Project planning and estimating is a big field. In a nutshell, the key to making such an effort very effective is just really good knowledge of and experience

Project planning and estimating is a big field. In a nutshell, the key to making such an effort very effective is just really good knowledge of and experience with all the parts of the project to be done. Below in (1) -- (5) I give an overview of parts of the field. (1) Importance of Experience. Here some examples where a lot of experience is crucial: A kitchen/bath remodeling firm can give a good estimate of time and cost because they have already done, say, one such job a week for five years, that is, 250 jobs; so, essentially they have already seen it all, that is, all the possibilities of what can go wrong and how much there is to do. Similarly for an auto body shop, a residential HVAC company, a plumber or electrician, a landscaping company, an asphalt paving company, a residential housing general contractor, etc. (2) Software Projects and Experience. One means of project planning and estimating for software projects looks at each of the parts of the work and starts with the experience level of the team for that part. For parts of the work where the team has no experience, that is, is doing such work for the first time, no estimates are made! So, the project planning works only for projects where there is good experience for all the parts of the work. Alas, often software projects need to do some things for the first time. (3) Critical Path. Some US DoD projects made use of Gantt charts, http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0CDUQFjAA&url=http%3A%2F%2Fen.wikipedia.org%2Fwiki%2FGantt_chart&ei=VVPOVL2RBsKvggTTmoLQAQ&usg=AFQjCNGPgpcTN31kjmjYV37AeWRWfKguxg&bvm=bv.85076809,d.eXY http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd... and in particular the concept of a critical path. As I recall, this work can make good use of linear programming which can identify the critical path. Intuitively the critical path is a sequence of tasks such that, if any task in that sequence takes one minute longer, then the whole project will take one minute longer. So, the tasks on the critical path have no slack. So, to speed up the whole project, must at least speed up the projects on the critical path. Next, then, of course, is the issue of where to put more resources to speed up the projects on the critical path. This, then, is a resource allocation problem, another optimization problem. (4) Alternatives and Uncertainty. One of the biggest problems in getting projects done on time and within budget is handling uncertainty, e.g., the effects of unpredictable, outside influences, that it, exogenous random inputs. And, there can be some alternatives in how to execute the projects; then during the project might select different alternatives depending on how the project has gone so far, that is, the then current state of the project. State -- Essentially the situation or status of the project at some particular time, and generally want enough information in the state (vector or collection of information) such that the past and the future of the project are conditionally independent given the state, that is, want to satisfy the Markov assumption so that in planning the future can use just the current state and f'get about all the rest of the past. One such state is always the full past history of the project, but commonly much less information is also sufficient to serve as the state. Commonly we can be drawn into the applied mathematics of stochastic optimal control, say, at least discrete time stochastic dynamic programming, e.g., Stuart E. Dreyfus and Averill M. Law, 'The Art and Theory of Dynamic Programming', ISBN 0-12-221860-4, Academic Press. Dimitri P. Bertsekas and Steven E. Shreve, 'Stochastic Optimal Control: The Discrete Time Case', ISBN 0-12-093260-1, Academic Press. Wendell H. Fleming and Raymond W. Rishel, 'Deterministic and Stochastic Optimal Control', ISBN 0-387-90155-8, Springer-Verlag, Berlin, 1979. E. B. Dynkin and A. A. Yushkevich, 'Controlled Markov Processes', ISBN 0-387-90387-9, Springer-Verlag, Berlin. So, a lot of quite serious applied math research has been done, and a lot is known. Broadly stochastic dynamic programming makes the best decisions that can be made making use of only information available at the time each decision is to be made. Nicely enough from the Markov assumption and just a simple application of Fubini's theorem, we can say a little more about being best possible. Interesting here, and a way to get some massive speedups of some software, is the concept of certainty equivalence in the case of LQG, that is, linear, quadratic, Gaussian control as in http://en.wikipedia.org/wiki/Linear-quadratic-Gaussian_control http://en.wikipedia.org/wiki/Linear-quadratic-Gaussian_contr... So, the system is linear (as in a linear operator or, for an important special case, matrix multiplication); the exogenous random variables are Gaussian; and the quantity to be controlled, say, minimized, is quadratic (which also includes linear). (5) Spreadsheets. Sometimes a spreadsheet is used for project planning, and there is an interesting observation to be made here: So, suppose we are doing financial planning for five years and develop a spreadsheet with one column for each month, that is, 60 columns, maybe 61, and one row for each variable. So, some of the spreadsheet cells represent exogenous random variables, and some cells represent decisions to be made. (A) We can fix the decisions and recalculate the spreadsheet, say, 500 times and, thus, do Monte Carlo simulation for the random cells and get an empirical distribution and expected value of the money made at the end of the project. (B) We can fix the values of the exogenous random variables and then use some optimization to find the values of the decisions that maximize the money made at the end of the project. Spreadsheet software has long, often had some optimization, from linear programming or L. Lasdon's generalized reduced gradient software, version 2 -- GRG2. This approach, in terms of football, say, the Superbowl later today, would be like the quarterback on first and ten calling the next four plays without considering the extra information he will have after each of the plays. Quarterbacks wouldn't do that, and business planners should not either. (C) But what about making the best decisions with the random exogenous random variables not fixed? Then usually we want to maximize the expected value of the money made at the end, but the form of the decisions is special: At each period, the decisions are in terms of the state of the project as of the end of the last period. So, such optimization is called discrete time stochastic dynamic programming, Markov decision theory, etc. So, nicely enough, computer spreadsheet software has enabled some millions of planners to specify in very precise terms some nearly complete formulations of problems to be solved with stochastic dynamic programming. For a reasonably large spreadsheet, such optimization would be a super computer application -- nicely, there really is a lot of parallelism that can be exploited. At one time I pursued just this direction at the IBM Watson lab in Yorktown Heights. Had the semi-far sighted management let me continue, by now there should be a good business for cloud sites to do such problems, say, use a few thousand cores. Ah, IBM has missed out on several much larger opportunities! There are various approaches to making the computations faster, say, using multivariate splines to approximate some of the functions (tables) to be generated, the R. Rockafellar idea of scenario aggregation, and more. My guess is that the OP has in mind something simpler. As I recall, there is some project planning software. Right, a simple Google search shows, e.g., Microsoft's Project 2013.