Predicting The Future With Learning Curve Theory

Widespread use of good predictive methodologies has allowed mankind to employ an ever increasing stream of sophisticated technology.  The annals of contributions include Newton’s laws, Pythagoras’ Music of the Spheres, and more recently John Wanamaker’s invention of the price tags in his Philadelphia department store (, n.d).  Wanamaker’s contribution to commerce is as significant as open source code is to the internet.  Price tags not only allowed the equal treatment of his customers, but also publicized predictable expenditures.  Families were able to effectively budget for purchases of large and small items.  The store was able to accurately calculate its profit margin throughout a product life cycle.  The increased efficiency for both consumer and retailer lead to its adoption across multiple industries.  Today fixed pricing and price tags aren’t just reasonably commonplace, they dominate most transactions across the globe.

Predictable mechanisms are important in project management.  Resource management in projects is the art of integrating assets to maximize their effectiveness towards the project’s conclusion.  This paper will discuss the importance of Learning Curve Theory and how it applies to project management.  This discussion will take place in two parts.  The first will be a general overview of the theory in its relationship to project management.  The second part of this paper will focus on applying its principles to a project familiar to the author, namely tactical military communication systems installation.  An example exercise is provided at the end of this document.

Learning curve theory allows cost estimates to grow beyond a fixed rate estimate.  Fix rate estimates fail to adapt to the ability of individuals or systems to learn and improve their efficiency of a given task over time.  Learning curve estimates make this adjustment by calculating a rate of improvement over the units of production (Pinto, 2016).  Rates are estimated based upon expert experience and historical data, but are never truly 100% accurate.  Still, the ability to more accurately predict cost and production output makes this theory extremely valuable.  
At work we employ military tactical communications assemblages on demand in diverse locations throughout Europe.  Each of these on-demand instances is its own project.  Some of the repetitive tasks involved include creating proper grounding, system updates, and technical configurations.  In true military fashion these tasks are already broken down per person on the team with time standards for completion.  The military time standards do not take into account the ability of the Soldiers employing the systems to learn and improve upon the baseline standard.

Over the course of the next two weeks my Soldiers are being trained on the upgraded equipment and new configuration requirements to employ these systems.  After the training we are required to conduct a localized field test to confirm we are ready to provide communications support on demand going forward.  I will be using learning curve theory at work to predict the setup times for each of the 10 teams I have to manage.  To do this I will track the cost of time of their initial setup and the time required to reach a steady state.  This will be based upon my experience as both a Soldier performing similar tasks and as a supervisor.

Predictive methodologies are extremely valuable tools whether predicting how force at a given point of impact, the price of a series of goods, or the time it takes to reach a steady state of predictable output during a repetitive task.  As these models become more refined mankind will likely build the next series of technologies that improve our lives beyond our wildest expectations.