Engineering topics
Kao, Gio K.
Publications and source records attributed to Kao, Gio K..
Framework and methodology for supply chain lifecycle analytics
The various technologies presented herein relate to pertaining to identifying and mitigating risks and attacks on a supply chain. A computer-implemented representation of a supply chain is generated comprising nodes (locations) and edges (objects, information). Risk to attack and different attack vectors can be defined for the various nodes and edges, and further, based upon the risks and attacks, (difficulty, consequence) pairs can be determined. One or more mitigations can be generated to increase a difficulty of attack and/or reduce consequence of an attack. The one or more mitigations can be constrained, e.g., by cost, time, etc., to facilitate determination of how feasible a respective mitigation is to implement with regard to finances available, duration to implement, etc. A context-free grammar can be utilized to identify one or more attacks in the supply chain. Further, the risks can undergo a ranking to enable mitigation priority to be determined.
Macro Supply Chain Lifecycle Decision Analytics
This report summarizes a two-year LDRD project that investigated the problem of representing complex supply chains, identifying the worst risks and evaluating mitigation options. Our team developed a framework that includes a representation for business processes, risk assessment questions, risk indicators and methods for analyzing and summarizing the data. In our approach, the Process Matrix represents an overall supply chain for an end product in a high-level, tabular form. It connects the various touch-points of a business process including people, external vendors, tools, storage locations and transportation services while capturing the flow of both physical and intellectual artifacts. We believe these direct connections are exactly the things that a process owner can typically control. These material flows (both physical and intellectual) are also represented in a graph. This enables us to use graph-oriented analysis such as fault tree analysis and attack graph generation. Our approach is top-down, which helps users to get a more holistic understanding for a given amount of resources. Understanding the provenance of materials is difficult and it is easy to exhaust the analysts' resources. Rather than a tool to do vendor analysis or product comparison, our framework enables an enterprise-level analysis. The risk assessment questionnaires have a varying levels of detail and cover various aspects of the supply chain such as process steps, artifacts, suppliers, etc. and connections between these aspects such as artifact-storage, artifact-supplier, etc. Each question is further associated with one of seven risk indicators which can be used to summarize the risk. These risk indicators can also be weighted to reflect a user's concerns. We have successfully applied our framework to several use cases in various stages of its development and provided valuable insights to our partners, but it can also be applied to other complex systems outside of the supply chain security problem.
The CPAT Domain Model - How CPAT "Thinks" from an Analyst Perspective
To help effectively plan the management and modernization of its large and diverse fleet of vehicles, the Program Executive Office Ground Combat Systems (PEO GCS) commissioned the development of a large-scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This report contains a description of the organizational fleet structure and a thorough explanation of the business rules that the CPAT formulation follows involving performance, scheduling, production, and budgets.
The Capability Portfolio Analysis Tool (CPAT): A Mixed Integer Linear Programming Formulation for Fleet Modernization Analysis.
In order to effectively plan the management and modernization of its large and diverse fleet of vehicles, the Program Executive Office Ground Combat Systems (PEO GCS) commissioned the development of a large-scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This paper contains a thorough documentation of the terminology, parameters, variables, and constraints that comprise the fleet management mixed integer linear programming (MILP) mathematical formulation.
Quantitative adaptation analytics for assessing dynamic systems of systems: LDRD Final Report
Our society is increasingly reliant on systems and interoperating collections of systems, known as systems of systems (SoS). These SoS are often subject to changing missions (e.g., nation- building, arms-control treaties), threats (e.g., asymmetric warfare, terrorism), natural environments (e.g., climate, weather, natural disasters) and budgets. How well can SoS adapt to these types of dynamic conditions? This report details the results of a three year Laboratory Directed Research and Development (LDRD) project aimed at developing metrics and methodologies for quantifying the adaptability of systems and SoS. Work products include: derivation of a set of adaptability metrics, a method for combining the metrics into a system of systems adaptability index (SoSAI) used to compare adaptability of SoS designs, development of a prototype dynamic SoS (proto-dSoS) simulation environment which provides the ability to investigate the validity of the adaptability metric set, and two test cases that evaluate the usefulness of a subset of the adaptability metrics and SoSAI for distinguishing good from poor adaptability in a SoS. Intellectual property results include three patents pending: A Method For Quantifying Relative System Adaptability, Method for Evaluating System Performance, and A Method for Determining Systems Re-Tasking.