Learning Curves Redux: A New Use for a Familiar Tool
This paper proposes another use for learning curves, namely the scheduling of production operations. This is not entirely a new idea, but it is usually not formally implemented in the planning of production operations. Learning curves alone can’t do this planning job effectively, but when combined with other appropriate, relatively simple logic, the result can be an automated scheduling process that predicts not only the cost of each produced item, but also the dates when production of each item will start and finish, plus a spread of labor hours and material costs by month, or even by week, if desired.
The method can be applied to multiple production lots of arbitrary size, making it useful for many situations, including block production, simultaneous production at multiple facilities, and LRIP/FRP situations. In this paper, we demonstrate the process by showing methods for use of learning curves to schedule production in three planning modes:
• Constant effort, with the same labor hours expended in each time period
• Uniform rate, with the same production quantity in each time period
• Ramp-up, beginning at zero rate and accelerating linearly to a maximum rate.
User input is minimal. For each production lot, the user specifies production quantity, start date, finish date, learning curve theory and slope, mode of scheduling, and information about transfer of learning from a previous lot, if appropriate.
Evin Stump is a senior systems engineer for Galorath Incorporated. He has recently managed the Darwin project, aimed at developing an advanced cost model for estimating the effects on projects of “evolutionary acquisition.” Other recent and ongoing assignments include development of the Spyglass model for estimating costs and schedules of development and production of electro-optical sensors used in space, and updating the SEER-IC model for estimating costs and schedules of development and production of integrated circuits.
Mr. Stump has over 54 years of experience in the aerospace industry having worked as a test engineer, design engineer, systems engineer, project engineer, cost engineer, engineering manager, and independent consultant. Major projects to which he has made a significant contribution include Mercury, Gemini, Apollo, Tacit Rainbow, Brilliant Eyes, C-17, and RSA IIA. Mr. Stump is a former president of the Southern California chapter of the International Society of Parametric Analysts (ISPA), and also of the Southern California chapter of the Society of Cost Estimating and Analysis (SCEA).
Mr. Stump holds the BS in Engineering form Loyola University of Los Angeles, and the MS in Operations Research from the University of Texas at Austin. He has also earned the professional designation in government contract management from UCLA/NCMA.
Alexandra Minevich is a Systems Engineer/Cost Analyst for Galorath Incorporated. She has recently assisted in development of the labor effort and duration allocation schemes for SEER-SEM Client for MS Project. She also participated in the development process of SEER-H Client for MS Project, and has developed and tested a prototype of the algorithms which are the subject of this paper.
Ms. Minevich holds the BS in Computer Science from the University of California at Los Angeles. As a student at UCLA, Ms. Minevich participated in a project to develop a health alert and monitoring system. The project involved establishing system requirements, subsystem specifications, organizational arrangements, work breakdown structures, marketing approach, and risk analysis.