Today's globalized supply chains are complex systems of systems characterized by a conglomeration of interconnected networks and dependencies. There is a constant supply and demand for materials and information exchange with many entities such as people, organizations, processes, services, products, and infrastructure at various levels of involvement. Fully comprehending supply chain risk (SCR) is a challenging problem, as attacks can be initiated at any point within the system lifecycle and can have detrimental effects to mission assurance. Counterfeit items, from individual components to entire systems, have been found in commercial and government systems. Cyber-attacks have been enabled by suppliers' lack of security. Furthermore, there have been recent trends to incorporate supply chain security to help defend against potential cyber-attacks, however, we find that traditional supply chain risk reduction and screening methods do not typically identify intrinsic vulnerabilities of realized systems. This paper presents the application of a supply chain decision analytics framework for assisting decision makers in performing risk-based cost-benefit prioritization of security investments to manage SCR. It also presents results from a case study along with discussions on data collection and pragmatic insight to supply chain security approaches. This case study considers application of the framework in analyzing the supply chain of a United States Government critical infrastructure construction project, clarifies gaps between supply chain analysis and technical vulnerability analysis, and illustrates how the framework can be used to identify supply chain threats and to suggest mitigations.
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.
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. iii This page intentionally left blank iv
Our society is increasingly reliant on systems and interoperating collections of systems, known as systems of systems (SoS). Our national security is built on SoS, such as Army brigades, airport security, and nuclear weapons security. These SoS are often subject to changing budgets, changing missions (e.g., nation building, arms-control treaties), changing threats (e.g., asymmetric warfare, terrorism, WMDs), and changing natural environments (e.g., climate, weather, natural disasters). Can vital SoS adapt to these changing landscapes effectively and efficiently? This paper describes research at Sandia National Laboratories to develop metrics for measuring the adaptability of SoS.Wereport thatwecouldnotfindasingle or absolute adaptability metric, in large part duetolackof general objectives orstructures of SoS. However, we do report a set of metrics that can be applied relatively, plus a method for combining the metrics into an adaptability index, a single value that can be used to compare SoS designs. We show in a test case that these metrics can distinguish good and poor performance under a variable mission space and an uncertain threat environment. The metrics are intended to support a long-range goal of creating an analytic capability to assist in the design and operation of adaptable systems and SoS.
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.
The globalization of today's supply chains (e.g., information and communication technologies, military systems, etc.) has created an emerging security threat that could degrade the integrity and availability of sensitive and critical government data, control systems, and infrastructures. Commercial-off-the-shelf (COTS) and even government-off-the-self (GOTS) products often are designed, developed, and manufactured overseas. Counterfeit items, from individual chips to entire systems, have been found in commercial and government sectors. Supply chain attacks can be initiated at any point during the product or system lifecycle, and can have detrimental effects to mission success. To date, there is a lack of analytics and decision support tools used to analyze supply chain security holistically, and to perform tradeoff analyses to determine how to invest in or deploy possible mitigation options for supply chain security such that the return on investment is optimal with respect to cost, efficiency, and security. This paper discusses the development of a supply chain decision analytics framework that will assist decision makers and stakeholders in performing risk-based cost-benefit prioritization of security investments to manage supply chain risk. Key aspects of our framework include the hierarchical supply chain representation, vulnerability and mitigation modeling, risk assessment and optimization. This work is a part of a long term research effort on supply chain decision analytics for trusted systems and communications research challenge.
The objective is to find the optimal fuel inventory management strategy roadmap for each supplier along the fuel delivery supply chain. SoSAT (System of Systems Analysis Toolset) Enterprise is a suite of software tools: State Model tool; Stochastic simulation tool; Advanced data visualization tools; and Optimization tools. Initially designed to provide DoDand supporting organizations the capability to analyze a System-of-Systems (SoS) and its various platforms: (1) Supporting multiple US Army Program Executive Office Integration (PEO-I) trade studies; (2) Supporting US Army Program Executive Office of Ground Combat Systems (PEO GCS) for Fleet Management and Modernization Planning initiative; and (3) Participating in formal Verification, Validation & Accreditation effort with Army Organizations (AMSAA and ATEC).