1. BERNARD PRICE Certified Professional Logistician
Supportability Optimization to Achieve Availability Goals in Acquisitions
This topic will cover supportability optimization to achieve availability goals in acquisitions. It will show the importance of sparing to availability modeling to generate cost effective end item support. It will introduce some generic Army product support, logistics chain analysis models that contain the sparing to availability methodology. The models can be provided to contractors at no cost.
BERNARD PRICE
Certified Professional Logistician

2. Achieving a System Operational Availability Requirement (ASOAR) Model
Optimally Allocates System Ao to an End Item Ao goal for the End Item Being Acquired
Integrated Macro-Level RAM and Supportability Analysis to Help Generate Early-On Requirements
Considers End Item Redundancy and Floats, Periodic Maintenance Actions, and Reliability of Other End Items In System in Ao Goal
Determines Ao Inputs to Use in Supportability Optimization Models
Drawback – Today Only DCSOPS Systems Analysis can Run ASOAR
The Achieving a System Operational Availability Requirement (ASOAR) model was developed by DCSOPS Systems Analysis Division. ASOAR can optimally allocate a system Ao or operational readiness rate requirement to an end item Ao goal for the end item being acquired.
ASOAR provides a macro-level, integrated analysis of reliability, availability and maintainability (RAM) and supportability. Since an ASOAR analysis does not require Line Replaceable Unit (LRU) level inputs, the model can be used early-on in the development cycle to help generate RAM requirements. The ASOAR model can consider end item redundancy and floats, periodic maintenance actions, and the reliability of other items in the system in its Ao goal allocation.
The end item Ao outputs of ASOAR can also be used later in supportability optimization models as the end item’s Ao objective function input.
Unfortunately, when the operating systems of our computers progressed, this FORTRAN based program will no longer work. If needed, the CE LCMC DCSOPS Systems Analysis Division can run the ASOAR model for you on an off-line computer with a Windows 98 operating system.

3. System Supportability Optimization Modeling to Operational Availability
SYSTEM Ao/ READINESS RATE REQUIREMENT
OPTIMAL ALLOCATION OF OPERATIONAL AVAILABILITY (Ao)
ASOAR
END ITEM Ao GOAL
MAINTENANCE
OPTIMIZATION
This picture reiterates how the Achieving a System Operational Availability Requirement (ASOAR) model helps to promote system supportability optimization modeling. It also introduces two Army standard support effectiveness optimization models using Operational Availability (Ao) as an objective function input.
The ASOAR model allocates a system Ao or readiness rate requirement to cost effective end item Ao goals or requirements. The allocated end item Ao outputs yield sparing to availability supportability optimization model input goals for those end items being developed or separately acquired that goes into the system.
The Computerized Optimization Model for Predicting and Analyzing Support Structures (COMPASS) model, which is an Army standard maintenance optimization model, will provide the least cost maintenance concepts for the Line Replaceable Units (LRUs) and Shop Replaceable Units (SRUs) within the end item. The Selected Essential-Item Stock for Availability Method (SESAME) model, which is an Army standard supply optimization model, will provide the least cost sparing mix for the critical LRUs within the end item.
SUPPLY
OPTIMIZATION
COMPASS
SESAME
LEAST COST MAINTENANCE
CONCEPT FOR LRUs & SRUs
LEAST COST SPARING
MIX FOR LRUs & SRUs

4. Logistics Chain Support Effectiveness Optimization
End Item
Multi-Echelon
Multi-Indenture
Support effectiveness optimization modeling will optimize maintenance and supply concepts with regard to the logistics chain in achieving the end item or system Ao goal or requirement. These models are decision recommendation tools that provide best value multi-echelon solutions for each multi-indentured level item.
Army standard support effectiveness models will permit analysis solutions for 1, 2 or 3 retail level support echelons and the wholesale level shown as the Depot. For supply optimization, the optimal sparing mix at the Organizational (Org) Unit level, Direct Support Unit (DSU), and/or the General Support Unit (GSU) are determined. The Depot level is modeled to have spares stored to a certain stock availability level. For maintenance optimization, the most economical support level to accomplish repair for each item, or throw away in lieu of repair, is determined.
Army standard support effectiveness models will permit analysis solutions for each Line Replaceable Unit (LRU) within the End Item and the higher failure rate Shop Replaceable Units (SRUs) used to repair the LRUs.

