1. Advanced Scheduling Concepts Software can land an aircraft. Why can’t it plan a project? Shane Archibald, Managing Principal Archibald Associates & Spider Project USA SESSION FRI35

2. Advanced Scheduling Concepts In this session, we will  Review some significant advances in software and computing capabilities… and how they came about  Review what we think we know about scheduling… and maybe learn otherwise  Shift our way of thinking and take a giant step forward  Review some interesting and unique functionality in a very special Scheduling tool

3. The Evolution of Computers

5. Computers have never been good at finding answers to complex questions  The very best can only comprehend and answer simple, straightforward questions but still get them wrong nearly one third of the time  Search engines don’t answer a question–they deliver thousands of search results that match keywords  Are we satisfied? If so, then Why?

6. IBM’s R&D Challenge The grand challenge driving IBM’s project was to win on the game show Jeopardy! The broader goal was to create a new generation of technology that could find answers in unstructured data

7. IBM’s Answer Watson

9. A Significant Step in Computing Evolution – IBM’s Watson Fed mountains of information, including text from  Commercial sources, such as the World Book Encyclopedia  Sources that allow open copying of their content, such as Wikipedia and books from Project Gutenberg

10. A Significant Step in Computing Evolution – IBM’s Watson Programed it to respond based on rank of findings Tested it in a mock game of Jeopardy! Failed miserably!

11. A Significant Step in Computing Evolution – IBM’s Watson

12. Watson They tried Again. And again. And failed each time. Asked themselves how people learn and improve The Paradigm Shift

13. February 2011 – Success!!

14. Watson – Early Lessons Learned Why was the Team successful?  Didn’t settle for existing “reality”  Stopped doing the same thing, expecting different results  Team asked themselves what the difference was between people and Watson  Understood a minor change in approach can lead to a significant change in outcome What does all this have to do with Scheduling?

15. IBM’s Watson – Relevant to Scheduling? Traditional CPM Software  Uses a single predefined formula; no heuristics  Uses heavy human intervention; no automated adjustments or changes  Results require significant analysis, validation & manual adjustments; “I’ll take it from there…”  Process is cumbersome, inefficient and inaccurate

16. Cumbersome Process?

17. Collect information and estimates (duration, effort, resource, cost, risk, etc.) Enter into scheduling system, calculate, validate; plan and schedule produced! Work Update for progress, recalculate, validate Analyze results and identify issues Meet with team to mitigate issues; make decisions and new plans Enter decisions / changes to schedule, recalculate, validate Repeat …For the life of the project!

18. Cumbersome Process? What if we are talking about aircraft autopilot? Collect information Enter into system Fly (for a second) Update information for flight progress, recalculate forecast, validate Analyze results and identify issues Meet with team to mitigate issues; make decisions and new plans Enter decisions / changes to flight controls, recalculate, validate Repeat …For the life of the flight!

19. What if we are talking about aircraft autopilot?

22. Software Can Land an Aircraft! But it needs the variables, rules, and algorithms to do so

23. The Mechanics

24. The Variables Internal variables  Elevator (Pitch)  Rudder (Yaw)  Aileron (Roll)  Flaps (Lift)  Ground & Air speed  Thrust  Gross weight  Drag / Friction External variables  Wind speed  Wind direction  Air pressure  Weather  Altitude  Destination (location, altitude, etc.)  Other aircraft  Other changes

25. The Rules Gross weight –– Air Speed Air speed –– Flaps Pitch –– Thrust Drag –– Thrust Etc…

26. The Algorithm If  Air Speed < X and Flaps < Y Then  Increase Thrust until Air Speed => X  OR  Lower Flaps until Flaps => Y  Or… Immediately measure again and repeat

27. The Results FAST response times ACCURATE adjustments REDUCED workload

28. Software Can Land an Aircraft! If a computer can land a 450 ton aircraft full of human life with zero intervention, then why can’t it correctly re-sequence a few thousand activities and resources? IT CAN! But it needs the variables, rules, and algorithms to do so

29. The Challenge How do we develop a “Self-Correcting” Schedule?

30. Re-pave Roadway A Sample Exercise: Re-pave Roadway

31. Existing “Reality” Standard Schedule Input for an Activity  Duration  Logic  Resources  Cost  Baseline  *Timeframe (Calendar window)

