Why should you use historical data to estimate project costs?

One thing I have found helpful is to have all possible historical data from similar projects. This is especially critical for cost estimations and risk assessments. Having a strong data backup will make a difference in the planning and execution phases.

The data provides information to use to minimize variences in estimating project/program budgets and schedules to support tasks.

In our daily business and relevant activities lot of values are being generated and if they are captured and saved it become historical data. There is no fix timeline to define historical data. Any data older than today is know as historical data.

Historical data of expenses will share insights on the past spent on similar project, which might be One Off / Incidental / Regulatory Compliance related / Wage revisions etc. All these buckets are necessary buckets, which tend to get overlooked when we do Zero base line for cost estimations.

Historical project costs are a good place to start IF you practiced good record-keeping and logged all expenses. Be careful to account for inflation and/or material changes.

A best use of Historical data can’t be defined until we use data analytics to it. Such analysis can be done using MS excel or any data analytic tool. Analysis’s of data help us to drive meaningful results and can be use to make better business decisions or course corrections.

It is critical to use historical data to backup any strategic recommendation a person makes. However, the data and its source must be validated for accuracy and relevance. As we always say “garbage in garbage out.”

In my experience when were tasked with revamping the DCS for one of our plant, I utilized historical data to estimate project costs, and it was proved invaluable. By referencing a similar project conducted 2-3 years ago, we could identify the major cost components, such as procurement of hardware and software, engineering hours, and site installation expenses.

Utilizing historical data in project cost estimation involves leveraging past project information to create more accurate cost estimates for future endeavors

Overall, using historical data in cost estimation enhances the reliability of projections, improves planning, and empowers project teams to make more informed decisions, ultimately leading to better project outcomes.

This is a very complete holistic description but I miss the influence of the "human factor". Which organizations must work together in the project?, how are the teams composed and do people know and trust each other? , how is (effective) communication and (adequate) decision making organized? Especially for non repeating projects this is to my experience a very important consideration!

The biggest mistake I’ve seen using historical costs as a baseline is not understanding the details of the previous project. For example, a work package delivered by an experienced engineer can be an order of magnitude different from a new engineer that lacks specific knowledge of the deliverable.

Relying solely on historical data without considering the current project's specifics can lead to inaccurate assessments or quotes. For example, a painter with experience painting 10' x 10' rooms might incorrectly assume that a 100 sq ft room will have similar dimensions and layout. However, if the room is actually 2' x 50' (a closet?), the nature of the work changes significantly due to the increased wall linear footage (108 feet instead of 40 feet). This difference affects the labor, time, and materials needed. It underscores the importance of gathering detailed information about each project before making assessments or commitments.

Access to historical data on project costs is an advantage. However, consider the following limitations this data may have to successfully support estimating the budget of new engineering projects. -Some engineering projects are long-term and complex, and the cost data may reflect adjustments made throughout the execution. -Macroeconomic conditions during past projects may not reflect the current cost of items such as labor, logistics, prototyping, and pre-production builds. -The general budgetary assumptions may require corrections to stay relevant for the current project. -Change orders, scope modifications, and unforeseen challenges of past projects may artificially inflate the cost. Ultimately, use historical data carefully.

Another important point to be noted is the maturity of the team and organization as a whole in using historical data. The team learning curve may improve from previous years, organization may adopt new tools to increase productivity so on. Historical data serve as a point of estimate and not the only way to base your planning upon. It's always advisable to look at the data with other estimation techniques and baseline the best based on needs and organization methods.

To further enhance the use of historical data, there is a need to emphasize the continuous learning aspect. By regularly conduct post-project reviews and incorporate lessons learned into the historical data archives, it ensures that the database evolves with industry trends and project specific requirements. It is time to leverage technological advancements such as data analytics and machine learning in identifying patterns and predict cost trends.

My advice is to talk to the former project team members to understand to what extent historical data and conditions is comparable and applicable.

By systematically analyzing and applying relevant historical data, project managers can enhance the accuracy and reliability of cost estimates for current projects, enabling better planning, resource allocation, and decision-making.

In my experience, incorporating historical data in project cost estimation is crucial for accurate forecasting. It provides valuable benchmarks, highlights potential challenges, and enhances decision-making. Let's delve into the significance of leveraging historical data for more precise project cost estimates.

Historical data is only as good as the description of the context where this data originated. It says to what extent the data is reliable and useful for the estimate you have to make. This can be expressed in an estimated bandwidth.