5. Sparing to Availability Concept Optimization Heuristic
LRU Cost to Failure Rate to Down Time Ratios Without LRU Spares are Compared (COST x MCTBF / CWT)
LRUs with Lowest Ratios are Spared Forward First
Sparing Lowers CWT to Increase Ratio for Next Spare
The LRU Sparing Increase Stops When the Product of LRU Availabilities Equal the End Item Ao Target
LRUs with a Ratio Higher Than the Final Ratio Meeting the Ao Target Will Not be Spared
A heuristic is a simplified methodology that yields outcomes close to a more complex methodology. This sparing optimization heuristic should provide results somewhat close to a Sparing to Availability optimization model. This heuristic is based on the cost to failure rate to downtime ratios of each Line Replaceable Unit (LRU) relative to each other. The LRU Cost times its Mean Calendar Time Between Failure (MCTBF) divided by its Customer Wait Time (CWT) per failure are the values used to compute this ratio. After comparing, the LRUs with the lowest cost to failure rate to down time ratios are spared forward first. Sparing an LRU lowers its CWT value to increase the LRU’s ratio associated with its next spare. The LRU sparing increase of lowest ratio items stops when the product of critical item LRU availabilities meets or exceeds the end item Ao target. LRUs that happen to have a ratio higher than the final ratio meeting the Ao target will not be spared at the forward support level.
Since LRU sparing only applies to corrective maintenance, down times caused by servicing, preventive maintenance, software changes, etc. are accounted for in the Ao target. The Ao target associated with corrective maintenance becomes a higher value than the equipment’s Ao goal or requirement to compensate for those other downtimes not impacted by LRU sparing. The Ao adjustment is determined by the frequency of each non-corrective or non-hardware maintenance action that brings the equipment down and the length of down time associated with that action.

6. Sparing Optimization Example
GIVEN:
LRU2 COSTS 20 TIMES MORE THAN LRU1
LRU2 HAS TWICE THE FAILURE RATE OF LRU1
WITHOUT SPARES, THEIR CUSTOMER WAIT TIMES ARE SIMILAR
CONCLUSION:
DUE TO HALF THE FAILURE FREQUENCY AND EQUAL DOWN TIME PER FAILURE, LRU1 IS HALF AS IMPORTANT FOR REDUCING DOWN TIME
SINCE LRU1 COSTS 20 TIMES LESS, THE FIRST SPARE OF LRU1 YIELDS APPROXIMATELY 10 TIMES LESS COST PER UNIT REDUCTION OF SYSTEM DOWN TIME (1/2 X 20 = 10)
ALTHOUGH LRU2 FAILS MORE, IT IS LESS COST EFFECTIVE TO SPARE
IF THE CWT ASSOCIATED TO LRU2 WERE 10 TIMES GREATER THAN LRU1, THE SPARING COSTS PER REDUCTION IN SYSTEM DOWN TIME BECOMES APPROXIMATELY EQUAL
A simple sparing optimization example is provided to show the logic behind sparing to availability. For this example, let’s say the equipment has 2 serially configured LRUs. LRU2 cost 20 more times more than LRU1, but LRU2 has twice the failure rate of LRU1. Also, without spares, their Customer Wait Time (CWT) or down times are similar. Based on this given information, we can conclude the following.
Due to half the failure frequency and equal down time per failure, LRU1 is only half as important for reducing down time. Since LRU1 costs 20 times less, the first spare of LRU1 yields approximately 10 times less cost per unit reduction of system down time. Although LRU2 fails more, its first spare is less cost effective than the first spare of LRU1. If the CWT associated with LRU2 becomes 10 times greater than LRU1, the sparing costs per unit reduction of system down time becomes approximately equal. Since the MSRT associated with obtaining a spare decreases when a spare is stocked forward, the 1st spare of LRU2 will likely be more cost effective to spare than the 2nd spare of LRU1.