32. Existing “Reality” What are we trying to represent?  Duration – Amount of work (and resource capacity)  Logic – Relationship to other work  Resources – People, Equipment & Materials used to complete that work  Cost – Cost of the work (and overall Spending Plan)  Baseline – Target of the work; success criteria  *Timeframe (Calendar window) – When the work will take place

33. Existing “Reality” What are the maintenance requirements?  Duration – Amount of work (and resource capacity)  Logic – Relationship to other work  Resources – People, Equipment & Materials used to complete that work  Cost – Cost of the work (and overall Spending Plan)  Baseline – Target of the work; success criteria  *Timeframe (Calendar window) – When the work will take place

34. Existing “Reality”

35. A Sample Exercise Are there opportunities for improvement?? Absolutely!

36. Remember the IBM R&D Team?

37. Shifting The Paradigm Didn’t settle for existing “reality” Stopped doing the same thing, expecting different results Asked themselves what the difference was between human-decision and software-output processes Understood that a minor change in approach could lead to a significant change in outcome

38. Let’s Think About This… What are the Variables?  What information do we, as a team and in a meeting, use when determining the best current plan or solution? What are the Rules?  How do we, as a team, chose alternatives when tasked with changing our plan? What should the Heuristic algorithm target?  The target is determined by the user  The algorithm is proprietary

39. A Sample Exercise Internal & External Variables

40. Internal Variables those that are completely self-contained within the subject, are directly related and work together to achieve some result External Variables IMPACT the Internal Variables in some way, shape or form

41. Our Challenge… …Is to develop the Internal Variables and related Rules such that they react to impacts from External Variables without human intervention Assumption: The Schedule “Activity” is the basic building block of a Schedule and will be our focus

42. What are the Internal Variables of our Activity? Unique characteristics Timeframe Effort (Hours) Duration Responsibility (Resources) Cost to Complete the Work *Scope of Work* (volume)

43. What are the External Variables? Labor Availability Equipment Availability Availability of Supplies & Materials Space limitations True Predecessors Special Events Weather or Season Site Conditions Target or Baseline Management “preference”

44. How do we Automate Internal Variables… …So they self-correct when something changes?

45. Primary Foundational Building Block Volume is an attribute of each activity, representing the amount of work to be done

46. Let’s Start SLOW, From The Top and Automate our Internal Variables Unique characteristics Timeframe Effort (Hours) Duration Responsibility (Resources) Cost to Complete the Work

47. Imagine… What if we could wrap Rules around the activity, like…  You should delay the work until the last minute  You cannot pause the work; it must be fully completed in a single pass  If Site A has standing water, you must remove that water before digging the trench

48. We Will Have Automated… Unique characteristics Timeframe Effort (Hours) Duration Responsibility (Resources) Cost to Complete the Work

49. Imagine Complex Logic… What if Logic could be used to represent complex relationships to other work, like…  You MUST do the work IMMEDIATELY following completion of predecessor Y  You can start the work as soon as predecessor X is 30 yards down the road but you cannot pass 50-yards until predecessor X is 10 yards from completion  If Z happens, then we need to do B, C, & D before A

50. We Will Have Automated… Unique characteristics Timeframe Effort (Hours) Duration Responsibility (Resources) Cost to Complete the Work

51. Imagine that Resources… …had unique Productivity Values for that type of work

52. Then Hours Required… …would be the result of Volume of work divided by individual Resource Productivity Hours = Volume / Resource Productivity And Duration = Hours / Resource Workday

53. Then Hours & Duration… …Would be Self-Adjusting based on Resources Assigned But we would still need to manually assign them

54. We Will Have Automated… Unique characteristics Timeframe Effort (Hours) Duration Responsibility (Resources) Cost to Complete the Work

55. Imagine Resources… …With attributes describing their specialized Skill …That could be automatically assigned to activities based on Skill required

56. We Will Have Automated… Unique characteristics Timeframe Effort (Hours) Duration Responsibility (Resources) Cost to Complete the Work Are we there yet?

