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Logistics Organization Analysis (LX)

                                   LX Tools and Methods

Operations Analysis (OA)

Operations Analysis is a discipline which encompasses a set of analytical tools and methods which are applied to complex engineering and management problems in order to analyze them and gain insights about possible solutions. Simply stated it is the discipline of applying advanced analytical methods to help make better decisions. Practitioners of OA use three primary analytical tools: data analysis, optimization, and simulation.

Data Analysis

Data Analysis is the process of inspecting, cleansing, and transforming data and then systematically applying statistical and/or logical techniques with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. We use this technique for making predictions, identifying trends, and finding those variables that contribute to the resultant outcome. For example, we may have 1,000 instances of bearing failure and for each instance we have identified several related factors such as: the number of hours of operation; the heat of the bearing at the time of failure; the RPM of the bearing at the time of failure; and the amount of degradation the bearing had sustained as of the last measurement. From this dataset we can determine, with a specified level of confidence, which of those factors is ‘important’ in forecasting future bearing failures.

Optimization

Optimization, or Linear Programming, seeks to optimize (maximize or minimize) an objective given specified resources and constraints. Objectives are usually stated in terms such as 'Maximize readiness' or 'Minimize Cost'. Resources are things such as the amount of labor hours available or the amount of cubic lift we have. Constraints limit how those resources must be used. We may specify things like, ‘within budget’ or ‘by the Required Delivery Date’. The optimization process then translates those objectives, resources, and constraints into a set of algebraic expressions that can then be solved to find the best possible (or optimal) solution from among a large number of feasible answers.

Modeling and Simulation

Modeling and Simulation (M&S) is a discipline for developing a level of understanding of the interaction of the parts of a system and of the system as a whole. M&S seeks to do this by producing a simplified representation of the system with a set of computer instructions (a model) and then repeatedly varying designated inputs (simulation) in order to explore how those inputs affect parts of the system and the system as a whole. M&S allows us to quickly and cheaply explore many variations and outcomes of a process to gain insights and to explore and refine processes prior to investing capital in full scale production. The level of detail in M&S can vary widely and depends on input from Subject Matter Experts.

Verification, Validation and Accreditation (VV&A)

VV&A is the process of establishing confidence in models and programs for use in the I&L community. Without this process, decisions and analysis derived from unaccredited models lack the official backing and support of the department. Verification is the determination a model or simulation accurately represents the developer’s conceptual description and specifications. Validation is the degree to which a model or simulation is an accurate representation of the real world. Accreditation is the determination that a model or simulation is acceptable to use for a specific purpose.