7. Sparing to Availability vs Demand Support Sparing
System Availability
Provisioning Model
Applied To All Items
This chart provides one type of example showing the need for sparing to availability modeling. Both the Army and industry have access to sparing to availability models, but unfortunately they are not used regularly.
Prior to using sparing to availability models, demand support criteria was frequently used by the Army. This type of sparing considered the number of demands expected per year at a support location, but does not consider the cost of spares. For a low deployment density system, the low number of demands yielded little sparing at the forward and intermediate levels of supply support. However, even with high deployment density systems that yield significant demand rates, more system availability can be obtained for the same amount of money and the system availability can also be cost effectively improved by using a sparing to availability model.
Demand Support
Sparing Computation
Stock Cost (Million $)

8. Sparing to Availability is Better than Sparing All Essential LRUs
Provisioning Model
Applied To All Items
One Each of All Essential
Items Spared at Each
Organizational Level
System Availability (%)
This chart provides another type of example showing why sparing to availability models are needed. On very low density equipment, there will generally be little to no spares using a demand support criteria. Therefore, a different non-quantitative supply scheme is often used today to get a reasonable Ao, which embodies “just-in-case” sparing. This scheme stocks one each of all essential LRU items forward. However, sparing to availability can yield more Ao for less money. With sparing to availability, the second spare of some low cost, high failure rate items can yield less cost per unit reduction of down time than the first spare of some high cost, low failure rate items. Therefore, as shown by the graph, more system availability can be obtained for less money using a sparing to availability model.
Stock Cost (Million $)

9. Multi-Echelon Sparing Optimization to Ao Requirement
Total Stock to
Achieve Ao Goal
Min
Cost
Total Second Echelon
Stockage
Sparing Cost
This chart shows how multi-echelon sparing optimization determines optimal stock availabilities to achieve an Ao requirement or goal. If a lot of items are put at the most forward retail level, less items are needed at the more centralized 2nd echelon support level to achieve the Ao goal. Conversely, if a lot of items are put at the 2nd echelon support level, less items are needed at each forward retail stock level to meet this same Ao goal.
When there are 2 or more support levels above the wholesale level, Sparing to availability models accomplishes a multi-echelon sparing optimization to determine the lowest or minimum total cost stockage recommendation that will achieve the Ao goal input. The wholesale level is not optimized because it is spared to achieve a specified stock availability based on expected demands over its repair or replenishment pipeline times. When there is only 1 retail support level above the wholesale level, the depot level may be modeled like a General Support Unit if a more cost effective multi-echelon sparing optimization solution is desired.
Total Forward Level
Stockage
A2 Goal
Stock Availability At Second Echelon Supply Level (A2)

10. SESAME Selected Essential-item Stock to Availability Method
Supply Chain Mission is to Support Operational Readiness & Performance
Emphasis on Budgeting & Stocking to Achieve System Ao Performance Goals at Least Cost
Decision Support Tool with Cost as a Major Factor in Sparing to Reduce Risk of Procuring Wrong Parts
Identifies Initial Provisioning Requirements Prior to Production
This slide introduces the Selected Essential-Item Stock for Availability Method (SESAME) model. Although industry and the Air Force have existing Sparing to Availability models that are potentially comparable, I have personal knowledge of the SESAME model, which is a very versatile tool. The Army Materiel Systems Analysis Activity (AMSAA) is the proponent of the SESAME model.
The supply chain mission is to support Operational Readiness and performance. SESAME emphasizes budgeting and stocking to achieve a system Operational Availability (Ao) performance goal at the least cost. SESAME is also a decision support tool using cost as a major factor in sparing to reduce the risk of procuring the wrong parts. Also, SESAME is an Army standard model that is used to identify the initial provisioning requirement for spares prior to production to determine what items should be placed at which support levels when fielding of the systems occur.
Army policy want to make supportability a co-equal to cost, schedule and performance when acquiring equipment. The push for reduced support costs in achieving Ao requirements or goals with Ao analysis models improves Army policy implementation. Therefore, on 15 August 2000, the Assistant Secretary of the Army for Acquisition, Logistics and Technology distributed a memorandum encouraging the use of SESAME for determining the provisioning of initial spares requirements.

