Authors: Edward Walsh and Richard Lee
Abstract
The political environment and competing budgetary priorities will likely mean that there will be capability shortfalls. This context partially underlied the Department of Defense’s (DoD) Better Buying Power initiatives. Practices to ensure efficiency and effectiveness are necessary to stretch a finite amount of dollars to minimize capability gaps and ensure the timely deployment of technically superior forces.
One particular method for improving affordability for large, complex assets is the use of “modularization.” The process breaks down the complexity of a single product line into smaller, more manageable building units. Modularization allows for the building of the pieces or modules with more fit-out and testing than the traditional “straight stick” approach. The result is shorter construction spans, reduced overhead costs, and lower direct costs from favorable learning effects. The approach is utilized in commercial and military shipbuilding. Within the Navy, the process has been applied with great success in the construction of nuclear submarines as well as on surface ships, such as nuclear carriers, Littoral Combat Ships, and destroyers.
The mission to accelerate acquisition programs potentially injects greater risk across a program’s entire life cycle. Improving the “speed to market” is necessary to ensure technical superiority, but limits the margin for “known unknowns” and “unknown
unknowns” in the development and fielding of complex weapon systems. Hence, managing risk has always been a crucial element in program management, but it will be increasingly so given the capability demands, delivery needs, and budget constraints.
Given this backdrop, this presentation expands on the research completed in the 2015 ICEAA presentation, “Manufacturing Assembly Plan (MAP) Tool: Bridging the Gap between performance and the Construction Process” with a focus on a theoretical approach for assessing a program’s risk by module. The theoretical risk approach enables analysis to be performed on (1) a micro-level, a single ship, and (2) on a macro-level, a shipyard. A statistical approach can be employed to develop risk bounds by each module, accounting for the dynamic nature of risk; as a project progresses, there are less unknowns and thus less risk. Work occurs in time and place; applying risk associated with an entire ship may overstate or understate the risk at any given point in time.
More accurate risk analysis informs shipyard workload assumptions, informing labor rates and, subsequently, price. By comparing the price associated with a risk-adjusted labor hour position to the baseline budget, program managers or senior decision makers are armed with better information to make trade-offs, reallocating resources between products or product lines.
Current Environment
Background:
Since 2018, Under Secretary of Defense for Acquisition and Sustainment focused on making the military’s acquisition system one that “moves at the speed of relevance”
Problem Statement:
Push to accelerate acquisition programs potentially injects greater risk across a program’s entire life-cycle
Call to Action:
Given the increased focus on the timeliness of delivering war-fighting capabilities and the implication to risk, there is a greater weight placed on risk management
Program management needs to effectively integrate cost, schedule, and risk
“For project plan to be effective it must equally address the parameters of 'activity time' and 'activity logic'. This logical relationship is required to model the effect schedule variance will have down stream in the project." - Rory BurkeModular Construction/
Manufacturing & Assembly Plan
> Definition: Modular construction is a building process, focusing on prefabrication that is ultimately assembled on-site
Stand-alone “modules,” which is a set of concentrated hardware and software that drives one or more specific functions, are designed and built independently and then assembled to form a complete platform
Multiple control paths vs. one traditional critical path
> Process delivers numerous benefits, such as (1) shorter build times, and (2) greater efficiency
Improved learning due to repetitious work on modules
Maximize the construction that takes place in an open shop environment and minimize what has to be done within the confines of a platform
> Modular construction is utilized across large complex programs, spanning both commercial and defense applications
> VIRGINIA Class’ modular construction exemplifies this process
Overview
Objective: To provide a framework that offers an additional perspective in understanding a program and its risk holistically and that is aligned with how the product is manufactured.
Method: The framework reflects a theoretical approach in assessing the risk of the VIRGINIA Class Manufacturing Assembly Plan (MAP), also known as the Four-Module Build Plan
MAP Applications
EVM Risk Concept - Analysis
> EVM methodologies primarily captured in 2 categories
Regression techniques (e.g., Weibull Function)
Performance Assessments (e.g., EVM Gold Card)
> EVM regression techniques applied to each MAP section to develop estimated burn rate
* Graphs do not reflect real data, but are for conceptual purposes only.
EVM Risk Concept - Analysis
> Calculate the difference between the estimated average % burn rate and the upper/lower risk bounds where data points exist for a given section
EVM Risk Concept - Example
> Example below is shown for multiple sections
EVM Risk Concept
> High and Low bounds established for each Section
Assumed dependencies between each Module (i.e., 1/2A, 2B/5, 6/7, and 8/9) and Final Assembly and Test (FAT)
Cost Estimation Concept
> Example: Assume adding a payload module to Section 2B/5
Since capability is added to Section 2B/5, there should be no impact/minimal impact to the other modules (i.e., Section 1/2A, 6/7, 8/9)
There could potentially be impacts to Final Assembly Test (FAT)
Potential methodology:
Apply cost estimation factor to Mean of Section 2B/5 and FAT
Likely to shift S Curve to the right
Re-assess risk for Section 2B/5 and FAT (Coefficient of Variation, CV)
Likely to increase risk (i.e., 'flatten' the S Curve)
Program Management Concept
> Assumed dependencies between each Module (i.e., 1/2A, 2B/5, 6/7, and 8/9) & Final Assembly Test (FAT)
Program Management Concept
> By modeling labor hour burn rates and risk profiles at the Shipyard, Program Managers could optimize workload at the Shipyards and make better informed decisions
Conclusions
> Manufacturing Assembly Plan (MAP) offers an additional perspective in understanding a program and its risk by aligning cost estimating, earned value management, and program management to how the product is manufactured
> Risk is an intricate part of program management; it should be embedded into all processes
Risk is key consideration to drive decisions and not an afterthought to be applied once a decision is made
> Risk is dynamic, evolving over time, and impacted by other activities
> Earned Value Management
Refine EAC and schedule estimates by better understanding when risk exists and when it is retired
> Cost Estimating
Estimating at lower levels provides insight into impact on other modules and other projects
> Program Management
Enables strategic decision making (e.g., when to add capability into program, budget requests, stability of industrial base given workload impacts, etc.)
Contact Information:
Edward Walsh: EWalsh@mypantheonsolutions.com
Richard Lee: RLee@mypantheonsolutions.com
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