Enhance Reports With Detailed Contributor Analysis

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Enhancing Reports with Detailed Contributor Analysis

Hey guys! Today, we're diving deep into a project focused on making our reports even more insightful by adding detailed contributor analysis. This is all about replacing those generic "NA" messages with some seriously meaningful text that explains which specific items or factors are contributing to both high and low scores in our resilience dimensions. Let's break it down, make it human-friendly, and ensure we're delivering top-notch value to our readers.

The Goal: No More "NA"s!

Our main aim here is to provide detailed text analysis that pinpoints the key contributors to high and low dimension scores. We're talking about getting rid of those unhelpful "NA" messages and replacing them with actionable insights. Think of it like this: instead of just saying something is good or bad, we'll explain why it's good or bad.

Definition of Done

To make sure we're all on the same page, here's what we need to accomplish:

  • Identify High Contributors: We need to figure out which specific items are driving high scores in each dimension.
  • Identify Low Contributors: Similarly, we need to pinpoint the items pulling the scores down.
  • Match Current Report Style: The new text should seamlessly blend with the existing report format. No one wants a Frankenstein report!
  • Clear Examples: For instance, we want to say things like, "Items that contribute to a high level of Flexibility are: - Alternative transportation options, alternative suppliers available."
  • Coverage: This needs to work for all 5 dimensions (Redundancy, Collaboration, Flexibility, Visibility, Agility) and all 3 pillars (Upstream, Internal, Downstream).
  • Key Insights Alignment: The insights we generate must match the actual report data. No pulling rabbits out of hats here!
  • Validation: Ronald needs to give us the thumbs up after testing.

The Problem We're Solving

Currently, our reports are showing those dreaded "NA" messages in the detailed analysis section. It's like having a puzzle with missing pieces – not very helpful, right? We need to fill in those gaps with some juicy analysis.

Expected Behavior

Imagine a report that gives you concrete examples like this:

Items that contribute to a high level of Flexibility are:
- Alternative transportation options
- Alternative suppliers available
- Quick response to market changes

Items that contribute to a low level of Flexibility are:
- Long-term fixed contracts
- Limited supplier options
- Inflexible production schedules

See? Now that's actionable information!

Current vs. New Reports: Bridging the Gap

One of the challenges is ensuring that our key insights align with the reports currently being generated. There's a bit of a mismatch, so we need to iron out the kinks. Ronald is going to provide examples, and we'll need to do some thorough testing to spot any discrepancies. It's all about making sure the old and new formats play nicely together.

Technical Deep Dive

Let's get a bit techy for a moment and talk about the nuts and bolts of what we need to do.

Data Analysis Requirements

For each dimension (R, C, F, V, A), we need to identify:

  1. High Contributors: Which survey questions or items have the highest scores?
  2. Low Contributors: Which ones are lagging behind with the lowest scores?
  3. Contextual Text: How do we translate these scores into human-readable descriptions?

Report Sections in the Spotlight

We'll be focusing on these sections:

  • Detailed dimension analysis
  • Key Insights section
  • Strategic recommendations

Data Sources: The Treasure Trove

We need to analyze the survey responses at the question level, not just aggregated dimension scores. This means digging into the raw data. Here's the kind of data structure we're aiming for:

company_name, dimension, question_id, question_text, score
"Company A", "Flexibility", "flex_1", "Alternative suppliers available", 4.5
"Company A", "Flexibility", "flex_2", "Quick response to changes", 3.2
"Company A", "Flexibility", "flex_3", "Flexible contracts", 2.8

Currently, we're working with aggregated scores like up__f, in__f, do__f. It's like having the forest but needing to see the trees!

Implementation Options: Choose Your Adventure

We've got a few paths we can take to get this done. Let's explore our options:

Option 1: Question-Level Data Analysis (The Ideal)

This is the dream scenario – working with raw survey responses that include question-level data. If we can get our hands on this, we can really nail the analysis.

# Analyze question-level data for Flexibility dimension
flexibility_questions <- survey_data %>%
  filter(dimension == "Flexibility") %>%
  arrange(desc(score))

high_contributors <- head(flexibility_questions, 3)
low_contributors <- tail(flexibility_questions, 3)

# Generate text
cat("Items that contribute to a high level of Flexibility are:\n")
cat(paste("-", high_contributors$question_text, collapse = "\n"))

cat("\nItems that contribute to a low level of Flexibility are:\n")
cat(paste("-", low_contributors$question_text, collapse = "\n"))

Option 2: Dimension Comparison (Using Current Data)

If we're stuck with the aggregated dimension scores, we can still make some magic happen. This involves comparing dimensions within a pillar.