11. SESAME Usefulness
Optimizes Multi-Echelon Retail Level Initial Sparing to Achieve End Item Ao Requirement or a Procurement $, Weight or Volume Goal
-OR-
Optimizes Plus Up Sparing to Achieve End Item Ao Given the Present Retail Level Sparing Mix
Evaluates End Item Ao Based on Sparing Mix, LRU Reliabilities and Logistics Response Times
Maintenance Concept for each Essential Item is Proposed or Known
This slide introduces the usefulness of the Selected Essential-Item Stock for Availability Method (SESAME) model. Prior to fielding, SESAME can optimize the multi-echelon retail mix of spares to achieve an end item Ao requirement or goal. SESAME can optionally be used to optimize sparing at retail levels to a procurement dollar, weight or volume goal. Therefore, it achieves the readiness goal at a minimum cost for LRU spares, or buys a maximum amount of readiness for a given initial provisioning budget, volume or weight input.
SESAME can also be used later after initial provisioning or after fielding to optimally plus up sparing to achieve the end item Ao or increase readiness rates if the present sparing mix is found to be inadequate to achieve the needed level of performance.
SESAME can also be used in an evaluation mode to estimate an end item’s Ao based on the proposed or known LRU sparing mix, each LRU’s logistics reliability and their logistics response times due to the order and shipment time and repair cycle times.
To use SESAME, the proposed or known maintenance concept for each essential item must be inputted in the model.

12. SESAME Execution Modes
Decision
Support
Tools
“How much should
I budget to meet
my AO target?”
Operational Performance Optimization:
Determine Least Cost Mix of Spares
that will meet Target AO
Budget Constraint Optimization:
Determine maximum AO that can be achieved
given a fixed spares budget
“How much AO
can I buy
with my budget?”
Evaluation:
Evaluate Stock Levels in terms of AO
- Existing stock in inventory
- Vendor Recommendation
“How good is the
contractor’s
recommendation?”
The Army Materiel Systems Analysis Agency (AMSAA) is the proponent for the SESAME model. This is their slide covering the SESAME Execution Modes to emphasize its flexibility as a decision support tool throughout the acquisition process after LRU level data becomes available.
The Operational Availability (Ao) performance optimization mode determines the least cost mix of spares and how much of a time phased budget is needed to meet the Ao target. The budget constraint optimization mode determines the maximum Ao that can be bought with the allocated spares budget. The evaluation mode determines either the Ao associated with a contractor’s sparing recommendation or the Ao of existing stock levels when actual logistics chain and demand data are obtained. The plus up mode determines whether the Ao target is met and will augment existing stock levels with an optimal increase in the sparing mix to meet the Ao target if it is not met.
Plus-Up:
Augment Existing Stock Levels
- to meet Target AO
- Optimal increase
“Given that my stock levels
won’t meet my AO target,
How should I augment them?”

13. SESAME Outputs Summary Data: Sparing for Each Unit at Each Echelon:
Ao vs. $ Graph and Table
Budget at Each Retail Support Echelon
Budget Requirement by Year
Initial Retail Support Spares
Depot Pipeline Spares
Consumption Spares
Sparing for Each Unit at Each Echelon:
Stock Quantities of Each Item
Item Cost Contributions
SESAME outputs are listed on this slide. As an end item level summary, there is a graphed line picturing the build up of the minimum total cost of spares needed at Org, DS and GS to attain different Ao levels until the Ao or dollar target level is reached. There is also a corresponding table of Ao and total sparing dollar values that depict internal steps homing in on the inputted target value. The initial spares budget requirement at each retail support echelon are also outputted. Outputs also display the budget requirement by year for initial retail spares at the ORG, DS and GS echelons, depot level pipeline spares to cover Repair Cycle Times, and consumption spares to cover time phased yearly replenishment quantities.
SESAME outputs the sparing requirements for each unit at each supporting echelon. Stock quantities of each item are determined and the item’s cost contribution to the sparing cost estimate is also shown.

