A NOVEL APPLICATION OF SELECT AGILE CONCEPTS AND STOCHASTIC ANALYSIS FOR THE OPTIMIZATION OF TRAINING PROGRAMS WITHIN HIGH RELIABILITY ORGANIZATIONS IN HIGH TURN-OVER ENVIRONMENTS AT EDUCATIONAL INSTITUTES AND IN INDUSTRY
Creators: name: Blanton, Richard L
Publisher: name: Digital Repository at the University of Maryland
Year: 2023
Subjects: subject: Operations research
subject: Civil engineering
subject: FOS: Civil engineering
subject: Organization theory
subject: Agile
subject: Markov matrices
subject: Stochastic analysis
subject: Training program optmization
subject: Transfer of training

description: High-turnover environments have been extensively studied with the bulk of the literature focusing on the negative effects on business operations.[1] They present challenges to the resilience of the organization while also limiting the potential profitability from consistently having to spend time training new staff. Furthermore, in manufacturing environments inexperienced staff are prone to mistakes and uncertainty, which can lead to increases in scrap materials and lower production rates due to a lack of mastery of the process. From an organizational standpoint a high-turnover environment presents an unmitigated risk to the organization from the continuous loss of institutional knowledge. This loss can present challenges to the organization in numerous ways, such as capital equipment that no longer has staff qualified or experienced enough to use it leading to costly retraining by the manufacturer or increased risk of a catastrophic failure resulting in damage to the equipment and or injury to the staff. Furthermore, the loss of institutional history leads to the loss of why operations are performed a certain way. As the common saying goes, “those who forget history are bound to repeat it.” which can lead to substantial costs for the organization while old solutions that were previously rejected due to lack of merit are constantly rehashed due to a lack of understanding of how the organization arrived at its current policies. This thesis presents a novel framework to mitigate the potential loss of institutional knowledge via a multifaceted approach. To achieve this a specific topic was identified and used to frame questions that guided the research. Mitigation of the negative impacts of high-turnover in manufacturing environments with a specific focus on the optimization of training programs. This topic led to the formulation of the following research questions. What steps can be taken to reduce the chance of lost institutional knowledge in a high-turnover environment? What steps can be taken to reduce the time needed to train a high performing replacement employee, while maintaining strict performance and safety standards? What steps should be taken to improve the accuracy of budgetary projections? What steps need to be taken to enable accurate analysis of potential future investment opportunities in a training program. The answers to the above research questions are compiled and presented with the aim to provide professionals, who are responsible for training programs in high-turnover environments that require a high organizational reliability, with a framework and analysis toolset that will enable data-driven decision making regarding the program. Additionally this thesis provides a framework for addressing the continuous risk of loss of institutional knowledge. When contrasted with a standard training model, where a trainee is presented with new material and then tested for retention before moving to the next topic, the proposed model implements a schema that can be rapidly iterated upon and improved until the desired performance outcome is achieved, while increasing the potential accuracy of budgetary estimation by as much as 57%. Throughout the process, decision makers will have insight into the long term effects of their potential actions by way of running simulations that give insight into not only the expected steady-state cost of a program but also the rough volume of trainees required to achieve that steady-state.