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Supply Chain Analytics for Just-In-Time

import pandas as pd
data = pd.read_csv("data/orders_and_shipments.csv")
data

The data is initially explored to get an overview of it's characteristics with the help of the describe method.

data.describe()

Insight: The describe method tells us about the count of values, mean and standard deviation within the data, the quartiles and maximum values in each column. This is helpful as it provides perspective into the data.

In order to ensure the analysis is thorough and complete, it is essential to verify if the dataset has any missing values.

data.count()

Insight: There are no missing values and the data is of integer type, which is perfect for our analysis.

Goals of The Analysis:

Through this analysis, the performance of the supply chain department is analyzed to evaluate the performance and identify opportunities for improvement. This is done by considering the following factors:

  1. Delays of orders
  2. Customer Order cycle time
  3. Inventory Analytics.

Calculating the delay dimension

The Delay has been calculated as the Difference between the Shipment Day and Order Day and the difference between the Scheduled Days.

data['Delay'] = (data['Shipment Day']-data[' Order Day ']) - data[' Shipment Days - Scheduled ']
data.head()

Insights on Delays:

#Identifying the maximum delay of a product
data['Delay'].max()

Insight: There are products that are getting delayed by 4 weeks, which is not good.

More information about products with the most delay

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