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💡 Newskategorie: AI Nachrichten
🔗 Quelle: towardsdatascience.com
Use data analytics to simulate the impact of a circular model on the CO2 emissions and water usage of a fast fashion retailer
A circular economy is an economic model that aims to minimize waste and maximize resource efficiency.
It involves designing products and processes focusing on longevity, reuse, and recycling.
Some companies have implemented a subscription model where customers pay a regular fee to access a product or service for a specific period.
The objective is to reduce the environmental impact along your product's life cycle; a rented product is produced once and can be reused several times.
How much CO2 emissions reduction can we reach with a (circular) subscription model?
You reduce the resources and energy needed for manufacturing but you may add CO2 emissions for the logistics of your rental service.
As a Supply Chain Data Scientist, you can build simulation models to assess the effectiveness of these initiatives.
In this article, we will simulate the impacts of several scenarios of circular subscription models on emissions reductions and water usage of a Fast Fashion Retailer.
Scenario
Distribution Network of a Fast Fashion Retailer
We will take the example of a Fast Fashion Retail company with 10 stores in the city of Shanghai (PRC).
The stores’ inventory is managed by distribution planners using an ERP
- Stores are replenished by a Central Warehouse
- Central Warehouse is replenished by factories
We will use the model designed for the previous article about Green Inventory Management considering the following assumptions
- 10 stores locations
- 365 days of sales transactions
- 3,300 active SKUs with 400 SKUs included in the circular model
- Inventory Periodic Review Policy Rule: 2 days
- Delivery Lead Time from Warehouse for Circular Items: 2 days
- Cleaning & Inspection at Warehouse: 1 day
- Return to Warehouse Lead Time: 2 days
In short, that means your stores are replenished every two days by the Central Warehouse.
When an item is returned after the rental period, it takes two days to ship it back to the warehouse.
Another day is needed to inspect and clean. And finally, it will be delivered back to a store after 2 additional days.
Inventory Management x Rental Model
For every order coming from stores, we will apply the First-In First Out (FIFO) principle.
Taking the example above, these four items returned from stores are available for order after cleaning and inspection.
Because store 2 ordered first, the first three units that arrived in the warehouse stock will be shipped there.
💡 Additional Insights
- That means an item can be rented in several stores along its life cycle.
- If the inventory of rented products is too low, orders are completed with new items.
Life Cycle Assessment: Linear vs. Circular Model
The objective is to estimate the impact of the circular model on
- Total CO2e emissions of your Supply Chain (kg CO2e)
- Quantity of water used to produce and deliver items to stores (L)
In the circular model example, a single item is purchased and used five times.
The total footprint is including a single full cycle and four times the return process.
We can then estimate the savings using the formula below,
💡 Additional Insights
- Savings will be impacted by the percentage of items reused
- The same calculation will be done for water usage.
- Emissions and water are taken from the master data.
- The additional impacts are estimated using the parameters listed below.
- If there are no rented items in stock, new items are shipped to the store for replenishment.
Let us now will simulate the distribution flows considering
- Rental durations of 2, 7, 14 and 28 days
- Sales period of 6 months
Simulation
Rental Period of 7 days
Now that we have built our model with the assumptions listed above.
We can start to explore the results with a rental period of one week and see the impacts on CO2 emissions and water usage.
What is the percentage of new items used?
💡 Insights
- During the first 12 days, the inventory of returned items is zero, so the store is using new items for rental.
- When you have peaks of volume like on day 16, the accumulated inventory of returned items cannot meet the demand so you new items to meet the demand.
This ratio, which we’ll define as a new metric called the percentage of circularity (%), is an important parameter influencing the footprint of your circular model.
During the first 12 days, the footprint of your rental model is the highest as we are using new items.
This can be easily explained by looking at the volume of returned items.
Indeed, we can see that the first batch of rented items is returned on the 8th day.
After 5 days for pick-up, warehouse delivery, cleaning and store shipping they are available on day 13 for new sales.
From this day, we have a balanced distribution of rented and returned items which provides enough inventory to have >75% of items reused.
How many rental cycles per item on average?
In this donut chart, we can visualize the distribution of items considering the number of rental cycles they are used during the 6-month period.
For instance, 9.8% of items have been used 10 times.
💡 Insights
- Only 110,458 unique items are used to fulfil 951,856 rental transactions which makes an average of 8.61 rental cycles per item
- The fact that some items can reach 14 cycles may raise the question of the maximum usage before disposal.
- A non-negligible part of the inventory is only used a single time.
What impacts on emissions reduction do we have for each item?
