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Improving project efficiency with dynamic resource redistribution in software development companies

Mandourarakis Ioannis

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Summary

All start-ups, especially those based on innovative technologies, where their project management experience is limited to the most recent data projects that present similar know-how (e.g., project profiles in software development companies), face major difficulty in assessing costs that emerge from the inefficiency of provisional planning (like company positioning and tactical pivoting based on pre-planning, requirement analysis, the definition of cost centers and evaluation regarding project logistics and procurement status). This involves a) the risks during the fixation of contracts (mostly related to the evaluation of the pricing margin for maximizing profitability), b) the deviations influencing the program and project scheduling of the implementation and testing cycles of the software, and consequently, c) the effects on the ordinary business-as-usual processes causing misalignment effects of the resulting product mix against the strategic objectives of the enterprise.This dissertation proposes a method that can extract useful insights for any potential improvement in project delivery times through re-allocation remarks of human resources in project-based teams, where the selection takes place with limited or no evidence-based reviews. The method assumes no or limited knowledge on similar project performance outside the frame of the company’s ecosystem, so it is solely based on estimations regarding the skill and competence level of the members of the teams against the requirements of each project. The confidence of the method can be increased by refining the initial estimations during project execution. The results facilitate the method’s application maturity, with gradual weight factors’ readjustment per iteration, enhancing the credibility of future solutions. This tool is mostly useful in start-ups with no gap analysis or prior knowledge regarding the trade traits and, subsequently, the available margins of improvement per case. The method is designed to be applied in a regular or sporadic fashion, allowing for iterative re-estimations that can support interactive decision-making in various levels of corporate governance. The work of this research is based on a multi-criteria decision support system that has been coded in the MATLAB environment. The support system examines various scenarios based on real business cases and realistic data for dynamic resource allocation, exploiting competency-profile matching based on a genetic algorithm design.

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