As the MRL team continues to redefine the risk assessment and management process at Pratt & Whitney, the results of this project set the groundwork for a large push to-ward integration of real time data into the risk assessment process. The three phases of this project support specific challenges currently met by the qualitative risk assess-ment process, but also comprise a single comprehensive quantitative risk assessassess-ment methodology that can be used in tandem with the legacy industry approved methods.
Going forward, most of the development of this project will be in integration into central data analytics systems. The IT team is currently obtaining data connection approval with several sources that will allow for transition from the semi-automated proof of concept model to a fully automated and customizable dashboard.
Concerning the internal logic of the risk assessment process, a major hurdle for this project was determining methods of retroactively calculating risk impact for previous programs. Ultimately, due to lack of data collection infrastructure at the time, the team was unable to build a reasonable set of empirical risk data. However, following the completion of this project, tools from this thesis have set up the ability of the program to collect operations data next to empirical risk data in order to build a training set. While the current risk calculation methods were determined using knowledge from industry experts, and research into the cost structure of the organization, a purely quantitative method of generating the risk formula would yield interesting results. Now that the tools are built, the MRL team has the ability to continuously learn from trends in implementation of the quantitative risk assessment process.
Chapter 7 Conclusions
The hypothesis of this project was that development of standard processes and tools for identifying, analyzing, and mitigating production risk using real time operations data significantly enhances the ability of management and manufacturing teams to understand and react to operations inefficiencies that reduce final production capac-ity. Figure 7-1 shows the flow of information through the Quantitative Risk Assess-ment process developed by this thesis, and the resulting outputs used to validate the hypothesis.
Through development and initial implementation of the quantitative risk assess-ment process, the results showed significant improveassess-ments in risk assessassess-ment effi-ciency. The quantitative process introduced automation and numerical methods into parts of the legacy risk assessment process that normally required large amount of non-value added time to the Operations organization. Rather than having industry experts spend weeks requesting and manipulating data, the outputs of this project centralize critical data and standardize analysis allowing them to focus on applying their expertise to complex problems.
Integration of quantitative operations metrics into the legacy qualitative system provided a purely quantitative output that had the ability to be aggregated describing any group of parts in an engine program. This allows management to use the same method of analysis at a part level and program level maintaining consistency between assessments.
Figure 7-1: Information flow diagram depicting inputs and outputs of Quantitative Risk Assessment process. Shows movement of data from independent sources to Ops-BOM and final dashboard as well as final validation interaction with current MRL process.
Finally, development of mitigation tools increases communication between the MRL team and individual manufacturing teams, and enables manufacturing teams to take ownership of MRL and general risk improvement efforts. Streamlining the handoff between MRL and manufacturing significantly increases the Operations orga-nization’s ability to react and adapt to risk through the lifecycle of an engine program.
Due to the many observed benefits from initial implementation of the quantitative risk assessment process and identified future benefits as the project continues to grow within Pratt & Whitney, this thesis was able to produce a standardized process that enhanced the ability of the Operations organization to understand and react to operations inefficiencies in turbine engine programs.
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