Readiness-Based Sparing (RBS)

Background

RBS has been part of Department practice since the 1960’s, when it was used to optimize aircraft availability, and was incorporated into the now-cancelled DoD Supply Chain Materiel Management Regulation (DoD 4140.1-R) as the preferred method for calculating inventory levels. It is now an integral part of DoD Manual 4140.01, most particularly Volume 2.  This manual starts by stating that "To determine the inventory investment required for the fielding of a new weapon system, the DoD Components will use RBS methods, where feasible." It then goes on to outline detailed procedures for computing RBS inventory requirements.

Historically, DoD had used demand-based modeling to determine the appropriate consumer level of spares required to maintain key equipment/weapon systems. Success was measured by fill rate (expected percent of requirements filled), or protection level (expected percent of time without an unfilled requirement). Under the evolution to RBS the cost, readiness, reliability, maintainability and requisition response time for parts not available are considered to determine consumer or retail sparing requirements.

RBS on a System Basis

RBS models recommend parts on a system basis, according to which items provide the greatest contribution to a system’s availability for use per dollar spent. The system approach:

• Links item to system performance
• Presents a range of possible solutions
• Optimizes spares mix giving the most efficient performance per dollar

Alternatively, demand-based sparing models recommend parts on an item by item basis according to historical demand. Equipment readiness and investment are uncontrolled outputs.

Enhancing Business Capability

RBS is a business process and decision support system that provides the capability to achieve specified weapon system Ao, Fully Mission Capable (FMC), or Operational Readiness (OR) goals, and minimize investment in spares inventories. It can also maximize readiness at a fixed cost.

RBS focuses on a requirements determination process that computes the levels of secondary item spares needed to support a weapon system readiness goal at the lowest possible cost. RBS algorithms determine, for each inventory location (supply and maintenance), the lowest cost spares mix that will provide the required operational readiness level for a weapon system.

RBS models are to be used whenever possible to assess inventory investment required for fielding new programs (i.e., weapon system or subsystem) and to set sparing levels for secondary items that have support goals related to weapon system readiness. In addition to these primary objectives, RBS analytical capabilities can be used to negotiate performance-based supplier agreements; assess the effect of reliability, maintainability, and supportability improvements on weapon system readiness; plan and develop budgets; and conduct what-if exercises related to deployments. Defense contractors engaged in performance-based supplier agreements have for many years used either internally-developed or commercial-of-the-shelf (COTS) RBS models to achieve performance goals within cost boundaries.

Service Initiatives

The individual Services have implemented RBS in various ways over the past three decades. As an example, the Air Force applies RBS methodologies to inventory requirements (initial, replenishment, and contingency planning), stock leveling, depot-level and Centralized Repair Facility (CRA) repair prioritization, and asset distribution prioritization. The Navy, on the other hand, only uses RBS modeling for inventory requirements.

Under the DoD's Supply Chain Integration (SCI) efforts, the Office of the Assistant Secretary of Defense for Sustainment (OASD(S)) and the Services are seeking ways to standardize the approach to RBS and to make the various models used interoperable. The Services and Defense Logistics Agency (DLA) have, in the past, agreed to work together to implement CCOTS-based RBS models.  However, in recent years the Services have implemented other approaches, for example, the Naval Supply Systems Command (NAVSUP) has worked with the Naval Postgraduate School to develop a readiness based sparing model and are using it to develop most of their requirements.

As each of the Services and DLA advance their efforts towards achieving the benefits of an individualized yet interoperable RBS environment across DoD, the SCI organization within the OASD(S) office for SCI is providing research and development funding for those projects which align with the overall program objectives and goals. The following RBS projects have been supported by ODASD(Logistics) Supply Directorate:

• The Air Force explored capabilities and applicability of Click Commerce - a type of ecommerce software - and their Advanced Inventory Optimization (AIO) model, which was the RBS logic embedded in a proposed Air Force Enterprise Resource Planning (ERP) solution. To drive greater joint requirements planning, they developed a “Meta-model” concept to support coordinated inventory management for common items and analyzing potential benefits. The proposed approach was implemented by the Services. The Air Force is currently evaluating two models that employ inventory strategies for low demand or non-forecastable depot-level reparable items, Peak Policy and Next Gen, which have demonstrated improvements over traditional forecast-based models for DLA -managed consumable items.
• The Army evaluated MCA Solutions’ Service Planning and Optimization (SPO) software, comparing the results of its RBS calculations to the results of their existing legacy software tool, Selected Essential Item Stock for Availability Method (SESAME). The Army is also currently evaluating the Peak Policy and Next Gen models.
• The Navy has worked with the Naval Postgraduate School (NPS) to develop a new Naval Aviation RBS model (NAVARM).  It has been implemented for most requirements. NAVARM is a single-echelon multi-indenture RBS model that uses measured wholesale delay times to build retail requirements based on existing wholesale support levels.  The model is early in its development, but multi-echelon capabilities will be added in the future. It has been implemented in both pre-and post-MSD requirements determination.  OPNAV Instruction 4442.5B, Readiness Based Sparing, provides considerable detail regarding implementation of RBS within the Navy.

• ---

  The DLA has been developing a solution to optimize inventory levels to support retail initiatives. They initially utilized JDA’s Inventory Policy Optimization (IPO) solution to replace legacy computation models, which calculated safety stock at the wholesale item level and could not be used for planning retail levels. They have since augmented IPO with the Peak Policy and Next Gen models.