# Compare dimensions within a pillar
upstream_scores <- c(
  Redundancy = up__r,
  Collaboration = up__c,
  Flexibility = up__f,
  Visibility = up__v,
  Agility = up__a
)

# Identify high and low
high_dim <- names(sort(upstream_scores, decreasing = TRUE))[1:2]
low_dim <- names(sort(upstream_scores, decreasing = FALSE))[1:2]

cat("Upstream strengths:", paste(high_dim, collapse = ", "), "\n")
cat("Upstream weaknesses:", paste(low_dim, collapse = ", "), "\n")

Option 3: Template-Based Text (The Interim Fix)

As a temporary solution, we can use predefined text templates based on score ranges. It's not as dynamic as Option 1, but it's better than nothing!

generate_flexibility_insights <- function(flex_score) {
  if (flex_score >= 4.0) {
    high <- c(
      "Strong alternative supplier network",
      "Quick adaptation to market changes",
      "Flexible contracting arrangements"
    )
    low <- c()
  } else if (flex_score < 3.0) {
    high <- c()
    low <- c(
      "Limited supplier alternatives",
      "Rigid contract terms",
      "Slow response to changes"
    )
  } else {
    high <- c("Moderate flexibility in some areas")
    low <- c("Room for improvement in adaptability")
  }
  
  return(list(high = high, low = low))
}

Task Breakdown: Let's Get Organized!

To tackle this project, we'll break it down into phases:

Phase 1: Data Detective Work

  • Check Data: Does question-level data exist in cleaned_master.csv?
  • Identify Columns: Which columns hold the detailed survey responses?
  • Map Questions: How do we link survey questions to dimensions?
  • Document: Let's make sure we have a clear record of our data structure.

Phase 2: Analysis Time!

  • Choose Approach: Which implementation option are we going with?
  • Write Code: Time to craft some R code to identify those high and low contributors.
  • Generate Text: Let's translate those scores into human-friendly text.
  • Format Output: We need to make sure the output matches the current report style.

Phase 3: Report Makeover

  • Modify Report: Let's tweak ResilienceReport.qmd to include the contributor analysis.
  • Replace "NA"s: Bye-bye, generic messages! Hello, detailed text!
  • Ensure Placement: The text needs to land in the right sections of the report.
  • Consistent Formatting: Let's keep the formatting consistent with the rest of the content.

Phase 4: Insight Validation

  • Compare Reports: How do the new reports stack up against the old ones?
  • Spot Discrepancies: Let's identify any differences between the two.
  • Ronald's Examples: We'll need Ronald's input here.
  • Adjust Logic: Time to fine-tune our logic to match the expected insights.
  • Multi-Company Testing: Let's test this with multiple companies to ensure it's robust.

Phase 5: Testing and Refinement

  • Full Database Test: Let's run this on all companies in our database.
  • Verify Text: Is the text meaningful and accurate?
  • Edge Cases: Let's look out for missing data, ties, and other tricky situations.
  • Ronald's Approval: We need the final stamp of approval.
  • Documentation: Let's update our documentation to reflect the changes.

Acceptance Criteria: What Success Looks Like

To know we've nailed it, we need to meet these criteria:

Detailed Analysis Section

  • High Score Contributors: Shows the specific items driving high scores. βœ…
  • Low Score Contributors: Shows the specific items pulling scores down. βœ…
  • Meaningful Text: The text is actionable and makes sense. βœ…
  • No "NA"s: No generic messages allowed! βœ…

Key Insights Section

  • Aligned Format: Matches the current report format. βœ…
  • Data Matching: Aligns with the actual data in the report. βœ…
  • Ronald's Validation: Ronald has given it the thumbs up. βœ…
  • Examples Tested: We've tested this with the provided examples. βœ…

Coverage

  • All Dimensions: Redundancy, Collaboration, Flexibility, Visibility, Agility – all covered! βœ…
  • All Pillars: Upstream, Internal, Downstream – we're looking at the whole picture! βœ…

Examples Needed: Ronald, We Need Your Help!

Ronald, your input is crucial! We need:

  1. Current report example with desired contributor text
  2. Survey questions mapped to each dimension
  3. Expected Key Insights format
  4. Any known discrepancies between old and new reports

Data Requirements: What We Need to Work With

If Question-Level Data is Available

company_name,dimension,pillar,question_id,question_text,score
"Company A","Flexibility","Upstream","Q1","Alternative suppliers",4.5
"Company A","Flexibility","Upstream","Q2","Quick response",3.2

If We're Limited to Aggregated Data

We'll need to create a mapping like this:

flexibility_factors <- list(
  high = c("Alternative suppliers", "Quick response", "Flexible contracts"),
  medium = c("Moderate adaptability", "Some alternatives"),
  low = c("Limited options", "Rigid terms", "Slow response")
)

Related Issues: Staying on Top of Things

We need to keep in mind these related issues:

  • Report quality and accuracy
  • Data structure and schema
  • Survey question mapping

Priority: This is a Big Deal!

HIGH – This directly impacts the report quality and its usefulness. We need to make this happen!

Scrum Roles: Who's Doing What?

  • Product Owner (Ronald): Needs reports with meaningful insights that match the current format.
  • Developer: Needs survey question data or a clear mapping to dimensions.
  • User/Researcher: Needs actionable insights about specific improvement areas.
  • Survey Respondent: Their detailed answers should be reflected in the analysis.

Final Thoughts: Let's Do This!

  • We're waiting on those examples from Ronald.
  • More testing is crucial to iron out those Key Insights discrepancies.
  • This might require some changes to our data collection and processing pipeline.
  • This is critical for report quality and stakeholder value. Let's make it awesome!

So, there you have it, guys! A comprehensive look at how we're going to enhance our reports with detailed contributor analysis. Let's roll up our sleeves and get to work!