As part of the service’s growing emphasis on information, the Navy has mined its maintenance and repair databases and tried to determine “where we think single points of failure might occur.”
Once the service identified those systems, it “pre-positioned” parts within the ship and within the ready group so Navy teams might accelerate their ability to repair systems most likely to break down, if or when ships run into trouble.
Navy has for years applied predictive maintenance practices within individual programs. For example, the service considered machine learning tools to help learn when to repair radars but now work is underway to adjust how they “provision” for the systems while at sea.
“We are shifting our perspective on the strategic value of condition data. We were always interested in individual areas of how we use condition data, but now, as we look at our advances to collect, store and process that data, we’re now at an inflection point.”
Predictive maintenance is the idea of identifying system failures before they happen with condition monitor and to then repair those systems before they break. The idea has quickly gained traction within Navy in recent years as a way to save time and money as well as to improve which aircraft, ships or vehicles may be available on any given day.
Other services are also considering employing artificial intelligence and machine-learning technology as a way to take advantage of the maintenance approach.
For example. Marine Corps Trucks have been trained how to diagnose worn-out parts put in order for replacement and get 3D Print part delivered to installation locations.
Marines equipped about 20 military vehicles, including 7-ton Medium Tactical Vehicle Replacements MTVR and massive Logistics Vehicle System Replacements LVSR tractor trailers, with engine sensors designed to anticipate and identify key parts failures.
It’s a commercially available technology that some industry vehicles already use, but it’s a new capability for Marine Corps trucks. Testing on those sensors will wrap-up soon and the service is going to assess how accurately and thoroughly the sensors captured and transmitted maintenance data.
If all goes well, the Marines then will work to connect the sensors with an automatic system that can order parts that will then be 3D printed on demand and delivered to the vehicle’s unit.
“How do we use that data and how do we link that back to our fabrication or supply network to make the system operate without a person in the loop, so make sure we’re doing push logistics versus pull logistics.
“Now we have the part there waiting when the vehicle gets back in from the convoy, or it’s already there a week in advance before we know we need to change it out. So that’s the concept and that’s what we’re going to try to prove with that.”
Marines want to bypass maintenance supply chains that sometimes have gear traveling thousands of miles to get to a unit downrange, and inefficient logistics systems that create lag while maintainers wait for parts to arrive.
“If we had the ability to print a part far forward, which we have that capability, that reduces your order-to-ship time. And you then combine that with what we call sense-and-respond logistics, or smart logistics, which is … it can tell you with a predictive capability that this part is going to fail in the next 20 hours or the next ten hours.
The goal of having trucks that can do everything but self-install repair parts is in keeping with the Marine Corps’ newfound interest in innovative technology.
Marines recently became the first military service to send 3D printers to combat zones with conventional troops, so that maintainers could print everything from 81mm mortar parts to pieces of radios in hours, instead of waiting days or longer for factory-made parts to arrive.
It's time for the Marine Corps to cash in on technologies that industry is already using to advantage.
Here we provide an overall Condition-based Maintenance CBM Business Case Approach BCA process, common set of cost elements, measures of effectiveness, a notional BCA framework, and factors to consider when assessing and subsequently conducting a CBM BCA to shape an understanding of the areas that CBM capabilities might benefit a program/system, in order to support a go/no‐go decision and subsequent investment decisions with justifiable information.
CBM is the application and integration of appropriate processes, technologies, and knowledge-based capabilities to improve the reliability and maintenance effectiveness of DoD systems and components. At its core, CBM is maintenance performed based on evidence of need and other enabling processes and technologies.
CBM uses a systems engineering approach to collect data, enable analysis, and support the decision‐making processes for system acquisition, operations, and sustainment. In evaluating potential CBM capabilities, whether they are technologies, maintenance processes, or information/data knowledge applications, a BCA needs to address these areas in a comprehensive and consistent manner, particularly when an incremental acquisition or fielding strategy is being considered.
Although the basic concept and purpose of BCAs are generally understood throughout DoD, many interpretations exist regarding assessment of CBM capabilities to ensure appropriate and accurate considerations are given to CBM capabilities, costs, and benefits.
So, what is a BCA? A BCA is a decision support approach that identifies alternatives and presents convincing business, economic, risk, and technical arguments for selection and implementation to achieve stated organisational objectives/imperatives.
A BCA does not replace the judgment of a decision maker, but rather provides an analytic and uniform foundation upon which sound investment decisions can be made. The subject of a BCA may include any significant investment decision that leadership is contemplating.
