Abstract:
Strategic planning (SP) is the process of aligning an organization’s activities with its own vision and mission. Several strategic planning frameworks and tools were developed such as SWOC, Porter’s five forces and PEST analysis. So far the balanced scorecard (BSC), proposed by Norton and Kaplan, is the most consistent since it accounts for strategic measures in four major perspectives. Shaping relevant decision rules to meet the target measures associated with the BSC four perspectives becomes a multiple-policy multi-objective (MPMO) process. During the past four decades, there has been some development of analytical methods that can guide SP analysts in policy makings of large systems. Different policy design techniques are proposed that help in steering organizations towards meeting a target level. Designing policies is usually constructed as a set of single-policy single-objective subsystem where proportional and, at most, derivative feedback control is presented without taking into consideration the four BSC perspectives.
In this thesis we consider a Master’s University, such as the Lebanese American University, as the organization. We associate the number of enrolled students, the academic reputation, student-to-faculty ratio and research productivity, and faculty recruitment and faculty development funds with the four BSC perspectives. The policies under consideration are number of faculty to be recruited, development funds to be dedicated to faculty at the associate professorial rank, and development funds to be dedicated to faculty at the professional rank. A 28th-order nonlinear state-space model is constructed in order to reflect the relevant system dynamics. A multiple-input multiple-output (MIMO) Proportional-Integral-Derivative (PID) controller is implemented for shaping the correlated three policies involved in this MPMO system. The associated ten-year target levels are set such that the university reputation is significantly improved, and the overall financial balance is considerably large in order to accommodate for capital expansion. Numerical simulations are included to illustrate the effectiveness of the proposed MPMO systematic approach.
