Capacity Planning Automation

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Project Participants: Huntington Ingalls Industries, Inc.

Project Start: June 2014

Currently, lay-down placement and assignment of units through successive shipyard work stations is done through a laborious, manual process. This process is dependent upon the experience of a limited number of personnel, time consuming paper processes, and is subject to a large amount of error. Some software tools are in place, but they are not automated. Automating the real-estate allocation process will save time in capacity planning, provide savings due to better lay-down coordination, reduce excessive movement of units, provide higher confidence in forecasting, and increase process efficiency.

Ingalls Shipbuilding Work Instructions define the processes and responsibilities for the proper allocation and optimization of real estate (lay-down spaces) for structural units and assemblies under construction, while providing forward visibility for scheduled or potential overloads to capacity. However, the capacity planning processes are tedious and overly time-consuming. Resulting real estate allocations are seldom optimal and often require substantial rework. One of the goals of an optimal real estate allocation process is to develop a layout and schedule that allows constructing as many units as possible under cover, versus outside.

The Capacity Planning Automation Project will permit a scheduling analyst to rapidly assess multiple changes from the current allocation of units to lay-down areas, largely mitigating the weaknesses of the current manual analysis method. Using the Master Construction Schedule (MCS), the Pre-Assembly Schedule (PAS), and Unit Estimated Completion Dates (ECDs) as the factual basis of an analysis, the system will use an applied artificial intelligence in the form of a rules-based Expert-System to produce an efficient utilization of available lay-down real estate. The system will be easily extended for future capabilities.

This Ingalls project has two phases: Phase I identifying and defining needs and requirements and Phase II developing and deploying the system solutions. Ingalls will deploy the solution in its target environment after initial acceptance tests are complete, and engage end users to ensure the solution satisfies documented needs and expectations. If any significant deficiencies are noted, the solution will be revised by the development team and acceptance testing will be repeated. Once implemented, this technology could reduce real estate allocation processing time by 30% and increase in number of units constructed under cover by 20 with an estimated $990K savings per year.

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