By: Dr. Dan Patterson, PMP Introduction Do you remember the number of times you have had to manually check your Word document for spelling errors? How many times a day do you explicitly type a recipient’s email address when compiling an email? How often do you mentally calculate how many miles a journey is when planning a road trip? The answer, of course, is never. Today, we rely on computers to store and, more importantly, recall knowledge, eliminating the need for us to carry out repetitive and mundane tasks. Why then do traditional project planning tools provide next to no guidance to a planner when building a project schedule? Suggestions regarding durations, guidance on whether the sequence of work is correct, warnings about erroneous building blocks such as open-ended tasks, etc. For the past twenty years, project planning tools have been reactive – waiting for you to enter data – instead of proactively offering suggestions and helping users input meaningful data. Project planning needs an overhaul, and I believe that should be in the form of what I am going to call “Predictive Planning.” Think about predictive text for a moment. Predictive text is called predictive for a reason – as you type, the computer is predicting what you want to type based on what you’ve already typed. Well, why can’t planning tools do the same? As I build out my plan and define activities, it shouldn’t be beyond the realms of reasonableness for the planning software to at least make suggestions. If they are good suggestions, then I’ll use them; if they are bad suggestions, I can tell the planning tool, and it should be able to learn from its mistakes in the same way we can train a spell checker to ignore certain words or use specific spellings. For predictive planning to really be effective we need two key capabilities: 1. Ability to capture and store sufficient historical data in order to establish trends or benchmarks 2. Ability to search this data storage and extract relevant patterns and make accurate suggestions Capturing Data Traditionally, data storage has been expensive and limited to highly structured data (e.g., modeled in a relational database or the likes of Excel). Today, data storage is not only cheap but a lot more flexible in terms of storing less structured data, e.g., natural language. Planning organizations pride themselves in hiring the smartest, most savvy planners, yet do little to try to capture and retain that expertise and knowledge. It really shouldn’t be all that difficult to take historical as-built plans (that, of course, have that expertise embedded in them), or corporate standards and benchmarks, and develop a knowledge library. If an organization can establish a knowledge library, and re-use the information in it, predictive planning is a no-brainer. Making Sound Suggestions For a computer or software tool to accurately predict an outcome (or in regard to predictive planning, make a suggestion to the planner), it firstly needs to understand context. In order to answer the question, “What is the weather going to be tomorrow?”, we need to know the location, time of year, and the weather trend for the past few days. With these data points, we can then use experience, based on historical data, to predict and model what is essentially a future outcome – i.e., tomorrow’s weather forecast. In the case of building a project plan, knowing the size, scope, and type of project is a first step in giving the software some guidance as to where to focus its search when coming up with suggestions. When planning, you are predicting. You are trying to predict as accurately as possible a future outcome. This prediction is based on context and historical analogies. Convergence of Technology Advancement and the Planning Discipline In recent years, computing technologies have enabled the likes of neural networks and expert systems to return more accurate and sensible predictions. This approach today is coined under the already overused and ridiculously broad term: Artificial Intelligence. I believe our approach to planning should really be more along the lines of augmented intelligence. We shouldn’t kid ourselves into thinking we can totally replace the expertise of a planner. However, we should be bold enough to embrace the fact that computers are smart enough now to assist during the planning process, make smart suggestions, and give guidance. Mistakes Make Us Smarter So, if a planning tool were able to store knowledge and subsequently provide suggestions based on that knowledge during the planning process, how would it get smarter over time? Well, that’s easy. As the planning tool offers its suggestion, the planner either accepts or pushes back on the suggestion. This push-back or acceptance is how the tool learns – it can calibrate how often its suggestions are correct and adjust accordingly. The more interaction between the planner and the planning tool, the smarter the planning tool becomes. Time to Replace the Planner? Given such advancements are becoming more than just a vision, does that mean we can expect to see the demise of the planner role? Absolutely not. In fact, quite the opposite. As projects continue to face increased time and cost scrutiny, the realization that there is a big difference between a true planner and someone who can just drive a scheduling tool is growing. Planners hold knowledge; they carry expertize – this knowledge and expertise needs to be captured and used in a meaningful way before the science of CPM-based planning dies a slow death. I believe we are in the early stages of a much-needed revamp and leap forward in the science of project planning. Working “on the plan” not “in the plan” is a much better use of a planner’s valuable time. Let’s make that a reality. Learn more at BasisPlanning ... | 