14. End Item Level Inputs
Ao or $ Goal to Optimize -or- LRU Sparing Mix to Evaluate Ao
End Item MCTBF (Applies only when not computed by the addition of serially configured LRU failure rates)
Number of End Items Fielded Each Year for Each Forward Support Level (Org or DS Unit may be Lowest Level modeled)
Number of Lower Level Units Supported by Higher Level Unit
For 2 level supply, Org or DS level and GS Level do not apply
For 3 level supply, Org or GS Level do not apply
Number of Clones Each Year for Each Applicable Unit
Copies with same number of end items & Support Structure
Saves inputting repetitive information
Typically Contractor Input
Typically a Government Input
This slide contains the macro-level inputs of the SESAME model applicable to the end item. The model uses an Ao, dollar, volume or weight goal as the objective to achieve. If Ao is to be evaluated rather than optimized, then the LRU sparing mix at each applicable supply support level is needed.
The end item Mean Calendar Time Between Failures (MCTBF) can either be inputted or computed by the model summing up serially configured, critical LRU annual demand rates.
The number of end items at each forward supply support unit is needed. A tree diagram showing which lower level forward support units are supported by which higher level unit is needed. Clones represent the repeat of a similar quantities of end items with a similar support structure. Inputting the time phased quantity of clones reduces the input of repetitive information.
Most of the inputs on this slide are Government Furnished inputs with the exception of the contractor proposed sparing mix and the end item reliability if it is computed from LRU failure rates based on the equipment design.

15. Critical LRU Level Inputs
Average Maintenance Time Parameters
Time to Restore End Item if Spares in PLL, or ASL when no PLL
Repair Cycle Time (Retrograde Ship Time + Turnaround Time)
Average Supply System Parameters
Order & Ship Times to PLL and to ASL by Theater
Wholesale/Depot Level LRU Stock Availability
Time for Wholesale/Depot Level to Fill Backorders
Data Needed for Each LRU
Failure Factors (Annual Removals per 100 End Item)
Average Procurement Cost
Maintenance Concept (% Thrown Away & % Repaired Where)
Typically a Contractor Input
Typically a Government Input
Input may come from Government or Contractor
This slide contains logistics response times and critical Line Replaceable Unit (LRU) level data SESAME inputs. The Mean Time to Restore the end item per failure input covers the system’s Mean Time to Repair (MTTR) plus its Mean Restoral Delay Time (MRDT). The MRDT input and Retrograde Ship Time portion of the Repair Cycle Time are typically Government Furnished inputs.
Depending on who is performing maintenance and who is shipping LRU spares forward helps to determine whether the contractor or the Government will provide the rest of the logistics response time data. Supply system parameters covering Order and Ship Times, the Stock Availability at Depot and the Mean Time to Fill Backorders when the Depot is out of stock are SESAME inputs.
LRU level data is typically provided by the contractor. For each LRU used to restore the end item or SRU used to repair the LRU, their Failure Factors, Procurement Cost and Maintenance Concept inputs are needed. The Cost of the Next Higher Assembly is an input necessary for each SRU. When failure factors are based on experienced annual demand rates rather than reliability predictions, this data may be obtained from the stakeholder recording it.

16. Evaluation Plan with Supportability in Competitive Solicitations
COST/PRICE
TECHNICAL
SUPPORT-
ABILITY
PRAG
FACTORS
OP AVAIL*
MANAGEMENT
SUBFACTOR
CONTRACT
COSTS/PRICES
SPT IMPROVEMENT PLAN
DATA SHARING PLAN
This chart pictures one potential way to evaluate Operational Availability (Ao) in competitive solicitations where Contractor Logistics Support (CLS) and contractor sparing recommendations are expected to drive the Ao of the equipment being purchased. The Ao of the bidder’s proposed design and support are estimated by using the SESAME model.
This picture shows Supportability as a factor separate from the Technical factor, which gives Supportability a relative weighting to the other factors.
The evaluated Ao will result in an adjectival rating based on risk for attaining quantitative thresholds of Ao. The adjectival rating can be designated as outstanding, good, acceptable or not acceptable.
If required in the evaluation plan, each bidder’s plan offered for improving supportability or sharing data with the Government may also be evaluated separately under a management sub-factor. It can also be priced out separately if covered by optional data items while still in competition. This is recommended if it is desired to continue optimizing product support to Ao after contract award. Contractor data sharing of the Ao factors that they control is recommended if the Government wants to manage Ao.
TECHNICAL INPUT
RISK FACTORS
SUPPORT INPUT
RISK FACTORS
COST REALISM (if not Fixed Price)
* EVALUATION RESULTS IN AN ADJECTIVAL RATING FOR QUANTITATIVE THRESHOLDS

