This appendix presents a set of proposed policies and supporting criteria for establishing Army and joint requirements to better contain and improve predictability for operational and sustainment (O&S) costs consistent with better buying power mandates from the Office of the Secretary of Defense. Sustainment readiness levels (SRLs), loosely analogous to technology readiness levels for the front end of the acquisition life cycle, are characterized here. Each SRL policy and its associated criteria can be applied to either currently fielded systems or fleets operating in the sustainment phase of their life cycles, or to new systems under development in the acquisition phase. In both cases, insight into cost-wise readiness and affordability issues can be obtained. Future O&S costs (and cost growth), and the ability to credibly relate current budgets to near-term readiness and programs to future capabilities, can likely be illuminated by addressing the degree to which these policies are in use or planned. For fielded systems currently operating in the sustainment portion of their lifecycles, empirical results for the sustainment policies described herein can be assessed; that is, either the policy is being implemented with measurable effect within a cost-performance (resources versus readiness) trade space, or it is not. For new systems, which have historically ignored the cost implications of the O&S sustainment phase, even though they typically constitute more than 70 percent of total life cycle costs, these criteria could be used as a planning checklist in conjunction with technology readiness levels. SRLs provide a means for Department of Defense (DoD) and Army officials, program managers, and materiel management centers to formally address and focus attention on the long-standing need, first identified by the Government Accountability Office on its high-risk list of federal agency shortcomings in 1990, to correct persistent problems in supply chain management, including deficiencies in demand forecasting, inventory policy, and strategic resource planning.
In the case of Army aviation, cost savings derived from implementing each of these supply chain policies have been estimated to be on the order of many multiples of $100 million (Parlier, 2010). This proposed sustainment concept supports development of readiness-driven supply networks and forms the analytical foundation for pursuing and achieving cost-wise readiness. Once fully implemented, the combined and interacting effects of these SRL policies are likely to be in the range of many billions of dollars, resulting in savings several orders of magnitude greater than the cost of implementation (Parlier, 2010). Using Class IX (spares and repair parts for major end items) as an example, proposed SRL policies follow. Assessment tools, or levels of maturity, for each SRL are also suggested.
1. Condition-based maintenance (CBM). CBM capitalizes on prognostic, sensor-based technologies (e.g., digital signal collectors) to determine the remaining useful life for expensive depot-level reparables (DLRs) and line-replaceable units (LRUs). CBM is intended to preclude collateral damage and lengthy repairs caused by catastrophic DLR failure, while also ensuring that these expensive items are not prematurely replaced based on arbitrary time-based maintenance policies. Connecting CBM to the supply chain enables DLR replacement to be anticipated—scheduled in advance (rather than needing unscheduled maintenance due to failure)—reducing the requirement for forward stocking. This anticipatory maintenance policy significantly reduces the overall fleet-wide requirement objective for those DLRs that are CBM-enabled by reducing tactical unit customer demand uncertainty and by capitalizing on risk-pooling. Possible assessment tools include the following:
• Does the supported end item (e.g., aircraft fleet) consist of DLR and/or LRU components or assemblies that have condition monitoring sensors (e.g., digital signal collectors) to support a CBM program?
• If so, how is remaining useful life prognostic information used to connect CBM to the supply chain for anticipatory supply support; what algorithms or processes are used, and what impact does this ability have on operational readiness, DLR and LRU positioning and distribution, materiel availability, and aggregate requirement objective reduction across the supply support enterprise?
2. Mission-based forecasting. This is a forecasting concept and method that identifies, differentiates, and uses empirical spare and repair part consumption patterns associated with different tactical missions and the operational environments within which they are conducted. Possible assessment tools include the following:
• Is customer demand accurately captured at the point of readiness generation (tactical units), and, if so, how?
• What are the explanatory factors (e.g., different operational missions, operating environmental conditions, etc.) that cause different customer demand patterns for spare and repair parts?
• How is this knowledge incorporated into demand forecasts for retail stock planning methods; specifically, how is supply aligned to real customer demand at the point of sale where readiness is generated (i.e., the tactical unit)?
