Decision support for locating manufacturing plants in emerging economies using a reliability approach

In today's distributed manufacturing reality, investors worldwide are faced with the dilemma of deciding on the optimal geographic spot for their manufacturing plants. On the one hand, emerging economies could be appealing because of their cheap labor as well as possibly their lack of or reduced regulations, litigation, and paperwork in some cases. On the other hand, these very same emerging economies can be quite risky because of the lack of stability of their political systems and hence, the associated economic volatility. Such economies can collapse in a relatively short period of time due to factors such as political instability, corruption, lack of democracy and the rule of law, social and racial injustices, and religious extremism, to name a few. In this paper, we propose a modeling approach where an economy is represented as an engineering system, the lifespan of which is subject to potential conditions, events, and failure modes. Such conditions and factors in the face of these fragile economies are modeled as pushers and deflators contributing to their instability. Hence, all laws of Reliability Engineering can be used to decide on the probability of success of such a system and its lifetime in the face of all uncertainty and given risks in today's global climate. It is imperative that the health of the economic climate is a critical element solving the facility location and allocation problem; this entails deciding on large manufacturing investments in the form of new manufacturing plants being constructed and the accompanied supply chains. Enablers to allow for packageable manufacturing systems easier to relocate in the wake of this uncertain economic turmoil are also discussed. System Dynamics will be used as future work to account for the forces (deflators and pushers) when quantifying the proposed metrics. AI and Data Analytics techniques are also recommended to quantify the reliability parameters.