17. Optional Evaluation Plan with Supportability
COST/PRICE
TECHNICAL
PRAG
FACTORS
MANAGEMENT
SUBFACTOR
OP AVAIL*
CONTRACT
COSTS/PRICES
SPT IMPROVEMENT PLAN
DATA SHARING PLAN
This chart pictures a different potential way to evaluate Operational Availability (Ao) in competitive solicitations where contractor sparing recommendations are expected to drive the Ao of the equipment being purchased. The bidder Ao being proposed is again estimated by SESAME based on the bidder’s design, the failure rates associated with the items in the design, the sparing mix proposed by the bidder and the logistics response times within their control. Government logistics response times, system fielding data, and tree diagram support level data covering the Organizational Units, DS Units and GS Unit become the Government Furnished inputs to this evaluation.
This chart shows Ao as a sub-factor within the Technical factor. A sub-factor contributes to the rating of the Technical factor, which in turn is relatively weighted to the other factors. In this diagram, the evaluation of Ao contributes to the adjectival rating of the Technical factor making it a part of the Source Selection while still in competition, but getting less emphasis than being a factor. This plan was used on the Phoenix source selection evaluation, which is a super-high frequency tactical terminal.
COST REALISM (if not Fixed Price)
CONTRACTOR DESIGN INPUT FACTORS
SUPPORT INPUT
FACTORS FROM GOVERNMENT & CONTRACTOR
* EVALUATION RESULTS IN AN ADJECTIVAL RATING FOR QUANTITATIVE THRESHOLDS

18. COMPASS Usefulness
Optimizes Maintenance Concepts (Level of Repair Analysis) to Achieve an End Item Ao/Readiness Requirement at Lowest Total Support Cost
Compares Similar Maintenance Level Alternatives (Source of Repair Analysis) for Best Value
Evaluates Design Breakdown Impacts to RAM Related Logistics Support Costs
Supply Sparing Mix Optimization to End Item Ao is Embedded
This slide introduces the usefulness of the Computerized Optimization Model for Predicting and Analyzing Support Structures (COMPASS). COMPASS is a Logistics Support Optimization analysis tool and is considered an Army standard Level Of Repair Analysis (LORA) model that optimizes maintenance concepts to achieve an end item Operational Availability (Ao) requirement or goal at the lowest total support cost. A LORA determines where an item can be cost effectively repaired.
COMPASS may also be used as a Source Of Repair Analysis (SORA) model to determine how an item can be cost effectively repaired at the same maintenance level. One kind of SORA compares the type of test equipment that may be best applied.
Since COMPASS needs information about the Line Replaceable Units (LRUs) and high failure rate Shop Replaceable Units (SRUs) within the equipment, COMPASS has the capability and fidelity to evaluate item level impacts to Reliability, Availability and Maintainability (RAM) related logistics support costs.
COMPASS also contains an embedded supply sparing mix optimization to a system Ao goal that automatically optimizes the supply support for each alternative maintenance concept being analyzed. COMPASS is commonly used by the Logistics and Systems Analysis communities and the Army Materiel Command (AMC) Logistics Support Activity is the proponent of this model.

19. Maintenance (TMDE, etc.) Supply Support (Spares)
Model Objective
Model Objective
Maintenance (TMDE, etc.)
Supply Support (Spares)
The COMPASS is a level of repair analysis or LORA model. There are two important factors that must be considered when making repair level decisions. On one hand, there are the maintenance requirements, such as any test equipment or repairmen that may be needed to perform various repair functions. On the other hand, there are spares being provisioned to maintain the required level of Operational Availability (Ao). If either of these two factors is evaluated in isolation, the total cost associated with the solution will not be optimal. For example, if maintenance requirements are looked at alone, moving all repairs to a centralized, depot location will minimize the number of test sets that must be purchased and possibly reduce the number of maintenance personnel needed to be trained. On the other hand, the number of spares required to maintain the Ao with this longer pipeline may become quite expensive. If the goal is solely to minimize spares, repairs would be moved as far forward as possible. However, this could lead to a significant investment in test equipment and repair personnel needed to be placed at several maintenance locations.
Therefore, maintenance and supply support must both be simultaneously considered to come up with the optimal repair level decision.
Level of Repair Decisions
Source of Repair Decisions

