How is data changing supply chain? Supply chain relies on data to provide transparency and intelligence for organizations to “optimize relationships, reduce environmental impact, and adapt to disruptions in real-time”.
To enable data in supply chain, it requires organizations to follow these 3 core processes:
Supply chain design
Redesigning and transforming product innovation to reduce the supply chain and sustainability costs. Organizations need to design waste out and be mindful of the materials used – packaging, suppliers, production processes, and energy consumption. Supply chain design has to be actively conscious of the processes and networks involved; designed to reduct waste and remain sustainable.
Supply chain monitoring
Organizations have to understand where their supplies are coming from and be involved in every supply chain process – logistical or environmental. Reinforcing on the sustainable process will need organizations to monitor their sources – water, deforestation, agriculture, and mining. Monitoring these processes and network interactions requires a scalable data repository and integration of different data sources.
Supply chain execution
In order to integrate data, machine learning and artificial intelligence into these processes, we need to:
- Build the collective data models: putting every part of the supply chain into a model
- Differentiate and monitor risk: risks should be categorized accordingly by companies so that most of the scenarios covered
- Monitor sustainability: to promote a circular economy, the sustainability effort of one company is not enough, it should be an effort from the whole supply chain. Businesses should be aware of the emission rates or energy consumption not only of their own but of their partners as well.
- Leverage AI/ML algorithms: AI and ML is the future technology that is very useful to innovate products, design supply networks, predict risks and manage disruptions.
These require a comprehensive digital supply chain platform to boost the supply chain’s resilience and sustainability:
- A digital twin of the supply chain where we can predict, model, and assess risks.
- User access to the digital twin copy of the supply chain. Every appropriate personnel within the supply chain should have access to this platform, on all of their devices for timely notifications so that they can analyze data and communicate internally or externally.
- Supply chain simulation and optimization – data, AI, and ML algorithms will do its job by creating simulation and providing optimized solutions for every scenario.
- Supply chain partners – a network for enterprises’ internal systems that needs a holistic visualization to manage the supply chain risks and sustainability.