Forward pass

As explained in Statistics Every Programmer Needs

A forward pass is a project-scheduling calculation that determines each activity's earliest start and earliest finish times by progressing through its dependencies.

A forward pass schedules project activities as early as their dependencies allow. It computes each activity’s earliest start (ES) and earliest finish (EF), providing the earliest achievable project timeline and the basis for critical path analysis. section 13.3 section 13.3.1

Apply the calculation across the dependency network:

• Start activities without predecessors at time zero. section 13.3.1
• Set EF to ES plus the activity’s expected duration. section 13.3.1
• Set an activity’s ES to the EF of its predecessor when one predecessor exists; with multiple predecessors, use the latest predecessor EF. section 13.3.1
• Repeat for subsequent activities, then use the resulting times with a backward pass to determine latest times and slack. section 13.3 section 13.3.3

Automated reporting tool

The schedule begins with activity A at ES 0 and EF 3, followed by B at ES 3 and EF 5. Applying the same dependency calculation produces a 29-week project timeline. In a Python implementation, forwardPass stores ES and EF in a pandas data frame; later calculations identify zero-slack critical activities. section 13.3.1

Figure 13.2 A visual representation of the automated reporting tool project plan, where each activity is represented by its unique identifier. Lines and arrows between activities represent dependencies. The sequence of digits immediately beneath each activity represents, in order, the activity’s earliest start time, expected duration, and earliest finish time. The earliest times are derived by using a forward pass through the network. Based on this analysis, the project timeline is 29 weeks, which is the length of the longest path in the network.

Trace each activity’s dependency arrows and read the displayed ES, duration, and EF values across the project network. section 13.3.1

How Statistics Every Programmer Needs covers it

How deep it goes

Defines ES and EF, explains predecessor handling, demonstrates a network, and implements the calculation in Python.

Examples

Uses an automated reporting tool network, activities A and B, a 29-week timeline, and a tabular CPM output.

What it assumes

Assumes activities, dependencies, and expected durations, while defining earliest and latest scheduling terms explicitly.

What you can do after

Build it from scratch with Python, pandas, and NumPy to calculate earliest schedule times for dependent activities.

How it connects

Connects project scheduling with critical path method, backward pass, slack, dependencies, and work breakdown structure.

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Related concepts

• neural network layeruses · A neural network layer is a computational stage or component that transforms input representations into outputs within a neural network.
• predicted valueproduces · A predicted value is an estimated outcome or output that a model produces for given input values.
• backpropagationcontrasts with · Backpropagation is a procedure for computing derivatives of an output or loss with respect to inputs and parameters by applying the chain rule backward through a computational graph.
• generationproduces · Generation is the computational production of outputs from supplied inputs or context, often by applying a model to produce predictions or new content.

This explainer was generated by Manning's knowledge graph from the text of Statistics Every Programmer Needs. The section links point to the parts of the book it draws on.