20. Ao COMPASS Outputs Outputs Maintenance Policy
Where:
Org, Intermediate, Depot, Contractor, Discard
How:
ATE, Common TMDE, Special TMDE
Initial Provisioning
Net Present Value Costs
COMPASS generates three basic outputs. The first, and most important, is a set of maintenance policies for each of the items in the system. The model recommends the maintenance level at which each individual maintenance action should be performed. Another repair level possibility is no repair, which results in a discard at failure. COMPASS may also be used to select among three different repair methods for accomplishing each maintenance action. Determining the type of test equipment to use at a repair echelon is considered a Source of Repair Analysis. The next output lists the initial provisioning spares that should be purchased to support the recommended maintenance concept. Finally, the model sums up all of the net present value costs associated with the recommended supply and maintenance policy on an item level basis and the special test equipment and/or special repairman costs on an end item level basis.
The output concludes with the Operational Availability being achieved. However, this not just an output because the Ao target is an input to the model, which is used to drive all of the decisions. Thus, the maintenance concept recommended is the least cost way to achieving the target Ao considering both supply and maintenance requirements. Ao

21. Net Present Value Costs Estimated
Initial Provisioning
Consumption (Replenishment) Spares
Inventory Holding
Transportation (Shipping spares back and forth)
Requisition
Cataloging
Enter and maintain line on PLL/ASL
Common Labor
Screening
Documentation
Test Program Set Development & Maintenance
Contractor
Variable per repair costs
Fixed costs
Contact Team
Common Test Equipment
Special Test Equipment
Special Repairmen
This is a list of the costs that are covered by COMPASS because the Net Present Value comparison of all these costs that are relevant to the maintenance support decision. Those costs that would be the same for all potential maintenance concepts are not considered by COMPASS. For example, first destination transportation shipping costs are not included in COMPASS because this cost is the same under all maintenance alternatives. Similarly, operational costs such as batteries, Petroleum Oil & Lubrication (POL) and operator personnel are not included because they also do not impact the selection of the optimal maintenance concept.
Note that there are two different ways in which maintenance labor and test equipment costs can be calculated. Common labor costs uses a common, hourly labor rate at the unit accomplishing maintenance multiplied by average repair or screening times. The purchase and annual maintenance cost for common test equipment is prorated based on its hours of usage relative to the maintenance echelon’s available hours of usage potential. The other way uses special repairmen or special test equipment assigned to various repair functions dedicated to the system. Since special repairman and special test equipment are dedicated for use on the system, the full annual loaded salary (including training, benefits, etc) of the repairman and the development and maintenance cost of the test equipment are fully charged to the system.

22. End Item Level Inputs Ao Target & Maintenance Concept if not optimized
Total Number of Systems Fielded
Operating Hours per Year & MTBF if not computed
Support Structure
Number of Sites at Each Maintenance Level
Order and Ship Times to Each Retail Support Level
MTTR & Restoral Time if DS is forward supply
General Cost Parameters
Shipping
Cataloging, Bin, Inventory Holding Cost %
Typically a Contractor Input
Typically a Government Input
Input may come from Government or Contractor
End item level inputs to COMPASS include reliability, Operational Availability (Ao), supportability, and support cost data. The Ao target input drives the model’s maintenance and supply outcomes. However, if the contractor provides a maintenance concept, the final state Net Present Value support cost outcomes will become the focus of the COMPASS run.
The total number of systems fielded worldwide and the operating hours per year are Government Furnished inputs used to determine the expected total number of annual failures for each component. The end item Mean Time Between Failure (MTBF) is optionally inputted if the end item reliability is not going to be computed from each serially configured, critical LRU MTBF.
The support structure tells the model the average number of systems supported at each maintenance level and how long it takes to ship spares and repair parts when available in supply.
Finally, there are some general cost parameters that covers the transportation cost associated with shipping items between support unit locations and other supply related cost factors that the Government typically furnishes.

