Clockwork Solutions, Ltd.

Clockwork Solutions is a leading provider of reliability-centered, total-life-cycle Performance Prediction and Risk Management solutions for capital-intensive major industrial companies, military organizations and government agencies throughout the world.

Our proprietary technology and predictive modeling & simulation platform SPAR™, simulates the behaviors of complex systems forward in time, exposing effects of equipment aging, maintenance policies, and operating scenarios.

We provide unique and valuable solutions in the areas of Total-Life-Cycle System Management, Performance Based Logistics, Condition Based Maintenance and Service Parts Planning & Optimization. These solutions allow our customers to relate cause to effect, quantify risks to performance, and guide capital investments and major operations & maintenance improvements.

The defense industry represents one of the most, if not the most, complex system of systems in existence today. Aircraft, missiles, ground vehicles, ships, C4I equipment and numerous leading edge technologies must perform to their designed functions, and interoperate together, so that the end mission objectives can be met. Designing and sustaining such complex systems to achieve desired readiness and performance in cost-effective ways is too complex to deal with by trial-and-error.

Life cycle modeling and simulation provides an affordable test bed for assessing risks that may otherwise go undiscovered. It is essential to understand, however, that all models are not created equal and accuracy of predictions depends strongly on the level of complexity that can be accommodated. Clockwork Solutions’ simulation software SPAR is a best-in-class COTS product that deals with complexities inherent in Defense systems and consequently provides high fidelity, actionable results for use in various planning stages.

Prior generations of modeling capability were perhaps adequate in earlier times of less budget pressure, comparatively static mission goals, and slower technology evolution.

Today, the evolving global mission, rapid response deployments, intra service interoperability and overall support requirements that are driving system design and support concepts are also pushing the envelope of analytical software capabilities needed to support wise and cost effective decisions for these new concepts. The interrelationships are so complex that simulation-based approaches are the only means for generating high fidelity models and representations of reality. Services and programs have attempted to design and develop organic simulation-based capabilities, however, SPAR has features which in independent comparisons have been judged far superior.

SPAR and SPAR-based applications have a strong track record in supporting performance/support/cost trade studies in new system design, as well as legacy systems modification, upgrade, reconditioning and replacement.

Modeling supports acquisition and related strategic planning by addressing issues having the greatest long term cost and performance impacts. The goal at this point in the life cycle is to converge on a combination of design, supportability, and costs that achieves program and mission goals from both performance and price perspectives.

Model inputs at this point are design specifics (reliability block diagrams), characterizations of possible missions and design constraints, reliability data, estimated repair process capacity and cycle times, repair crew sizes, etc.

Outputs include quantitative predictions (time dependent, mean, variability measures, etc.) for:

• Mission availability and effectiveness for highly dynamic missions characterized by stochastic initiation times, durations, and/or other parameters.

• Cost vs. benefit for design alternatives characterized by different levels of redundancy in the design.

• Manning requirements driven by nature and frequency of various failure modes, decisions about number, skills and locations of onboard and/or other repair personnel

• Spare parts requirements as determined by inherent reliability of potential components, location, criticality, re-supply options, and repair infrastructure capacity.

• Impacts of different re-supply alternatives and combinations of alternatives such as on-board spares, VERTREP, UNREP, lateral re-supply (peer to peer), etc.

• Risk profiles for any or all performance metrics

• Risk/reward tradeoffs and warranty parameter evaluation for CLS scenarios.

• Initial provisioning

At this point in system life, the design, support processes, and manning requirements are defined. This is not to say that such characteristics do not evolve and improve. Rather, the focus shifts to supporting already operational assets. Results needed here include:

• Spares buy plans (over time) tailored to the combined needs of operating assets and new ship procurement schedules.

• Maintenance optimization and refinement based on actual operating performance data.

• Repair facility and manpower loading predictions for budget planning.

• Management of component aging and technology refresh

• Leveraging of onboard condition monitoring and prognostics to drive reliability improvements and maintenance planning.

• Reliability growth

• Program management analysis tailored to budget constraints and op tempo.

This capability provides decision support related to near term deployment and operations planning. The same information and modeling infrastructure that serves for acquisition and sustainment planning is an enabler of a tactical planning capability. The goal is to assure mission success while minimizing support costs, and logistics pipelines and footprints. The primary difference between this application and the prior two is that goals are related to a much shorter planning horizon.

Additional inputs are required and include actual (as opposed to design basis) deployment and operations plans, latest operational state data for end item equipment, spares, crews, munitions, etc., supply chain status, etc. Some of this information is acquired from condition and prognostics monitoring aboard the deploying equipment and systems. Other requisite information that can be applied comes from supply system and personnel databases.

• This capability functions in near-real-time to generate results like recommended load/configurations for modules given prior history.

• It generates recommended forward provisioning plans based on specific deployment timing, mission locations, and mission types.

• It must have an ability to explore “what-if’s” including alternative deployment strategies, battle plans, and support alternatives and quantify their impacts on mission goals and costs.

• Examples of questions such a system can help answer:

• A FMS is returning to a supporting carrier to load a logistics module. The operations commander needs to know how best (optimally) to provision the module to support 4 FMS sister ships for operational situations expected to develop over the next few weeks.

• What retrograde flow of repairable items and associated transport requirements can be expected over an upcoming deployment?

Such comprehensive modeling may require more modeling effort than management is accustomed to, and can increase the levels of data inputs typically required. However, a structure and phased approach to comprehensive modeling of REALITY begins by using data that is available, then defines what must become available, incorporates the increased data fidelity as it is obtained, and ultimately provides a basis for continuous process improvement in terms of capability to assess and control future risk to performance over the systems life cycle.