For example, a BCA may be used to substantiate the case to invest in a new weapons system, but not at the same level as a Capabilities Based Assessment; transform business operations; develop a web‐based training curriculum; or retire an asset.
In general, BCAs are designed to answer the following question: What are the likely operational/business consequences if we execute this investment decision or this action? The possibility exists that any projected savings or cost reductions identified in the BCA could be viewed as an asset available for reallocation in the budgeting process.
In evaluating the potential application of a CBM capability, it is important to understand the desired end state from a CBM metrics perspective and key assumptions that may impact the system or CBM capability.
Must define the need for a BCA, understand and define the problem, and define the desired end state. This approach focuses on As‐Is system trends, evaluating Measures of Effectiveness and their cost drivers, key CBM metrics, determining if CBM is a viable solution and if so, what CBM capabilities are applicable, and then defining feasible solutions.
Here we present some general questions and guidance that may relate to your CBM initiative. Answers to these questions are provided as information and an approach to support CBM implementation. As you plan your CBM BCA, the questions may assist in framing your general approach and strategy and ensure your CBM BCA is adequately defined and scoped to address key CBM business areas.
1. What is the projected impact on system/component level replacement frequency?
2. Are there any contract alternatives/strategies impacting cost/schedule?
3. What is the projected impact on system/component level replacement frequency?
4. Are there any alternatives contract strategies impacting cost/schedule?
5. What cost, schedule, and performance risk is projected based on proposed technology for procurement, implementation, and sustainment?
6. What maintenance tasks or functions can be eliminated or reduced?
7.How can data analysis and decision making be automated to reduce support costs?
8. What data needs to be collected to measure the costs/benefits of the CBM?
9. What are the data sources and limitations for the data that needs to be collected?
10. Does the CBM initiative improve our ability to assess schedule/cost?
11. Does the CBM initiative improve our ability to modify/improve current systems?
12. Does the CBM initiative improve our ability to design new systems?
13. What is the impact on total life cycle cost, including disposal?
14. How will this CBM system/subsystem affect operator usability?
15. Are there incremental performance levels?
16. What changes will be required for operator and maintenance personnel?
17. What changes will be required for functional systems?
18. What are the identity-specific metrics critical to support customer expectations?
19. How will the repair/replace decision be affected?
20. Will the system/equipment modernisation plan be affected and if so how?
21. How will the CBM capability impact integration with other systems?
23. How will the CBM capability impact service life margins?
24. What is the impact on Maintenance Down Time?
25. Will the system/equipment modernisation plan be affected and if so how?
26. What are the system/sub‐system and/or components project CBM capability?
27. What systems will have a direct/ indirect affect on a planned modernisation improvement?
28. How will the CBM capability impact service life margins?
29. How will platform performance monitoring affect system performance?
30. Does the system provide any increased prognostic/diagnostic capability?
31. What risks are to be considered because of association with source data, data transfer, and systems processing data?
32. What effect does CBM capability have on available combat power?
33. What effect does the CBM capability have on system readiness?
34. What parts supply system processes are affected and specific metrics to be used for the analysis?
35. What are impacts to be assessed using operational availability, material readiness metrics?
36. What are Impacts to be assessed using total ownership cost, and mean downtime metrics?
37. What is the associated cost/risk of each course of action?
38. What maintenance and acquisition processes will be affected?
39. How will maintenance and acquisition processes be impacted in terms of data collection/transmission?
40. How will maintenance and acquisition processes impacted in terms of manpower costs associated with analysis and decision making?
41. How is ability to execute and implement alternatives addressed in the risk assessment and sensitivity analysis?
42. What functions, tasks, and activities for maintenance, acquisition, and logistics processes must be identified?
43. How will readiness, availability, ready for tasking, down time for parts or maintenance and/or unscheduled down time be affected?
44. How does this initiative improve the overall awareness of equipment condition at the tactical levels?
44. How does this initiative improve the overall awareness of equipment condition at strategic levels?
45. How does this initiative increase the accuracy in failure prediction and situational awareness?
46. How does this initiative increase the accuracy in failure prediction and situational awareness?
47. What specific system/sub‐system/component and existing performance levels failure rate, etc. is the CBM capability is targeted to support?
48 . What are the CBM functionality areas of fault detection, isolation and prediction?
49 . What are the CBM functionality areas of reporting, assessment, analysis, decision‐support execution and recovery?
50. How to define scope of the BCA ensure diagnostic/trending data is used to establish system/component maintenance/replacement baseline to assess CBM capability cost/benefit?