23. Critical Inputs LRU/SRU Level
LRU/SRU Level Inputs
Hardware
Failure Rate
False Pull Rate
Repair & Screening to Replenish Stock
Turnaround Time
Labor Time
Labor Rate
Contractor Repair if Repair & Return Used
Setup Costs
Response Time
Typically a Contractor Input
Typically a Government Input
Input may come from Government or Contractor
Unit Price
Washout Rate
Material Cost
Support Equipment/TPS
Tech Manual Cost
Cost per Repair
Cost per False Pull
Item level COMPASS inputs apply to each LRU and high failure rate SRU in the end item. They include reliability, maintenance repair and replenishment cost data. First, item level hardware data is needed to show how often an item fails, how often it is it removed when it did not fail, how much it costs, and the percentage of time the item is washed out because it can no longer be repaired.
For repair and screening to replenish stock, the Depot repair alternative in COMPASS is used. Inputs are needed to show how long it will take to repair including the waiting time, how much touch labor time is required, the touch labor rate, the average cost for the repair parts and material used, whether any special test equipment is required and the costs associated with support equipment and repair manual documentation to accomplish repair.
The contractor repair option in COMPASS has its cost estimates based on repair and return maintenance actions of the same serial numbered item. If the contractor, like the Government, ships depot level stock to fill orders on reparable items, then the contractor depot needs to be modeled just like organic depot level repair. When the contractor repair alternative in COMPASS is used, additional inputs are required. The inputs include the up front investment cost for set up, how long it takes the contractor to repair and return the item to the customer, and how much the contractor will charge the Government per repair action or per screening action for each item sent back to the contractor.

24. Equipment Breakdown Equipment Breakdown End Item LRU1 LRU2 SRU1 SRU2
COMPASS breaks the system or end item down in a hierarchical structure. The end item is broken into the LRUs, and then the SRUs that make up each LRU are defined.
COMPASS actually works and provides recommendations by failure mode. A failure mode is essentially an LRU-SRU combination that tells the model how the end item is put together. Using failure modes enables the model to recommend a different way to repair an LRU depending on what goes wrong with it. For example, if an older home stereo system is thought of as an end item, a turntable may be considered LRU. If the cartridge, SRU1 fails, we can fix the turntable ourselves in our home. If the motor, SRU2 fails, we must take the whole LRU to a repair shop where they have more expertise and special tools.
Note that SRU3 is in both of the LRUs in this pictured diagram. COMPASS has the ability to correctly account for this commonality. Treating SRU3 as if were two different items could cause incorrect results. Suppose, for example, that 100 repairs of SRU3 are needed to justify the establishment of a repair capability. If there were 50 failures in LRU1 and 60 failures in LRU2, neither would justify the investment to accomplish repairs on its own merit. However, taken together, the 110 failures would suggest the establishment of the repair capability.
Failure Mode 1: LRU1 SRU1
Failure Mode 2: LRU1 SRU2
Failure Mode 3: LRU1 SRU3
Failure Mode 4: LRU2 SRU3
Failure Mode 5: LRU2 SRU4

25. Use of Models Optimizing to Ao Requirements/Goals
SOURCE SELECTION EVAL WITH LRU DATA
OPTIMUM SUPPORT PRIOR TO FIELDING
RAM REQUIREMENTS EVALUATION
FIELD OR TEST DATA EVALUATION
This matrix is a picture summary of when to use models that optimize to an operational availability (Ao) requirement or goal. ASOAR can be used early enough in the acquisition cycle for Reliability, Availability and Maintainability (RAM) requirements evaluation. If LRU sparing is proposed, SESAME can be used in source selections to evaluate the Ao proposed. If LRU and high failure rate SRU maintenance policies are proposed, COMPASS can be used in source selection evaluations to determine RAM impacted supportability costs. COMPASS and SESAME are useful in acquisitions to determine the optimum maintenance or supply support concepts during systems engineering prior to fielding. After test data or demand data is obtained, SESAME may also be used to estimate the end item’s Ao determined from equipment testing or optimally plus up sparing based on the experience data after fielding.
- Applicable Tool Supplemental Tool