accenture_challenge

Datathon 2022 - UPC - Supply Chain Resilience

2022-11-12

Challenge

Understand main root-cause affecting supply chain deliveries and predict product delays based on historical inbound/outbound orders of the company. Providing visibility of supply chain resiliency to maximize customer service levels

What is it expected from the technical challenge?

• Provide data insights based on descriptive analytics of historical data
• Regression model to predict likelihood of order delay

Evaluation Criteria

What will be measured?

• Business case presentation outlining descriptive insights of main drivers’ toward order delays. How those insights can be translated to business actions and value proposition.

• Predictive model to get likelihood of order delay. ROC Curve (AUC) evaluation metric for given test data set. A Kaggle Competition have been created for teams to submit and test their model results. Please sign-up to the competition and follow the instructions

Data

orders.csv

Transactional historical data of the company supply chain inbound/outbound shipments

• order_id (string): unique identifier of transactional order from port inbound to final destination. Primary key of data set.
• origin_port (string): location of port where order imports arrives.
• 3pl (string): Third-party logistic company id used for distribution, warehousing, and fulfillment services.
• customs_procedure (string): Type of procedure to be used in the imports legal process
• logistic_hub (string): city name of company logistic hub address. Intermediate step between origin_port and customer
• customer (string): city name of customer destination address
• product_id (string): unique identifier of final product
• units (integer): order size quantity
• late_order (boolean): target variable, if 1 the order_id have been tagged as a late delivery, 0 is on-time

product_attributes.csv

Master data of product unit weight

• product_id (string): unique identifier of final product
• weight (integer): product weight per 1 unit in grams
• material_handling (integer): Classification id for product safety risk and risk of damage e.g. fragile, toxic, flammable.

cities_data.csv

Geographic coordinates of cities involve in the supply chain. Including distance between pair of cities

• city_from_name (string): City of location starting point
• city_to_name (string): City of destination location
• city_from_coord (tuple): Coordinates in (latitude, longitude) of city_from
• city_to_coord (tuple): Coordinates in (latitude, longitude) of city_to
• distance (float): kilometers between the pair cities

test.csv

Same as orders.csv but variable late_order has been truncated. This is the target variable

Starter Script

[link to notebook] Example of a python code that reads all data sources and fit a naive average model to predict likelihood of an order arriving late. Notebook generates a submission file, based on the test data set, as an example of the expected file be uploaded to Kaggle Competition

FAQ

1. What is the phisical flow path of the supply chain? Inbound orders arrives at first stage at the origin_port. The order import is processed following a custom proceduce and it is sent to the logistic_hub. Later on, the order is transported to the final destionation of the customer.

2. Is the product_id in product_attributes.csv and orders.csv a 1:1 relationship? Yes, product_attributes.csv is a master data file, having product_id as primary key and attibutes weight and material_handling fix value for each product.

3. Is it the material_handling a ranking category? No, data has been annonimized, values represent a category type not a ranking i.e value=3 does not mean it is more complex/riskier to manipulate the product compare to value=1

4. Are all cities in orders.csv available in cities_data.csv? Yes, all cities represented on columns origin_port, logistic_hub and customer are shown in the file. Notice that city could be either on city_from_name or city_to_name. File contains a unique pair combination between all cities and it is the same distance from city_a -> city_b as city_b -> city_a

5. What does it mean to have a empty value in logistic_hub column for a specific order? Orders with missing info in logistic_hub are "direct shipments", going for port to customer directly.

6. What should be the format of my submission file? Starter script shows an example of expected rows and columns of file. Please, make sure submission file have all and unique order_id as in the test set and target variable late_order is a numerical value between 0 and 1. File must be comma-separate and does not include 'index', only columns order_id and late_order