InterSystems Supply Chain Orchestrator

Supply Chain Orchestrator is a solution that has been designed specifically to enable supply chain leaders and their teams to take full advantage of a comprehensive view of end-to-end supply chain data, from within their own enterprise, from suppliers, from logistics carriers and from customers. Data from all these sources are ingested into a supply chain specific, canonical data model (CDM) that includes API and Adaptors. This approach simplifies and speeds up the task of integrating data that doesn’t always adhere to industry norms and taxonomies, into rationalised, normalised unified data for ease of analysis by the underpinning data platform. Additionally, it utilises what InterSystems calls, a smart data fabric (see Fig). Unlike existing data fabric tools, this Smart Data Fabric is a fully integrated solution that takes the data fabric approach one step further by embedding a wide range of analytics capabilities, including data exploration, business intelligence, natural language processing, and machine learning directly within the fabric, making it faster and easier for organisations to gain new insights and power intelligent predictive and prescriptive services and applications. This is described by InterSystems as the ‘last mile’ of analytics and positioned as an essential capability for generating real value from analytics technology. We are inclined to agree.

Supply Chain Orchestrator comes with analytics cubes preconfigured with a variety of supply chain specific topics such as sales orders, shipments, purchase orders, inventory etc. All of these are easily configurable with no coding required and facilitate the development of business outcome based KPIs.

The underpinning capabilities for the Supply Chain Orchestrator solution are founded on InterSystems IRIS Data Platform – introduced in 2018 as a single, unified platform that is the evolution of the company’s Caché database, Ensemble interoperability platform, and its various analytics offerings. InterSystems IRIS is available for on-premises, cloud-based, and hybrid deployments, with Kubernetes support included.

The major elements of InterSystems IRIS include a horizontally and vertically scalable, multi-model, transactional-analytic database with full ACID compliance and immediate consistency; scalable and distributed application server(s); a Visual Studio Code-based development environment supporting several languages; a business process layer equipped with a rules engine, workflow and process orchestration; specific capabilities to support self-service-enabled analytics on structured or unstructured data; integration with streaming environments such as Apache Kafka; and on-demand access to data across multiple data sources via a data fabric architecture.

InterSystems IRIS stores data in multi-dimensional arrays. It supports relational, object (with full persistence, polymorphism, inheritance, and so forth, and with no requirement for object-relational mappings), document, and multi-dimensional models, and you can implement any number of these within the same environment, with full interoperability across these projections, and without any duplication of data. Note that you can read an array either vertically or horizontally, which means that you only need to store data once to support both transactions and analytics. This is a major differentiator for InterSystems.

InterSystems IRIS scales to accommodate large workloads and data sets on commodity hardware, with both database and application servers scaling out horizontally. Application code is decoupled from the persistence of data, which allows application servers to scale horizontally, independently of the number of shard servers, and to distribute workloads automatically, thereby supporting both performance and consistency. The environment is configured so that nodes are designated as query, transactional or hybrid servers, in order to cater for different workload resource requirements. Data ingestion can be parallelised directly to each shard server, providing high-performance ingestion for streaming data, and analytic queries can be pushed down to partitioned or sharded data tables, further increasing performance and resource efficiency. The software can also make direct use of graphics processors for pipelining and so forth.

Analytics capabilities provided by InterSystems IRIS include a new IntegratedML feature that allows you to create and use predictive models using automated SQL functions; support for PMML (Predictive Modelling Mark-up Language) and a server side Python runtime engine that allows native execution of predictive models; and a connector for leveraging Apache Spark-based machine learning and predictive models within the InterSystems IRIS environment (with parallel operations and high-speed connections from each of the shard servers into a Spark cluster). ‘Adaptive Analytics’ is another new feature that allows you to expose analytic data only once, in such a way that it can serve multiple use cases simultaneously. This is accomplished using a ‘virtual cube’ data model, an alternative to the OLAP cube, that can be assembled using a drag and drop interface and deployed to various business intelligence and visualisation tools, such as Tableau, Power BI, and Qlik (supported via an ODBC interface).

On that note, to facilitate the embedding of real-time business intelligence into operational applications, InterSystems IRIS includes a designer for creating dashboards; an analysis component, that can be employed by business users to explore and display relevant data; and an architect component, used to define your data model. For unstructured data, InterSystems IRIS includes natural language capabilities, and it supports Apache UIMA (Unified Information Management Architecture).

InterSystems IRIS provides several language options for development purposes. Apps that run on the platform directly can leverage SQL, Python, or ObjectScript (an in-house programming language) while external applications can also use Java, .Net or Node.js. Access to Embedded Python is a recent development and is a significant step forward in terms of access to trained developers (Python is more commonly used than ObjectScript) and programming libraries, without needing to sacrifice the performance, security, scalability and other benefits of an embedded approach. Notably, Python can run in the kernel directly on the data and is considered a ‘full peer’ to ObjectScript, meaning that the two can essentially be treated as interchangeable and cross-compatible within InterSystems IRIS.

InterSystems Supply Chain Orchestrator offers, not only the ability to provide powerful analytics, on a public cloud or on-premises, using data from existing systems, but also as an underlying transactional platform for newly developed microservices-based applications to run on. InterSystems has a number of publicly available reference cases, like SPAR Austria, a member of SPAR, the world’s largest food retailer consortium, which is a €4 billion company with more than 800 outlets and 600 SPAR merchants in Austria. SPAR Austria initially developed a complete warehouse management system utilising the InterSystems data platform for SPAR stores across Eastern Europe. This has now been extended to 1400 stores in Austria and is planned to be rolled out into Italy as well.

While SPAR used the IRIS data platform to develop, very rapidly, new custom applications, UST, a leading digital transformation solutions company required a different approach. It has integrated its Optum solution, based on SAP/Hana, with Supply Chain Orchestrator to deliver an Azure cloud-based Optimisation-as-a-Service that achieves enhanced supply chain orchestration and gains end-to-end visibility.

Such a hybrid real-time platform offers the opportunity to develop quickly new use cases to meet the never-normal environment of the 2020s. We consider the underlying InterSystems IRIS platform to be highly performant and scalable. It is, for instance, quite capable of processing transactions, indexing incoming data, and performing analytics on both real-time data and non-real-time data (that is, historical data and reference data) at scale and in real-time.

The platform also takes pains to make sure its analytics are not just highly effective in theory, but easy to build, access and use in practice. You can see this in practice with the specifically supply chain focused canonical data model and analytics cubes.

The Bottom Line

Recent developments to InterSystems IRIS, the development of a smart data fabric and introduction of InterSystems Supply Chain Orchestrator solution make for a compelling hybrid transactional/analytic offering for the management and optimisation of supply chains.