What are the most effective ways to use data analysis to improve your company's circular economy performance?

It can be a challenge for supply chains to know where to start! One way to use data is to attempt to determine the “length” of the supply chain for your most popular product (including all distance travelled by all the components added up). The process of doing this will be enlightening as it will be more difficult than perhaps anticipated- and if you can manage to do it, you can identify how to shorten or localize / distribute that supply chain to be closer to point of use.

Data analysis can be used for below supply chain cases for improving circular economy - 1. tracing the life cycle of materials, identifying areas for sustainable sourcing and waste reduction. 2. Evaluating energy usage to identify efficiency improvements opportunities and the integration of renewable energy sources. 3. Analying the environmental impact of your products from cradle to grave.

Another way to leverage data to better manage circular supply chains is to measure duration - that is, to time it takes for assets to flow through the supply chain. Not only new or serviceable assets moving forward to end users, but also unserviceable assets flowing from end users back through the supply chain to an appropriate level of repair/refurbishment/remanufacturing ... And how long these assets are out of service while undergoing restorative processes.

Understanding how your resources or materials move through steps like production, consumption, and waste dispoal is important to identifying circular opportunities. Data inputs could be waste generated, recycling waste, or the input of materials and the process or transportation behind them. This ties into lifecycle analysis which can help identify hotspots like resource use or energy consumption - or waste analysis which looks at whether your products can be recycled or upcycled. Work wth your suppliers to gather data on raw materials, sourcing, and transportation and then use data to predict your future needs and how you might adapt.

Leveraging data analysis offers numerous opportunities to enhance a company's circular economy performance. By analyzing resource usage, waste generation, and product lifecycle data, companies can identify inefficiencies and areas for improvement. This analysis enables the optimization of processes to minimize waste generation, increase resource efficiency, and extend product lifecycles through strategies like remanufacturing or recycling. Furthermore, data analysis can inform decision-making regarding sustainable sourcing, product design, and supply chain optimization, facilitating the transition towards a more circular business model and ultimately reducing environmental impact while enhancing economic value.

Conducting experiments is essential! A company can leverage simulation models to explore different scenarios, such as incorporating modular components or recycled materials, and use lifecycle assessment to quantify the environmental benefits of their circular solutions. They even conduct pilot projects to validate their ideas, collecting experimental data to refine and perfect their approach. The result? A company that not only minimizes its environmental footprint but also creates value and sets an inspiring example for others to follow – all thanks to the power of data analysis in designing circular solutions.

Using data analysis to engage consumers and shift behaviors in support of circularity is valuable. Analyzing consumer preferences, usage patterns, and attitudes can yield insights into designing appealing circular offerings, implementing effective take-back schemes, and running targeted awareness campaigns. Granular customer data can power personalized recommendations and incentives to drive circular consumption. Advanced analytics like AI will be increasingly important to optimize circular supply chains and decision-making in real time. Predictive models can dynamically match supply and demand for secondary materials, automate smart routing to maximize utilization of assets, and provide intelligence on emerging circular opportunities.

An interesting way to use technology here would be to create a digital twin. Digital twins are replicas of your physical assets like a building, facility, manufacturing chain, ecosystem etc. By creating a digital twin you can run scenarios and monitor performance, resource use, and waste and simular scenarios that deal with circularity to test their outcomes. This can help data driven decision-making.

There are internal and external stakeholders you need to convince and work with to improve circularity. Having a holistic approach means that you will bring together diverse stakeholders to share a vision and collaborate together. This allows each stakeholder to implement or support the practices where they can, so that overall impact increases. Internally - consider leadership (such as c-suite and board) and management, employees, and shareholders - how do you get them invested in the journey? Externally - how can you work with your customers, suppliers, communities, NGO groups, or even competitors - for example by setting industry standards or supplier terms of conditions - to increase impact?

Knowing the impact you want to achieve and what metrics and KPIs you want to use is important to define impact. Are you looking at rates of recycling? Revenue share? Upcycled products? Minimized waste? Reduced consumption or production? This might be different for each product that you have. How you are tracking this progress (ie using data dashboards and visualization) will be important in conveying the impact you have had.

Es inminente el trabajo colaborativo para lograr que la economía sea eficiente y sostenible. La creación de un ecosistema industrial no solo promueve la cooperación entre industrias (tradicionalmente separadas), sino que además proporciona mejoras importantes en la eficiencia energética y de recursos, favoreciendo el aprovechamiento de subproductos de una industria. El reto es crear soluciones cooperativas inteligentes que permitan optimizar el uso de los recursos.