Slides 10 3 calculate projected costs with the cumulative a

Calculate Projected Costs with the Cumulative Average Learning Curve Intermediate Cost Analysis and Management © Forrrrrrrre!!! Should I take lessons? â Terminal Learning Objective Task: Calculate Projected Costs with the Cumulative Average Learning Curve • Condition: You are training to become an ACE with access to ICAM course handouts, readings, and spreadsheet tools and awareness of Operational Environment (OE)/Contemporary Operational Environment (COE) variables and actors • Standard: with at least 80% accuracy • Describe the concept of learning curve • Identify the key variables in the learning curve calculation • Solve for missing variables in the learning curve calculation â What is the Learning Curve? Learning is an important part of continuous improvement • Learning curve theory can predict future improvement as experience grows • Learning occurs most rapidly with the first few trials and then slows • Cumulative learning curve percentage conveys the factors by which the cumulative average adjusts with every doubling of experience â In-Class Activity • • Appoint one student as class timekeeper Divide class into teams Instructor issues materials Instructor specifies task All teams start immediately and at same time Timekeeper records time each team finishes task Instructor converts time into resource consumption (person seconds) Team A B C D E F People Seconds Per-secs â Class Discussion How did we do? • How can we it better? • Was there role confusion? • Were we over staffed? • How much better can we it? © Cumulative Average Learning Curve (CALC) Theory “The Cumulative Average per Unit Decreases by a Constant Percentage Each Time the Number of Iterations Doubles” • Expect a certain level of improvement with each repetition • Absolute improvement is marginal and will decrease over many repetitions • Assume a consistent percentage of improvement at Doubling Points (2nd, 4th, 8th, 16th, etc.) • Improvement is based on cumulative average cost © Cumulative Average Learning Curve (CALC) Theory “The Cumulative Average per Unit Decreases by a Constant Percentage Each Time the Number of Iterations Doubles” • Expect a certain level of improvement with each repetition • Absolute improvement is marginal and will decrease over many repetitions • Assume a consistent percentage of improvement at Doubling Points (2nd, 4th, 8th, 16th, etc.) • Improvement is based on cumulative average cost © Cumulative Average Learning Curve (CALC) Theory “The Cumulative Average per Unit Decreases by a Constant Percentage Each Time the Number of Iterations Doubles” • Expect a certain level of improvement with each repetition • Absolute improvement is marginal and will decrease over many repetitions • Assume a consistent percentage of improvement at Doubling Points (2nd, 4th, 8th, 16th, etc.) • Improvement is based on cumulative average cost © Cumulative Average Learning Curve (CALC) Theory “The Cumulative Average per Unit Decreases by a Constant Percentage Each Time the Number of Iterations Doubles” • Expect a certain level of improvement with each repetition • Absolute improvement is marginal and will decrease over many repetitions • Assume a consistent percentage of improvement at Doubling Points (2nd, 4th, 8th, 16th, etc.) • Improvement is based on cumulative average cost © 10 The CALC Template Cumulative average for Event • Total per-secs = cumulative total/1 after 2nd event is sum of 1st and 2nd events (300 + 240 = 540) Trial Number Event Per-Secs 300 240 Total Cumulative Per-Secs Average 300 300 /1 = 540 270 CALC % 90% • Cumulative Average after 2nd event is Total divided by number of events in the Total (540/2 = 270) • CALC% is the ratio between cumulative averages of 2nd and 1st events (270/300 = 90%) © 16 The CALC Template • Total per-secs after 2nd event is sum of 1st and 2nd events (300 + 240 = 540) Trial Number Event Per-Secs 300 240 Total Per-Secs 300 540 Cumulative Average 300 270 CALC % 90% • Cumulative Average after 2nd event is Total divided by number of events in the Total (540/2 = 270) • CALC% is the ratio between