Some time ago, I posted a poll on LinkedIn asking about the origins of the CPM method. Here’s what the respondents said:
- 56% believe the method is all about making schedules efficient, focusing on time.
- 4% think it was originally created to save costs.
- 39% believe it was meant to optimise both time and costs.
I want to thank everyone who commented and shared links to websites and papers. Those resources are definitely worth checking out!
Certain planners have argued that optimising time inherently leads to cost optimisation as well. This assumption, however, may be inaccurate and could contribute to the higher project costs. It might be worthwhile to have a separate discussion to determine whether the common saying “Time is money” holds true in the context of projects.
The initial objective of the original CPM model was to identify the lowest project cost across various potential project durations. Achieving this goal necessitated a means of balancing both project time and cost. In my view, both responses, “Cost optimisation” and “Time and Cost optimisation,” are valid.
Research Article: “Sixty years of project planning: history and future” (M. Hajdu and S. Isaac).
“The development of the CPM technique started in 1956, when the management of DuPont decided to utilize their UNIVAC 1 computer to support the maintenance work of their production plants. The management of the company wanted to prove that IT is the future, and that the money they had spent on the computer was not in vain. DuPont’s management thought that using the computer for planning and cost optimization was an excellent way to prove its utility. Morgan Walker, an engineer at DuPont, got the assignment of figuring out whether UNIVAC could be used for solving such problems.”
Since the 1960s, numerous papers on the Critical Path Method (CPM) have been published. While some proposals address specific situations, we still lack a comprehensive method to calculate schedules that are optimised for cost. When the Critical Path Method was first developed, the challenge of balancing time and cost was too complex for the existing computers and algorithms of that time to handle effectively.
Today, however, computers have become significantly more powerful, and AI solutions are reshaping different aspects of our life. Despite these advancements, there has been limited progress in solving the challenge of cost-optimised scheduling.
In upcoming posts, we review why the challenge is so complex, and we can discuss if it could be solved in the near future.