Practical data science tips to overcome operational challenges in transitioning to a circular economy
A circular economy is an economic system in which waste is minimized and resources are continually reused or recycled.
Imagine a world where your waste doesn’t end up in landfills but instead becomes the raw material for new products.
The transition from our current linear economy to a more sustainable circular economy is a major topic for many businesses.
What is holding us back?
As the current linear economic model reaches its limits, discussions around new circular economic models are becoming increasingly important.
These discussions mainly focus on
- Operational and business obstacles block transition
- Alternative strategies to increase the use of recycled materials
- Rental models to reduce the environmental footprint
As an analytics manager for a retail company, how can I support this transition?
Analytics experts can leverage system-generated data to overcome these obstacles by identifying opportunities to create a sustainable and profitable circular supply chain.
In this article, we will put ourselves in the shoes of an analytics manager responsible for supporting the operational transformation of a fashion retail company.
I. Transition to a Circular Economy
1. What is the environmental impact of a T-shirt?
2. Data-driven Process Design
II. Overcoming the Operational Challenges
1. The Opacity of Supply Chain Networks
2. The Low Residual Value of Used Products
III. Material Efficiency & Recycled Materials Usage
1. Raw Material Optimization…