3. Intermittent demand. It is often difficult to determine any pattern or trend that would otherwise enable accurate demand forecasts for spare and repair parts to be developed. This is especially true for infrequently used parts in complex systems—referred to as sporadic or intermittent demand. Standard traditional forecasting methods—typically time-based (e.g., exponential smoothing and its derivatives)—do not yield good results when applied to intermittent demands, although their use has been pervasive. New methods have shown great promise, although they have not yet been incorporated into current DoD enterprise resource-planning systems. Possible assessment tools include the following:
• Are there empirical spare or repair part usage profiles that are not amenable to standard forecasting methods, and can these non-standard patterns be characterized as intermittent?
• For those categorized as intermittent, what forecasting method is used for demand planning? Describe the metric used to assess forecast error and what forecast accuracy is achieved with this metric.
4. Sparing to availability. Inventory optimization policies, including readiness-based sparing (RBS) methods, trade off the cost of parts, contribution to readiness (criticality), and frequency of use to achieve a desired operational availability objective at minimum cost. Possible assessment tools include the following:
• Where are the costs for current and planned operational performance (e.g., readiness) and associated retail (tactical)-level spare parts and repair parts incurred? Portray graphically within a cost-performance trade space.
• How does this performance compare to a cost-effective, efficient operating curve? Describe. How has this efficient operating curve been developed or computed, and what inventory policy has been incorporated? Explain.
These sustainment maturity model polices, with their associated SRLs, could be combined as integration opportunities to pursue cost-wise readiness outcomes. For example, combining RBS and MBF can provide a means for aligning and synchronizing Class IX logistics support consistent with the force management process, mission requirements, and also regionally aligned forces.
5. Readiness responsive retrograde. Unlike consumable repair parts, DLRs are not consumed in the readiness generation process. Rather, a DLR is removed; replaced by a serviceable one; returned through the reverse pipeline for inspection, rebuilding, and modification, as needed by organic depots and commercial repair facilities; then returned back to the forward supply chain for distribution and re-use. This process forms a closed-loop supply chain—a feedback loop in terms of system dynamics. The responsiveness of this retrograde process impacts the output of the system (readiness) as well as the aggregate requirement objective for these DLRs. Possible assessment tools include the following:
• What is the retrograde (reverse pipeline) structure, existing and anticipated materiel availability, and what is the impact of retrograde performance on the aggregate end item requirement objective? Describe.
• What reverse logistics metrics are used to assess responsiveness, efficiency, and effectiveness, and how is the retrograde process synchronized with depot repair?
6. Multi-echelon RBS. This is a centralized, risk-pooling inventory optimization method used in large-scale, multistage supply distribution systems. It incorporates single-stage RBS elements along with transportation costs and times to optimize the placement and distribution of spare and repair parts within a supply support network. Possible assessment tools include the following:
• What is the organizational structure of the supply chain? Describe.
• If multiple stages (organizational echelons beyond the retail level) exist for supply distribution and/or maintenance support, what multi-echelon inventory optimization method, if any, is used for inventory distribution allocation across the supply support network? Explain.
7. Sustainment early warning system. The Defense Readiness Reporting System requires the services to report not only the current and expected near-term readiness of tactical units in the field, but also the readiness of their respective Title X institutional support capacities as well (e.g., man, organize, train, equip, sustain). Leading indicators are needed to anticipate, diagnose, and then preempt potential supply chain failures. Analytically based decision support systems can significantly contribute toward this DoD mandate by linking planning, programming, budgeting, and execution (i.e., a resource planning system) to operational planning systems (i.e., capabilities). Informed by this supply chain health monitoring and management concept, planning guidance, funding decisions, and execution performance can then be related in meaningful ways (Parlier, 2010). Possible assessment tools include the following:
• Has a materiel enterprise supply support early warning system been established; and, if so, how is it characterized?
• Are the predictive analytics used for early warning credible and useful to management?
The potential magnitude for improvement by adopting a sustainment early warning concept is truly dramatic—tens of billions of dollars in further savings are likely, and more importantly, it becomes possible to actually achieve predictive readiness by credibly and accurately relating investment levels to current readiness and future capabilities. Collectively, these SRLs can yield a more effective, resilient, and increasingly more efficient materiel support enterprise that achieves equipment readiness goals, is adaptive to change, and has greater materiel availability at less cost.
REFERENCE
Parlier, G.H. 2010. Transforming U.S. Army Supply Chains: An Analytical Architecture for Management Innovation. Pp. 69-96 in The Supply Chain in Manufacturing, Distribution, and Transportation Modeling, Optimization, and Applications, edited by V.M. Miori. Boca Raton, Fla.: CRC Press, Taylor & Francis Group, Auerbach Publications.