cumulative averages of 2nd and 1st events (270/300 = 90%) © 17 The CALC Template • Total per-secs after 2nd event is sum of 1st and 2nd events (300 + 240 = 540) Trial Number Event Per-Secs 300 240 Total Cumulative Per-Secs Average 300 300 540 270 /2 = CALC % 90% • Cumulative Average after 2nd event is Total divided by number of events in the Total (540/2 = 270) • CALC% is the ratio between cumulative averages of 2nd and 1st events (270/300 = 90%) â 18 The CALC Template Total per-secs after 2nd event is sum of 1st and 2nd events (300 + 240 = 540) Trial Number Event Per-Secs 300 240 Total Cumulative Per-Secs Average 300 300 540 270 /2 = CALC % 90% • Cumulative Average after 2nd event is Total divided by number of events in the Total (540/2 = 270) • CALC% is the ratio between cumulative averages of 2nd and 1st events (270/300 = 90%) © 19 What CALC% Did the Teams Achieve? • Complete the table Team 1st event cum avg 2nd event cum avg 2nd event CALC% © 20 Can We Get Better? • Of course! There is always a better way • However, learning curve theory recognizes that improvement occurs with doubling of experience • Consider the 80% CALC Trial Cum Avg 100 80 64 51.2 16 40.96 32 32.768 © 21 Can We Predict the Event rd • Yes – but this gets more complicated • Because the 3rd event is not a doubling of experience from the 2nd event • There is an equation: y = aX b • • • • b= ln calc%/ln a = 1st event per-secs X = event number y works out to 70.21 for the cum avg after 3rd event • (We are only interested in natural doubling in this course) â 22 However We can easily calculate the per-secs for the 3rd and 4th events combined Trial Number Event Per-Secs 300 240 Total Per-Secs 300 540 972 Cumulative Average 300 270 243 © CALC % 90% 90% assumed same as 2nd 23 However, • We can easily calculate the per-secs for the 3rd and 4th event combined Trial Number Event Per-Secs 300 240 Total Per-Secs 300 540 972 Cumulative Average 300 270 = 243 CALC % 90% 90% nd event 90% * 2nd cum avg © 24 However, • We can easily calculate the per-secs for the 3rd and 4th event combined Trial Number Event Per-Secs 300 240 Total Cumulative Per-Secs Average 300 300 540 270 972 4x 243 CALC % 90% 90% * cum avg for â 25 However, We can easily calculate the per-secs for the 3rd and 4th event combined Trial Number Event Per-Secs 300 240 Total Per-Secs 300 540 972 Cumulative Average 300 270 243 CALC % 90% 90% Prediction for total of events & is difference between cumulative total for and cumulative total for 4: 972 -540 = 432 © 26 Finishing Up • The team with the best 2nd event time and the team with the best CALC% will complete the task two additional times • Each student should calculate a prediction for the best total time for 3rd and 4th event • The team with the best 3rd and 4th event time and the three students with the closest prediction WIN © 27 Score Sheet Team: Team: Trial Number 3+4 pred 3+4 act Event Per-Secs Total Per-Secs Cumulative Average CALC % Trial Number 3+4 pred 3+4 act Event Per-Secs Total Per-Secs Cumulative Average CALC % â 28 Applications for Learning Curve Learning effects all costs and can be a major factor in evaluating contract bids • How many per-secs did the winning team save after four events compared to their 1st event time without learning? • Learning curve effects are very dramatic over the first few events • Consider the effect on new weapons systems developments • What are the advantages of a contractor who has already “come down the learning curve”? © 29 Practical Exercises © 30 ... Improvement is based on cumulative average cost © 10 Cumulative Average Learning Curve (CALC) Theory The Cumulative Average per Unit Decreases by a Constant Percentage Each Time the Number of Iterations... Improvement is based on cumulative average cost © Cumulative Average Learning Curve (CALC) Theory The Cumulative Average per Unit Decreases by a Constant Percentage Each Time the Number of Iterations... Improvement is based on cumulative average cost © Cumulative Average Learning Curve (CALC) Theory The Cumulative Average per Unit Decreases by a Constant Percentage Each Time the Number of Iterations