Ensuring that supply chains of data are prepared for future disruptions and problems.
COVID-19 has caused several supply chain disruptions, from the latest chip scarcity to the months last year when toilet paper was nearly difficult to find. The epidemic exposed the economic and human consequences of strained supply chains—but pandemics is not the only source of these disruptions.
Even before the epidemic, extreme weather occurrences, shifting geopolitical situations, access to raw resources, and environmental consequences all posed substantial hazards. To develop robust and sustainable supply chains in the future, we must account for all of these variables holistically.
We discuss why data management and insights generated by machine learning and artificial intelligence (ML and AI) are important to effective supply chain transformation in our whitepaper, How Data Is Driving Resilient Supply Chains. Too many supply chain models result in the waste of raw resources across the lifespan of the products that firms generate. They also rely on brittle networks that may be brought down if one link in the whole chain fails. A circular economy strategy is necessary, with agile, adaptable linkages across the supply chain; decoupling economic activity from the use of scarce resources, and designing waste-free processes.
In this post, we will cover some of the key aspects of our new whitepaper, such as how to build a robust and sustainable supply chain in practice.
Data is transforming supply chains.
Data is required in supply chains to offer insight and information across the network, allowing businesses to optimize connections, decrease environmental impact, and respond to disturbances in real-time. This entails rethinking three key processes:
Design Of The Supply Chain
Improving supply chain design entails changing both product innovation—which determines the majority of supply chain and sustainability costs—and network architecture. In the former instance, businesses want a more comprehensive understanding of which materials, ingredients, and components are being utilized; which packaging material is necessary; where supplies are being sourced; what manufacturing processes and energy usage are taking place; and so on. Businesses can only make point judgments in the absence of this understanding. Meanwhile, network design entails managing distribution networks as well as recycling and return processes—areas that, like those in product innovation, need full insight into data from many sources, as well as AI and ML models to make the data useful.
Supply Chain Management
Most businesses only have a relationship with their direct suppliers, which means they don’t know anything about their suppliers’ suppliers. Because of these constraints, it is hard to completely comprehend supply chain problems, whether logistical or environmental, throughout the lifespan of an enterprise’s product. Developing an efficient track-and-trace procedure thus necessitates a network of firms sharing information, and it may also involve sources such as satellites for monitoring deforestation, water, agriculture, and mining. Other technologies, including Blockchain, 5G, and sensors on pallets, are also crucial in providing secure, real-time connectivity to and insight into chains of custody and network interactions. Collecting and managing all of this data necessitates the use of a scalable data lake and the integration of several data sources.
Implementation Of The Supply Chain
This procedure involves risk forecasting and disruption management. As with the previous two key processes, there is a similar thread: bringing together data from various sources so that choices can be made holistically rather than in isolation and applying machine learning to the data to make judgments smarter and quicker. Companies nowadays, for example, prefer to make short-term choices primarily on cost implications, rather than taking a broader perspective of sustainability issues.
A number of data processing and management requirements emerge from these processes:
Create The Collective Data Models.
The first stage is to create a business network that comprises multi-tier suppliers, transportation partners, manufacturing partners, equipment makers, service providers, and customers. Similarly, all network products, components, and inputs should be modeled.
Risk Should Be Differentiated And Monitored.
Companies must distinguish three types of risks: natural (e.g., earthquakes, harsh weather); operational (e.g., factors influencing forecast accuracy, supplier shortages, inventory placement, and asset downtime); and financial (e.g., risk profiles of business partners).
Keep An Eye On The Long-Term Viability.
Most businesses have begun to monitor their greenhouse gas emissions, but in order to achieve a circular economy, businesses must understand not only the emissions they cause but also their energy consumption (including how much comes from renewable sources), as well as the emissions and energy consumption of their partners. Similarly, social responsibility should be prioritized, with businesses monitoring deforestation, human rights situations, mining consequences, and other variables that connect with their suppliers.
Make Use Of AI/ML Algorithms.
The present focus of risk management and sustainability procedures is on analytics and scoring systems; the next step is to employ AI and ML algorithms for product creation, supply network design, risk prediction, and disruption management. A multinational logistics company was able to cut annual fuel usage by 10 million gallons (about 38 million liters) and save over $400 million US dollars by utilizing AI to dynamically optimize fleet routing.
Creating A Digital Supply Chain For The Future
We can identify four pillars for architecting a comprehensive digital supply chain platform for sustainable and resilient supply chains based on the needs stated above:
The Supply Chain’s Digital Twin
Each company-specific twin, a digital representation of the physical supply chain, assists enterprises in seeing and understanding the relationships that comprise their supply chain, detecting opportunities for optimization, modeling and assessing different changes and planning for natural disasters or other disruptions.
The relevant supply chain workers should have access to the digital twin’s information on all of their devices, including the ability to receive alerts, evaluate data, interact with colleagues or business partners, and initiate simulations.
Simulation And Optimization Of Supply Chains
Resilient and sustainable supply chain operations pose substantial data issues, and AI and ML algorithms assist businesses in considerably reducing risks and meeting sustainability goals.
Partners In The Supply Chain
Few, if any, companies’ internal systems include all of the data types necessary to view and manage supply chain risk and sustainability comprehensively. Partner connections, as well as the flexibility with which they may be handled, are critical.