Project scheduling represents an ongoing challenge for project professionals and organisations. Understand our journey to solve this challenge.
Founded in 2018, pminsight started in my study where I helped direct reports improve their project plans, schedules and forecasts. During this time, I investigated ways to better manage pipeline demands versus approved inflight projects, which includes the inevitable shared resource pool challenge.
Initial efforts focused on developing a ‘Critical Path Method‘ and a ‘Microsoft Project Schedule‘ Quick Reference Guide, and providing structured training using worked examples. Interestingly, this experience demonstrated most project managers prefer and benefit from having a dedicated ‘Master Scheduler‘, who is specialised in project planning, scheduling and control than actually doing this work themselves.
As a result of Master Scheduler ‘project performance‘ and ‘forecast‘ data-driven insights, project manager decision-making improved, which correlated with improved project outcomes and client-satisfaction. While, individual project delivery capability had improved, results were hampered by having ‘over-allocated resources‘ as a result of ‘shared resource pools‘. Initial efforts resulted in a Microsoft Excel resourcing tool, however, this became very unwieldy to manage and had limited ‘scenario analysis‘ functionality.
Not having access to ‘Microsoft Project Server‘, Microsoft Project’s ‘Master Project’ functionality was then researched. This approach saved a lot of time and effort, since it allowed me to consolidate reporting, better understand cross-project dependencies, undertake ‘what-if’ scenario analysis and manage resource allocations. This then led to researching Microsoft Projects ‘Resource Levelling’ functionality.
Unfortunately, Microsoft Project’s levelling function caused project duration’s to be extended far beyond client promised due dates. Researching ‘how’ to optimise schedules then led to learning about ‘Linear Programming (LP)‘, which is a mathematical technique that solves the problem of ‘allocating limited resources among competing activities in a best possible (i.e. optimal) way’.
While LP produces outstanding results and is of interest from a research perspective, it has limited practical application because of the time and effort required to analyse, model and test. ‘Heuristic algorithms‘ overcome this limitation by generating good solutions in a reasonable amount of time. Heuristics choose between alternatives, and involve a ‘priority list‘ and ‘schedule generator‘ to solve ‘Resource Constrained Project Scheduling Problems (RCPSP)‘.
‘Constructive heuristics‘ generate a first solution using either a ‘single‘ or ‘multiple-pass‘ priority rule, which is based on activity, network, scheduling or resource information. Single pass selects only one priority rule. Since no such priority rule exists that suits all projects because of underlying characteristics and resource usage it is likely a different priority rule will result in an improved outcome. Consequently, multiple-pass cycles through different priority rules that are applied individually to ‘serial‘ & ‘parallel‘ schedule generation schemes to find the project’s best time, resource or cost performance outcome.
Commercial off the shelf tools including Microsoft Project only perform a single pass for their proprietary time heuristic and schedule generation scheme. This is why, resourcing levelling results are discarded because the project duration is extended beyond what is reasonable and / or expected. ‘Meta-heuristics’ such as ‘simulated annealing‘, ‘tabu search‘ etc improve constructive heuristic multiple-pass results by adopting ‘local search neighbourhood‘ methods that apply a local modification at each iteration to the current solution. In addition to this, activity, network, scheduling and resource priority rules can be combined to generate solutions that better suit business needs rather than just focusing on the minimisation of time.
While all this sounds complex and it is, and while individual project schedules could be optimised for given resource constraints it didn’t solve my problem of needing to optimise multiple-project schedules for a shared resource pool. This is because multiple-project scheduling is significantly more complicated due to the number of parameters involved, variability in project structures, shared resource scheduling and deciding project and activity priorities. This is reflected in research, where most effort to date focus’s on optimising individual & not ‘Resource Constrained Multiple Project Scheduling Problems (RCMPSP)‘.
Over the past few years I have been fortunate to work with a number of vendors. The only tool that consistently optimises multiple-project schedules for a shared resource pool is ‘Oritames‘ Advanced Planning & Scheduling System. Developed by ‘MangoGem‘, they continue to respond to market needs and are investing heavily to develop more sophisticated solutions that drive better business and client outcomes.
This five-year journey has resulted in the development of pminsight’s ‘Advanced Planning & Scheduling 5-step (APS5)‘ framework. I am constantly researching supplementary tools and methods to enhance the APS5 service offering, and if you have any suggestions then please let me know.
In closing, I hope you have found this article of interest, and that you take the time to look around our website to better understand our value proposition. To ensure you become comfortable with pminsight, I will be blogging regularly from October 2018 and will be providing scheduling research articles that maybe relevant to your complex project scheduling problems. Schedule Better. Faster. More. pminsight