. The outputs of the AHP analysis feeds into the novel decision support system, the concepts of which are introduced in this contribution.