COST EFFECTIVE PRODUCT SUPPORT SOLUTIONS
With the need to make good decisions when designing and managing a Product Support solution, and a business drive to reduce ownership costs, the challenge of cost-effective equipment support is pushed to the limits. A tool of choice in these circumstances is Analyzer.
The Analyzer modelling software includes Life Cycle Cost (LCC) Optimization, Level of Repair Analysis (LORA), Spares Optimization, and Availability Modelling. It is a key tool for managers, decision-makers, engineers, ILS teams and other staff involved in system design, system acquisition, proposal writing, support solution optimization, in-service support management and through-life support.
Analyzer uses recognized processes and analytical methods to develop, store and evaluate information about operational equipment and the support environment.
A software product that provides the power of a modern GUI and the sophistication of the embedded proven algorithms. Analyzer can determine the best repair policy for equipment and reduce the cost of owning spare parts. The cost estimation and prediction support decisions based on Life Cycle Costs.
Analyzer is either a stand-alone decision modelling tool or integrated with OmegaPS LSAR to provide a tool that works with the many international standards of supportability data (eg. MIL-STD-1388-2B, SAE GEIA-STD-0007, DEF-STAN-0060, DEF-AUST 5692, S3000L) additionally, Analyzer can be aligned with any of your bespoke standards through data exchange. The core analyses of Analyzer include:
THE LCC MODEL
Determines the Life Cycle Cost (LCC) of an operation and support scenario. The basic cost breakdown structure for R&D, Acquisition, In-Service and Disposal is developed. The cost of allocated spares recommended by the Sparing model is also taken into consideration. The basic model calculations can be extended with user defined cost classes, inflation rates and customized costs. Cost results are analyzed by using Sensitivity or Trade-off analyses and by applying cost Risk analysis. The application of project-based cost breakdown structures aligns the LCC results with other project costs.
THE SPARING MODEL
The Sparing model determines the optimal allocation of the quantity of repairable and consumable spares within a defined logistics support organization, resulting in, minimum inventory requirements to meet desired availability for least spares cost. It handles replaceable units that will drive the Prime Equipment effectiveness and consumables stocking policies for inventory distribution, reorder points and reorder quantities.
Mission Analysis mode assumes equipment will be sent on a mission for a specified duration and that only on-site maintenance will be possible. This module provides a list of the spares required to maintain the equipment at a specified “Measure of Effectiveness”, such as availability.
THE LORA MODEL
The Level of Repair Analysis (LORA) model, determines the most cost-effective maintenance policy for each replaceable unit of a Prime Equipment. LORA examines the operating and support costs for every disassembly and repair option possible within the specified capability of the Maintenance Organization. After making the initial repair versus discard decision, LORA will select the optimal location for repair of the defective unit.
LORA uses cost estimates based on the LCC model and includes user defined values that are unique to the product analysis undertaken. LORA includes non-economic overrides to drive the repair decision based on operational and support constraints.
Modelling Support Tasks
Analyzer includes the definition of support tasks to allow for the more detailed modelling of your support solution. Using a Task Library, all parts in the logistical structure can have specific PM and CM tasks assigned. Each task is driven by some trigger (i.e., failure event, scheduled maintenance, inspection) and has task techs and resources assigned.
Using the task-based analysis a more detailed LCC can be estimated, and detailed labour hours determined.
Model Based Product Support
Analyzer is a valuable modelling tool to support your MBPS application. As a through-life modelling capability using the Baseline Comparison System approach to analysis, Analyzer will predict outcomes of alternative solutions during Options Analysis; determine optimal support solution designs during acquisition; and evaluate operational data feedback to assess performance providing improvements during the in service operational life.
AVAILABILITY ASSESSMENT MODEL
Analyzer provides an availability assessment function for the analyst to review the influence of the designed support solution with product usage and availability targets.
The Through Life Availability Simulation (TLAS) is a discrete event based, Monte Carlo simulation of the product’s support solution design within mission scenarios over its life cycle. TLAS provides outputs of the supportability and mission capabilities to assist in assessing the support solution design.
TLAS is a model that incorporates a definable life cycle to assess, by simulation, the described system, conducting various missions within a defined operations and support organization.
The concept of operational availability is closely related to the concept of operational readiness. In operational applications, this means that the assigned numbers of operating and maintenance personnel, the supply chain for spare parts, the support and test equipment and maintenance directions, and training are adequate. A manufacturer may be capable of manufacturing a very reliable and maintainable product (i.e., very good inherent availability). But if there is a poor distribution and transportation system or the parts needed are not stocked or enough service personnel to support the systems in the field are unavailable, then, the readiness of this system is low.
Logistic planners, design engineers and maintainability engineers can collaboratively estimate the repair needs of the system, required personnel, spares, maintenance tasks, repair procedures, support equipment and other resources. Only when all downtime causes are addressed will you be able to paint a realistic picture of the system’s availability in actual operation. The role of TLAS in simulating events based on the definition of the R&M values of the system and the support system in place is to identify what an expected Ao could be and the causes for not achieving established targets.
In cases where input data is based on engineering or contractor estimates as opposed to actual data, it is recommended that a study be conducted to determine how sensitive the solution is to variations in some input parameters. Analyzer provides a convenient method of performing sensitivity analysis by providing the user with a wide range of sensitivity factors that are applied at run-time and do not affect the data stored in the databases. Sensitivity analysis can be applied either globally on all values of a parameter or can automatically assess a sequence of changes to a specific value.
Analyzer provides for full trade off analysis to test the “what if?” scenarios. By comparing the results of a comparative analysis against the baseline, the user can determine the relative merit of different decisions.
Optimize asset maintenance and support solutions by understanding life cycle costs and sustainment resources needed to meet your operational demands.
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