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Project Overview

As part of a data storytelling and analytics project, I analyzed logistics performance data from Olist, a large Brazilian e-commerce platform. Olist operates without its own fulfillment centers, instead relying on third-party sellers for delivery. I assessed whether a warehouse investment strategy could improve delivery speed, reduce freight costs, and enhance regional performance.

Using geospatial and transactional data from the Olist dataset, we formulated and explored three business questions that examined the relationships among delivery time, shipping cost, and revenue efficiency across Brazilian regions.

Objective

  • Evaluate the potential of strategic warehouse placement to improve delivery efficiency

  • Analyze regional freight costs, order density, and delivery times to determine investment priorities

  • Examine the economic feasibility of warehouse expansion vs. alternative efficiency strategies

  • Deliver data-driven recommendations for Olist’s long-term logistics improvement roadmap

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Warehouse Strategy & Logistics Optimization for Olist

Approach 2: Cost Optimization by Delivery Distance & Freight Charges

Question:
Which Brazilian states present the highest opportunity for freight cost savings by optimizing shipping distances or establishing localized warehouses?

This analysis explored the relationship between shipping distance and freight cost, with the goal of identifying high-cost regions that may benefit from logistics optimization or warehouse expansion. Using Tableau visualizations, two perspectives were analyzed:

  • Scatterplot showing distance vs. cost by state

  • Geospatial map showing average freight cost per customer state

Key Findings

  • Distance and cost are only weakly correlated: some short-distance deliveries were more expensive than long-distance ones, indicating influence from other factors (e.g., infrastructure, accessibility).

  • Northern and northeastern states (e.g., Amazonas, Rondônia, Pará) show the highest freight costs, but also low order volumes, suggesting that full-scale warehouse investment may not be justified.

  • Southeastern and southern states (e.g., São Paulo, Minas Gerais, Rio de Janeiro) have high order volumes and relatively low costs, signaling scalability potential.

Suggested Strategy

  • Remote, high-cost regions with low demand

→ Explore alternative logistics models like shared distribution hubs or demand-triggered delivery schedules instead of full warehouses.

  • High-demand, low-cost regions

→ Focus on scalability strategies such as last-mile delivery innovation, peak demand planning, and logistics capacity expansion.

Final Recommendation:

Based on the comprehensive analysis of delivery time, freight cost, and revenue-to-cost efficiency across Brazilian states, a phased logistics optimization strategy is recommended. This strategy balances regional order demand, infrastructure readiness, and economic feasibility to guide investment decisions. The key recommendations are as follows:

  • Prioritize Logistics Investment in High-Volume, Moderately Inefficient States

States such as São Paulo (SP), Rio de Janeiro (RJ), and Minas Gerais (MG) consistently demonstrate high order volumes with relatively efficient freight cost structures (freight costs account for 12–15% of total order value). These regions offer the greatest potential for scalable improvements through:

  • Warehouse expansion or delivery optimization

  • Last-mile delivery innovation

  • Proactive capacity management for peak demand periods

These regions represent the best balance of volume, revenue, and cost-saving potential and should be prioritized for logistics investments.

  • Consider Secondary Investment in Mid-Tier Regions

States like Rio Grande do Sul (RS) and Paraná (PR) show moderate order volumes and delivery inefficiencies. While they do not match the top-tier states in demand, strategic improvements, such as route optimization or shared hubs, can still yield meaningful performance gains. These regions are suitable for secondary investment consideration.

  • Avoid Full-Scale Investment in High-Cost, Low-Volume States

Regions such as Pernambuco (PE), Ceará (CE), and Pará (PA) show disproportionately high freight-to-revenue ratios (17–22%) despite low overall order volume. Full-scale warehouse investment in these regions may not be economically viable without:

  • External funding or government support

  • Long-term growth projections or regional development incentives

Instead, flexible logistics models, including third-party partnerships, decentralized fulfillment, or regional consolidation, should be explored.

