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Scenario: Inventory Management for Retail
You are the Data Science Manager of an international clothing group that has stores all around the world.
The distribution team is managing the replenishment of stores from local warehouses.
Your colleague, the Inventory Manager, is in charge of setting the store replenishment rules in the ERP.
She has implemented a periodic review policy Order-Up-To-Level (R, S)
- The ERP is reviewing stores’ inventory levels (also called inventory on hand) every R days: IOH
- For each review, the gap between the inventory level and the target inventory S is calculated: S — IOH
- A Replenishment Order is created and transmitted to the warehouse with the quantity Q = S — IOH
The idea is to deliver the missing quantity to reach this target level.
After transmission, the order is prepared at the warehouse and delivered to your store after a certain lead time LD (days).
The target stock is defined to absorb the demand variability and the replenishment lead time to avoid stock-outs (empty shelves) at the store.
I will not enter the details of how to set these differences in this article.
However, if you need more information, you can find a detailed explanation in this article.
Among the key parameters of this rule is the time between two reviews, which will drive the frequency of replenishment in your stores.
What if we change this review period?
What is Green Inventory Management?
The review period is setting the frequency of store replenishment order creation.
- For R = 2 days: stores are replenished very frequently
You can set a lower target stock level to cover the demand during the review period. - For R = 15 days: stores are replenished less frequently
The order quantity per replenishment must be higher as your target stock level needs to absorb the demand during a longer review period.
On the left side, we have more store deliveries (with a lower quantity per shipment) for the same duration.
This will definitely impact the efficiency of your warehouse and transportation operations.
Can we estimate the impacts of these two different approaches on the CO2 emissions?
You can simulate these and help your colleague estimate the impact of her inventory rules on the consumption of cartons and CO2 emissions.
Impact on the carton usage
Items are delivered at the stores in cartons containing units picked individually.
If the store orders 5 units of the reference XXX, the operator will
- Open a box of 20 units and take 5 units;
- Take a new box and put these 5 units;
- Complete the box with other items ordered by the store;
We must use additional carton material to create these mixed cartons containing different items. (instead of shipping full cartons)
How many additional mixed cartons do we have to prepare?
You can calculate the total number of mixed cartons using the formula below.
These boxes (or mixed cartons) will require additional packing material that will impact your footprint.
With a higher replenishment frequency, the quantity per replenishment is reduced, and this situation can occur more.
Can we estimate the impact on the Transportation efficiency?
Impact on transportation emissions
As it is the main driver of CO2 emissions, you should also estimate the impact on the number of trucks used and their filling rate.
The review period impacts the number of deliveries during a certain period.
For instance, doubling the delivery frequency will
- Multiply the number of delivers for the same quantity replenished;
- Reduce the quantity per replenishment and potentially increase the space in trucks
How can we translate these insights in estimated CO2 emissions increase?
In the next section, we will translate these operational insights into a simulation model to select the optimal inventory rules.
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