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Your current enterprise resource planning (ERP) and supply chain management systems are compatible with AWS Supply Chain, a cloud-based supply chain management tool.
Key Features of AWS Supply Chain
- Supply chain data lake – For the purpose of comprehending, extracting, and transforming various, incompatible data sets into a single data model, AWS Supply Chain sets up a data lake using ML models that have been previously trained for supply chains. Data from other sources, including your current ERP systems, such SAP S/4HANA, and supply chain management systems, can be ingested by the data lake. AWS Supply Chain employs machine learning (ML) and natural language processing (NLP) to link data from source systems to the unified data model, adding data from variable sources like EDI 856. With predefined but adjustable transformation recipes, EDI 850 and 860 messages are directly translated. AWS Supply Chain’s data lake will automatically receive data that you load into an Amazon S3 bucket from other systems.
2. Real time Visual Map – AWS Supply Chain uses a series of interactive, visual end-user interfaces built on a micro frontend (MFE) architecture to contextualize your data in a real-time visual map. The state of the inventory at each location is then highlighted by AWS Supply Chain, including the selection and quantity of current inventory and items that may run out of stock. By drilling down to specific facilities, inventory managers can examine the current inventory on hand, in transit, and potentially in danger in each location.
3. Insights – AWS Supply Chain automatically develops insights into potential supply chain risks (such as overstock or stockouts) using the extensive supply chain data in the data lake. It presents them with a real-time visual map. AWS Supply Chain leverages ML models developed using Amazon’s technology to produce more precise vendor lead times estimates. Supply planners might use these anticipated vendor lead times to alter static assumptions in planning models to lessen the danger of a stock-out or excess inventory. By choosing the location, the type of risk (such as stock-out or excess stock risk), and the stock threshold, inventory managers, demand planners, and supply chain leaders can also establish their insight watchlists. AWS Supply Chain will produce an alert if a risk is found, emphasizing the possible risk and any impacted sites.
4. Recommended Actions and Collaboration – To give inventory managers and planners advice on what to do if a risk is identified, AWS Supply Chain automatically assesses, ranks, and offers potential rebalancing solutions. The proportion of risk mitigated, the distance between facilities, and the sustainability impact are used to grade recommendation options. Supply chain managers can delve even deeper to examine how each choice affects other network distribution centers. AWS Supply Chain also continuously picks up knowledge from your choices to provide better recommendations over time.
AWS Supply Chain has built-in contextual collaboration features that can assist you in reaching an agreement with your coworkers and carrying out rebalancing activities. Teams can communicate more effectively and more quickly by messaging and chatting with one another. This reduces mistakes and delays brought on by poor communication.
5. Demand planning – To reduce unnecessary inventory expenditures and waste, AWS Supply Chain Demand Planning produces more precise demand projections, adapts to market situations, and enables demand planners to work across teams. AWS Supply Chain uses ML to analyze historical and real-time sales data (such as open orders), develop forecasts, and continuously tweak models to increase accuracy to reduce the manual effort and guesswork involved in demand planning. AWS Supply Chain Demand Planning also offers near real-time forecast updates so you can proactively adapt supply chain operations. It continuously learns from changing demand patterns and user inputs.
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AWS Supply Chain supports the following default user permission roles. You can also make unique user permission roles that contain a variety of permission responsibilities. Additionally, you can include particular places and goods.
- Administrator – Access to manage user permissions and create, view, and manage all data.
- Data Analyst – All data connections are accessible for creation, management, and viewing.
- Inventory Manager – Access to manage, generate, and view insights.
- Planner – Access to publish demand plans, generate views, and manage predictions and overrides.
AWS Supply Chain is a cloud-based platform that integrates data, offers actionable insights powered by ML, has integrated contextual collaboration, and can plan demand. AWS Supply Chain can be integrated with your existing enterprise resource planning (ERP) and supply chain management solutions without platform re-platforming, upfront licensing fees, or long-term commitments.
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1. What is AWS Supply Chain's Data Lake?
ANS: – The Data Lake used by AWS Supply Chain is a system that thoroughly arranges data from many sources into a single data model using machine learning and natural language processing. It can extract and transform data sets that are otherwise incompatible and can be used to ingest data from different sources, such as ERP systems, supply chain management systems, and EDI messages.
2. How does AWS Supply Chain's data lake handle EDI messages?
ANS: – AWS Supply Chain’s data lake uses predefined but adjustable transformation recipes to translate EDI 850 and 860 messages directly. Additionally, the system can add data from variable sources like EDI 856.
3. What is AWS Supply Chain Demand Planning?
ANS: – AWS Supply Chain Demand Planning is a system that uses machine learning to produce precise demand projections, adapt to market situations, and enable demand planners to work across teams. It analyzes historical and real-time sales data to develop forecasts and continuously tweaks models to increase accuracy, reducing the manual effort and guesswork involved in demand planning.
WRITTEN BY Swapnil Kumbar
Swapnil Kumbar is a Research Associate - DevOps. He knows various cloud platforms and has working experience on AWS, GCP, and azure. Enthusiast about leading technology in cloud and automation. He is also passionate about tailoring existing architecture.
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