Resource allocation in multi project management

Planning and human and asset allocation are the biggest issues of the modern job order organizations, because of the unpredictability of the events. An increase of personnel could often be the ideal solution, even if it generates cost surplus. The work is aimed at proposing a methodology for human resources allocation, applied to the technical department of “Dallara automobili”, a job order auto manufacturer. Designers and R&D staff have to solve short term problems, because of the lack of time that characterizes interdependent projects. Such a company needs a different technical department composition, that could change not only in personnel number, but in mix, too. That’s not possible because of over costs (recruitment and reassignment costs) and legislative matters. The built model focuses on the design and manufacturing activity, considering only two types of projects: IPS, that competes in a mono mark championship, and IRL with a high technological level. IPS has a low influence on business turnover, but a high technological stability, because it isn’t introduced in a competitive market. The scope of work is to create a mathematical model that changes or confirms the actual technical department (the core of the engineering and manufacturing design and definition, at present consisting of 3 designers, 4 draftsmen and 2 R&D staff), maximizing business turnover, due to minimizing management costs. The model comes out of six steps: step 1 is a detailed analysis of the actual technical department outline, gathered with interviews and related material. Some groups of external events, that potentially create planning and delivery delay, are identified. Their weights (measured in a 1 to 5 scale) are Monte Carlo Simulation inputs, that gives the global medium weight and the global frequency of every group of events. In step 3 a “human resource allocation function” is created, using two tables that correlate number and working time of each kind of personnel, to the correlated costs. “Consequences function” (step 4) takes up from the same tables, but it represents how costs, caused by events with different consequences, are knocked down by a bigger number of personnel. The function is considerate as a branch of a hyperbole: at growing number of personnel, costs produced by external events go down. Summing “human resource allocation function” with “consequence function” (step 5), “total cost function” is made: the minimum cost configuration is the required solution. In step 6 there is a possible scheduling of human resources. The model shows as dangerous events’ consequences could be reduced by a specific personnel mix, correlated to an ad hoc scheduling.