Enriching the supply chain business application
The supply chain reference architecture is a high-level view of a best-in-class supply chain landscape. It includes all the components that contribute to the major supply chain activities of plan, make, and deliver.
The architecture represents any combination of hybrid and multicloud deployments and emphasizes the major resource management areas of orders, transportation, and suppliers. It includes the modern view that the supply chain network’s function is to syndicate data between the major resource areas, warehouse the transaction data, and provide advanced analytics, AI, and data science functions on that data. Those functions, in turn, enrich the business application layer, which delivers value to all stakeholders across all enterprises that are involved in the supply chain function.
The architecture components are based on industry practices that support overall supply chain digital transformation and modernization.
Architecture diagram
Components
The following represents a typical set of components that are deployed in a supply-chain solution.
| Architecture components | How the component is used |
|---|---|
| Planning/Optimization | Planning and optimization applications focus on the forward-looking process of organizing resources to optimize the delivery of goods and services from the supplier all the way to the end consumer, taking into account supply and demand and other constraints. These applications typically also include simulation capabilities for “what if” scenarios, to allow experimentation with varying outcomes. |
| Inventory management | Inventory management applications encapsulate the process of ensuring the availability of resources involved in every step of the plan, make, and deliver activities of a supply chain. This can include everything from the raw materials that are involved in manufacturing to the finished goods ready for distribution. |
| Track and trace | Track and trace applications are similar to transaction insights applications in that they summarize the current status of a shipment by aggregating and collating across multiple objects and status updates that are involved in the shipment, with the additional ability to track the history of the shipment back to the source. Track and trace applications provide a “provenance path” for a good for such objectives as food freshness assurance, authenticity of origin, and audit trail of agencies, companies, and humans involved in a transaction. |
| Control tower | Control towers provide end-to-end visibility into the health of the entire supply chain, regardless of system or data source, typically in the form of statistics and key performance indicators. Alerts can be generated based on indicator thresholds, and thus control towers are also the launchpoint for issue resolution. |
| Store replenishment | Orders store stock based on current inventory and projected demand to prevent stock-outs. |
| Problem resolution | Whenever there is a disruption or issue within the supply chain, the problem resolution function provides an environment where all affected parties can collaborate on a resolution, often incorporating access to data and analytics in real time. |
| Transaction insights (O2C, P2P) | Transaction insights applications summarize the status of supply chain transactions, such as order-to-cash (O2C) and procure-to-pay (P2P), by aggregating and collating the hundreds of business objects involved in the transaction. Users of such applications can quickly understand the status of the transaction to help with such business functions as customer support and supplier relationship management. |
| eInvoice/Compliance | E-invoicing and compliance applications involve certifying and/or preserving the authenticity of the data being exchanged between multiple entities in the supply chain. For e-invoicing in particular, there are typically country-specific regulations on the exchange of electronic invoices between parties that must involve a third-party authenticator (signer). |
| Order fulfillment/management | An order management system encapsulates all processes and activities that are related to order origination and management, including customer orders, manufacturing orders and purchase orders. |
| Warehouse replenishment | A warehouse management system manages warehouse activities a business processes, including receiving, storing, picking, shipping, and inventory counting. |
| AI | Artificial intelligence (AI) applies advanced data analysis techniques that use logic-based techniques and mathematical algorithms to interpret data, generating alerts, events and insights from that data, in support of decision making and task automation. |
| Machine learning | Machine learning is an extension of artificial intelligence that can identify patterns and derive insights from data while also improving its own algorithms through lessons learned while analyzing the data. |
| Data science | Data science refers to discovering meaningful trends, patterns, and correlations across data from the Data Management layer. The results of this discovery are used by the supply chain applications to both inform business users and alert them to anomalies found in the data. |
| Blockchain | A blockchain is a digital ledger that records transactions in a secure, encrypted form that prevents the record from being removed or tampered with. In a supply chain, a blockchain can be used to support key business functions such as track and trace and multi-party collaboration. |
| Data management | Data management refers to the recording of business data and events as they flow through the supply chain network, for use by applications, analytics and machine learning. The intent is to sufficiently warehouse the business data and events for providing additional value to the business users in the form of insights and predictive analytics, for both improving the efficiency and value of the supply chain and to improve responsiveness to issues identified in the supply chain. |
| Connectivity | Connectivity refers to the function of the network responsible for electronically connecting to a party participating in the exchange of data in a supply chain, whether that connection be based on a data transfer protocol (for example SFTP) or API. |
| Internet of Things (IoT) | Internet of Things refers to the network of physical objects, like shipping containers, that contain sensing devices capable of transmitting the location and status of the object to a central data collection hub. For supply chains, IoT data collection provides visibility into real-time status, location, and state of shipped goods. |
| Transportation management system | A transportation management system handles the logistics and optimization of shipment unit definition, labor planning, shipment scheduling, including coordinating with 3rd party logistics providers and carriers. |
| Supplier provenance | Supplier provenance refers to the system responsible for tracking supplier performance, quality of output, compliance with regulatory standards, and general standing with the industry. |
| Supplier registry | A supplier information database, the supplier registry is meant to aid in the search, identification, and onboarding of suppliers to a supply chain network. |
| Enterprise resource planning | Enterprise resource planning involves the planning and management of all resources that are required for processing, shipping, and billing of orders as well as the forecasting the need and replenishment of supplies. |