57. Imagine Resources With… Specific hourly and overhead Rates

58. And… Cost of the work was based on  Forecasted hours and true rates of automatically assigned “named” resources  Overhead costs were applied by hour or flat rate or quantity of material or use of equipment  Materials consumed by Equipment and was automatically applied

59. Then TOTAL Cost… …Would be automatically calculated based on the specific Resource that has been automatically assigned

60. We Will Have Automated… Unique characteristics Timeframe Effort (Hours) Duration Responsibility (Resources) Cost to Complete the Work

61. How Long…? How long do you think it will take companies to develop software that will let us use Variables?

62. Actually… It’s already here!

63. Spider Does! Spider allows Multi-Resources too  Connect your Named Resources (e.g. Bricklayer, Bricks, Masonry, and Mixer) to create a multi-resource and make just a single assignment for brick work Maybe you have multiple mixers and bricklayers…  Use Skills as part of your Multi-Resource to let Spider pick the best named resource for the assignment

64. Spider Does! Teams of Resources can be:  Assigned to activities so the activities will start only when the whole Team is available to work  …Or Resources can be described as “able to do the work” and the software would pick the “best” or available team to complete the work, make the assignment, calculate the costs, and consume the materials  Teams can consist of may different Resources, Equipment, Materials, Multi-Resources or Skills

65. Spider Does! Resource assignments can carry Rules such as…  “You MUST have at least 2 of this type of resource to start the task. You can have more resources but cannot exceed 5 at any given time during performance of the task due to space or other limitations” Spider will start the task when it can find two resources that are ready to perform and will ADD up to three additional resources if and when they become available, to complete the task faster

66. Spider Does! Spider supports TRUE Risk Management  Insert REAL Risk Events into your activity network  Attach Mitigation Plans to those Risk Events using conditional logic Built-in Monte Carlo will consider these events and mitigation plans when calculating probabilities of success for At Complete dates and costs

67. Spider Does! Baseline values…  Can be driven by confidence levels, which can be driven by MC statistical analysis  Can easily compare values, in your schedule, ON YOUR SCREEN (and NOT in a 2,394 page text file!)  Can be easily updated

68. Spider Does! Spider supports the 3-Senarios Method  Build Optimistic, Pessimistic, and Most Likely schedules- Spider will automatically maintain synchronization from a single update  Provide the Pessimistic version to the owner or Client  Provide the Most Likely version to the Project Manager  …But drive the Team towards the Optimistic version

69. Spider Does! Algorithms are based on Heuristics heu·ris·tic  4. Computers, Mathematics. pertaining to a trial-and-error method of problem solving used when an algorithmic approach is impractical. -dictionary.com

70. Spider Does! Schedule Optimization Spider is the ONLY Project Management software that optimizes resource, cost, and material constrained schedules and budgets for projects and portfolios

71. Spider Does! Monte Carlo & Schedule Optimization Spider is the ONLY Project Management software that properly runs Monte Carlo on Optimized Schedules

72. Spider Does! Spider will search through multiple combinations of variables to automatically assign the best Resource(s) to each activity, based on:  Type of Resource(s) needed  Availability of the Resource(s), when the work takes place (NOT when the project is initially planned)  Total Cost to complete the work, based on the cost and productivity of the Resources in the pool  Or on speed to complete the work, based purely on the productivity of the resources in the pool  …Based on your personal choice

73. Spider Does! With the push of a button you can run a heuristics-based algorithm that will search through various combinations of all those variables to find the optimal schedule to give us:  The shortest possible schedule?  The lowest possible Cost?  The greatest return on investment?  The quickest release of key Resources?  The most Payment Milestones by the end of the year? What is your priority? What will it be tomorrow?

74. Spider Does! …SO much more Visit SpiderProjectUSA.com

75. Conclusion Paradigm Shifts ARE possible  Don’t settle for existing “reality”  Stop doing the same thing, expecting different results  Ask yourselves what the difference is between human-decision and software- output processes  Understand that a minor change in approach can AND DOES lead to a significant change in outcome

76. Conclusion Project Schedules can self-adjust if given variables, rules, and heuristic algorithms instead of static values and simple equations Spider Project employs significantly advanced heuristic- based functions and algorithms to support Dynamic Variable Scheduling

77. Contact Shane Archibald, Managing Principal Archibald Associates & Spider Project USA Shane@ArchibaldAssociates.com Shane@SpiderProjectUSA.com