  Our focus is on projects, i.e., business processes, which are emerging as the
economic drivers of our times. Differently from day-to-day operational
processes that do not require detailed planning, a project requires planning
and resource-constrained scheduling for coordinating resources across sub- or
related projects and organizations. A planner in charge of project planning has
to select a set of activities to perform, determine their precedence
constraints, and schedule them according to temporal project constraints. We
suggest a data-driven project planning approach for classes of projects such as
infrastructure building and information systems development projects. A project
network is first learned from historical records. The discovered network
relaxes temporal constraints embedded in individual projects, thus uncovering
where planning and scheduling flexibility can be exploited for greater benefit.
Then, the network, which contains multiple project plan variations, from which
one has to be selected, is enriched by identifying decision rules and frequent
paths. The planner can rely on the project network for: 1) decoding a project
variation such that it forms a new project plan, and 2) applying
resource-constrained project scheduling procedures to determine the project's
schedule and resource allocation. Using two real-world project datasets, we
show that the suggested approach may provide the planner with significant
flexibility (up to a 26% reduction of the critical path of a real project) to
adjust the project plan and schedule. We believe that the proposed approach can
play an important part in supporting decision making towards automated
data-driven project planning.