  • Adopt Agile, Region-Specific Logistics Strategies

Where warehouse investment is not justifiable, alternatives such as:

  • Route optimization

  • Cross-docking or shared delivery networks

  • Data-driven scheduling based on seasonal demand

could provide more cost-effective and responsive logistics solutions tailored to each region’s conditions.

Approach 3: Analysis of Order Delivery Costs and Revenue-to-Shipping Cost Ratio by Region

Question:
Which regions have above-average delivery costs per order and a relatively high shipping cost-to-revenue ratio? Would establishing a warehouse in these areas help reduce costs?

This analysis uses three visualizations to evaluate where high freight costs are not only present but may also significantly impact revenue:

  • Map: Highlights which customer states have above or below-average delivery costs.

    • 🔴 Red = Above-average freight cost

    • 🔵 Blue = Below-average freight cost

  • Bottom bar chart: Shows total order value (price + freight) for all states, indicating their overall commercial significance regardless of freight efficiency.

  • Top-left bar chart: Focuses only on states with above-average freight costs, showing their total revenue to determine if investment in these regions is justified.

Key Findings

  • Cost-to-Revenue Disparity Across Regions

    • Wealthier southeastern states (e.g., SP, RJ, MG, RS, PR) absorb logistics costs better.

      • Freight cost ratios: 12–15% of total order value.

    • Northern and northeastern states (e.g., PE, CE, PA) show freight ratios nearing 18%, despite generating much lower revenue.

  • Freight Cost vs Revenue Value

    • Some high-cost states like PE, CE, PA also show high shipping cost-to-revenue ratios (17–22%) but generate only a fraction of SP’s revenue.

    • For instance:

      • SP: ~6 million BRL revenue, 12.14% freight cost share

      • PE: ~320 thousand BRL revenue, 18% freight cost share

  • Strategic Prioritization

    • High-cost, low-revenue states are less ideal for warehouse investment despite inefficiencies.

    • States like SP, RJ, MG are still top priorities due to high commercial value and room to optimize cost structures further.

Approach 1: Major cities' order density and delivery time analysis

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Question:
Where are delivery delays most common, and is demand sufficient to justify a warehouse?

To identify high-priority regions for warehouse expansion, order density by city and average actual delivery time by state were analyzed, using a combined geospatial map and bar-line visualization. This approach allowed us to surface regions with both high demand and long delivery durations, key indicators of potential logistics inefficiency.

Key Findings

  • São Paulo (SP) processes the highest order volume (20,945 orders) with an average delivery time of 8.71 days, making it the most efficient among high-demand states.

→ While no immediate intervention is needed, long-term improvements in delivery speed could unlock further growth.

  • Minas Gerais (MG) and Rio de Janeiro (RJ) show moderate-to-high order volumes (5,915 and 6,392 orders respectively) but suffer from longer delivery times:

    • MG: 11.87 days

    • RJ: 15.34 days

→ Their geographic proximity and combined demand suggest that a shared warehouse location could improve performance across both.

  • Rio Grande do Sul (RS) and Paraná (PR) also show room for improvement, with delivery times of 15.49 days (RS) and 11.97 days (PR) despite moderate order volumes (~2,700 each).

→ These regions could be considered as secondary investment targets.

  • Bahia (BA): stands out due to long delays (up to 18.18 days), even though order volume is only moderate (~1,759).

→ Indicates potential infrastructure gaps that warrant further investigation, though cost-effectiveness depends on future demand growth.

  • Northern and northeastern states such as Pernambuco (PE) and Amazonas (AM) show high delivery times but low order density, making them low-priority for immediate investment.

Suggested Strategy

​We recommend a phased warehouse investment approach based on demand and delivery inefficiencies

  • Priority 1:  MG and RJ – High demand with suboptimal delivery time, SP

  • Priority 2: RS and PR – Moderate demand and poor performance

  • Priority 3:  BA – Consider for future investment if demand increases

Approach & Key Findings

Appendix: Report

View Report
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