Let us take an example of a coat rented 35 times using 10 unique pieces.
Linear model’s emissions (co2_linear in green) are considering the total footprint if your customers purchased these items. In this case, you’d need to produce and deliver 35 coats.
While the circular model’s emissions (co2_circ in orange) only include the production of 10 unique coats and the logistics for return management.
💡 Insights
- Emissions reduction will depend on the life cycle assessment of the item (total footprint from raw materials to delivery) and the percentage of items reused.
As we could expect, the amount of CO2e reduction is linearly linked with the number of cycles (i.e the number of times these items are reused).
What does impact the percentage of reused items?
Therefore, we would like (ideally) to reach 100% of rental transactions with reused items and limit the number of new items purchased.
When demand is extremely volatile, you usually face stock-outs in the inventory of rented products and you need to use newly produced items.
In the example above, the demand distribution is highly skewed.
💡 Insights
- 60% of the total demand for this reference is on the peak of day 105 when zero inventory has been built.
- Therefore the percentage of circularity (number of sales transactions fulfilled with reused items) is only 40%.
In this example, Garments 1018 is a high runner with a very stable distribution.
💡 Insights
- Except for the first days, the demand distribution provides enough flexibility to build an inventory of rented products (in your warehouse) to fulfil the demand with fewer new items.
- Therefore, we can reach 89% of sales transactions with reused products.
To sense the impact of the variability, we can introduce the Coefficient of Variation CV:
A demand distribution starts to be considered highly volatile for CV > 1,
💡 Insights
- 99.9% of items with CV<1 have a percentage of circular sales higher than 80%
- As we can see some items with CV > 1.5 with a percentage higher than 70% we can sense that it’s not the only parameter impacting.
And logically we can see the impact on the emissions reduction per item rented,
💡 Insights
- 100% of items with CV<1 have a reduction higher than 30 kg CO2e per Unit Rented.
Because we do not have control of the demand variability, let us see if we can improve the emissions reductions by changing the rental period.
Simulate for several rental periods
In the previous section, we simulated a rental period of 7 days.
We can now run the model with rental periods varying between two and twenty-eight days.
💡 Insights
- The reduction level is dropping when you increase the rental period.
- 2 days of the rental period: +4.5% of CO2 reductions vs. 7 days
- And we can see that directly linked with the percentage of rental with reused items.
It can be verified by looking at the percentage of circularity of items with a rental period of 2 days,
💡 Insights
- 99% of items with CV < 2 have more than 80% of their rental transactions fulfilled with reused items (versus CV<1 for rental period = 7 days)
It is clear that a short rental period provides more flexibility to deal with the demand variability.
Without any surprise, we can see the same trend with the amount of water saved,
With longer rental periods, item availability for a rental is dropping.
Thus, you are losing inventory flexibility to absorb demand variability.
That’s why, as you can see in the table above, your model requires more new items to meet your customer's demand.
And the impact is directly seen in CO2 emissions and water usage.
Conclusion
Results
Even in the worst-case scenario, a rental period of 28 days, we can reach a 60% of CO2 reduction and -74% of water usage.
That means the additional emissions due to the logistics of returned products are not offsetting the savings generated by reusing your products.
However, this is considering that your stores and logistics operations can manage this rental process for 400 items.
Additional parameters would be needed to have a complete assessment,
- Additional staff and systems to manage the returns flows
- Additional packaging or handling material needed?
Impact of Demand Variability
We can sense that the percentage of rental done with reused items is linked with the variability of your demand.
Assuming that we want to maximize this overall score, we probably should explore a solution of adapting the rental period with the demand variability.
For items that have a stable demand, we can have longer rental periods without impacting the performance.
Next Steps
To improve our analysis we should then focus on:
- Solution Design of the Logistics Operations Needed to Manage these flows
- Build an Optimal Policy to Set the Rental Period = f(Demand Variability)
- Investigate the Impact on Store Operations and Estimate the Capacity of Managing a Large Portfolio of Items
About Me
Let’s connect on Linkedin and Twitter, I am a Supply Chain Engineer using data analytics to improve logistics operations and reduce costs.
If you are interested in Data Analytics and Supply Chain, have a look at my website
Samir Saci | Data Science & Productivity
References
- Green Inventory Management Case Study, Samir Saci
- “Dry Cleaning and Laundry Services in the US” report by IBISWorld
- “Carbon footprint of garment cleaning and laundry services: A review” by T. Randell, M. Sohail, and M. Reynolds (Journal of Cleaner Production, 2016)
How Sustainable is Your Circular Economy? was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
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