Porter-Tableau

About This Project

The Porter Delivery Time Analysis dashboard provides a concise yet comprehensive overview of delivery performance within the Porter service framework. Through interactive visualizations and key metrics, stakeholders gain valuable insights into delivery times, regional variations, driver performance, and predictive analytics. This analysis empowers decision-makers with actionable information to enhance operational efficiency, optimize resource allocation, and improve customer satisfaction.

Problem statement :

The lack of a comprehensive analysis framework to assess delivery performance, identify root causes of delays, and implement targeted improvements is a pressing concern for Porter's management. Therefore, there is an urgent need to develop an analytical solution that provides actionable insights into delivery times, regional trends, driver performance, and predictive analytics. By addressing these challenges, Porter can streamline its delivery operations, enhance service reliability, and maintain a competitive edge in the rapidly evolving delivery industry.

Datasets :

Data Consist of Jan-Feb, 2023 Monthly placed and delivered orders data generated by porter, officially published by porter organization.

Analysis :

Delivery Time Distribution:

Visualizes the distribution of delivery times, allowing stakeholders to understand the typical time it takes for deliveries to be completed.
Utilizes histograms or box plots to showcase delivery time ranges and any outliers.

Delivery Time Trends:

Displays trends in delivery times over time periods (daily, weekly, monthly).
Helps in identifying patterns, seasonality, or fluctuations in delivery performance.

Regional Analysis:

Breaks down delivery times by regions or areas served by Porter.
Enables comparison of delivery performance across different geographical locations.
Helps in identifying areas where improvements may be needed.

Delivery Time vs. Order Volume:

Examines the relationship between delivery times and the volume of orders.
Determines if there are any correlations between order volume and delivery efficiency.
Helps in resource allocation and capacity planning.

On-Time Delivery Rate:

Calculates the percentage of deliveries that were completed within the expected timeframe.
Offers insights into Porter's ability to meet delivery commitments and customer expectations.

Driver Performance:

Assesses individual driver performance in terms of delivery times.
Identifies top-performing drivers and areas for improvement among others.
Provides insights for training and incentive programs.

Delivery Time Predictive Analytics:

Utilizes predictive models to forecast delivery times based on historical data, weather conditions, traffic patterns, etc.
Enables proactive management of delivery operations and better resource allocation.

• Analysis says that order delivered for bubbletea in Jan to Feb, 2023 in 47.3 average delivery time. which leads to gain average amount of $1.7k.

• In last block of horizontal bar chart we can see the last 10 days sales of bubble tea and above that the area variation over average order value.

• Analysis says that order delivered for vegetarian food in Jan to Feb, 2023 in 46.7 average delivery time. which leads to gain average amount of $2.6k.

• In last block of horizontal bar chart we can see the last 10 days sales of bubble tea and above that the area variation over average order value.

• Whereas, in extreme right side we can see each day quantity of orders of vegetarian food people ordered from porter delivery.

Conclusion :

In conclusion, the analysis of Porter's delivery performance has provided valuable insights into the factors influencing delivery times and operational efficiency. Through a comprehensive examination of delivery time distributions, trends, regional variations, driver performance, and predictive analytics, several key findings have emerged.

Firstly, it is evident that delivery times vary significantly across different regions, highlighting the need for targeted interventions to address regional disparities and optimize resource allocation.

Secondly, while certain drivers consistently meet or exceed delivery expectations, others may require additional training or support to improve their performance and ensure consistent service quality.

Overall, this analysis underscores the importance of data-driven decision-making in enhancing delivery performance, optimizing operational efficiency, and ultimately, improving customer satisfaction. By leveraging the insights gleaned from this analysis, Porter can implement targeted strategies to address identified challenges, streamline its delivery operations, and maintain a competitive edge in the market.

Porter Delivery - Time analysis

About This Project

Problem statement :

Datasets :

Analysis :

Conclusion :