A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item 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(2023) Blanton, Richard L; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)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.Item A Complexity-Based Approach to Intra-Organizational Team Selection(2013) Hsu, Shu-Chien; Cui, Qingbin; Skibniewski, Miroslaw; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Early studies recognized the significance of team's work capacity and suggested the selection of team members based on individual skills and performance in alignment with task characteristics. The equitable team selection method, for example, assigns people to different tasks with even skill distributions for the best overall performance. Recent advancement in organization science also identifies the importance of contextual skills. However, work teams are complex adaptive systems with interdependence between workers and social environment, and exhibit surprising, nonlinear behavior. Optimizing individual stages without taking organizational complexity into account is unlikely to yield a high performing new combination of teams. The objectives of this study can be stated as: a) Utilizing complex system theory to better understand the processes of team selection including forming teams with considering worker's interdependence and replacing the unsuitable members through a time frame; b) Comparing different team selection methods, including random selection, equity method, using knowledge of interdependence in different economic conditions through simulation; c) Comparing different policies of replacing members of teams. This study utilizes a computational model to understand the complexity of project team selection and to examine how diversity of capability and interdependence between workers to effect team performance in different economic conditions. The NK model, a widely used theory for complex systems is utilized here to illustrate the worker's interdependence and fed into an Agent-Based Model. This study uses a small design firm as a case implementation to examine the performance of a variety of team selection approaches and replacement policies. Project data, task assignment, and individual and team performance information were collected for the period of 2009-2011. The simulation results show that while the equity selection method can increase the diversity of capabilities of teams, the net performance is often worse than optimizing worker interdependencies. This study suggests that managers should protect their higher-performing workers with minimal interdependence disruption when they considered team selection. Thus taking the advantages and disadvantages of all three policies into account, transferring low contributors or least supported members are recommended to be enacted before hiring new workers to avoid this last policy's especially large additional costs.