Continuous Supply Chain Accelerator
Capture an end-to-end view of your supply chain with TIBCO’s Continuous Supply Chain Accelerator. Gain insight into customer demand changes and trends using sophisticated modelling tools and visualizations in TIBCO Spotfire. Link product stock levels to anticipated demand and optimize stock allocation for distribution to minimize costs. Track deliveries of products to retail and customer locations all in real-time.
TIBCO Spotfire® TIBCO® Streaming
TIBCO Enterprise Message Service
TIBCO Patterns Search
TIBCO Spotfire Analyst
TIBCO Spotfire Server
TIBCO Streaming Artifact Management Server
TIBCO Component Exchange License
The Continuous Supply Chain Accelerator contains components that allows users to apply historical sales data in building demand models. Models may be adjusted to acccount for sales campaigns to see how demand is influenced. Product stock is ordered and allocated to distribution centers and retail locations based on demand. An optimization model allocates retail locations to the best distribution center using an algorithm that minimizes total driving distance, while accounting for available warehouse capacity. Delivery routes are calculated to minimize costs by combining deliveries to multiple locations in a single trip. Driving directions are generated and deliveries are tracked in real-time.
The list of Supported Versions represents the TIBCO product versions that were used to build the currently released version of this accelerator. We expect newer versions of the TIBCO products will also work. Please see the wiki page for the accelerator for possible further details around product versions.
Accelerators are provided as fast start templates and design pattern examples and are supported as delivered. Please join the Community to discuss the use and implementation of the Continuous Supply Chain Accelerator.
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Continuous Supply Chain Accelerator
The Continuous Supply Chain Accelerator allows users to model product demand based on historical sales data, and plan for stock levels based on anticipated business. It provides tools to optimize routes and deliveries to retail locations and customer premises, plus real-time tracking of vehicle-based deliveries. It includes components for visualization of moving vehicles, rules to detect delays, and alerting for entry/exit from geofences.
May 11, 2020, release of Continuous Supply Chain Accelerator 2.2.0.
Today, everything is connected and every participant in a global supply chain must access data. So it is essential to lower the barrier between artificial intelligence and human intelligence. With open source at the core and democratizing business intelligence through self-service, an intelligent nervous system is now available to anyone. This augmented intelligence enables a shift from reactive to proactive management of all supply chain areas. Digital twins allow us to predict future system states, anticipate problems, model alternative scenarios and choose an optimal solution. Humans better understand that digital fabric and are able to act in real time.
Typically, planning is the most data-driven process in the supply chain, using a wide range of inputs from Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) planning tools. There is now significant potential to truly redefine the planning process to sense and respond to billions of events a day, in collaboration with suppliers, to make real-time demand and supply adaptation a reality.
Transportation firms have used analytics to improve operations for years to optimize routing and reduce wait times. But most existing analytics are based on historical data, and new possibilities help companies monitor and respond to changing conditions in real-time data from connected land, air and sea vehicles, shipping environment sensors, real-time order flow, supply chain geoanalytics, live traffic patterns and continuous weather forecasting and the rescoring of predictive models.
This accelerator is an implementation based on the Connected Vehicles Accelerator with the addition of demand planning analytics and modelling. At the heart of the real-time components is the Trip. This is a journey consisting of several stops operating on a schedule, which has dependency on the resources of Vehicle, Crew, and Consignment.
The Continuous Supply Chain captures data from existing internal systems, and combines it with real-time feeds from these resources and processes. In addition, it can capture real-time feeds from third party data providers such as weather and traffic. Accelerator rules analyse this data and produce automated actions, advisories to operations staff, and alerts to outside parties. The current state of the network is displayed in true real-time on an operations dashboard, and near real-time using analytics tools.
By aggregating all this information in one place, the accelerator gives unique insight into supply chain operations that is not available in any other single system.
Benefits and Business Value
Making a supply chain more real-time gives business the ability to be more agile and react to competitive and market pressures. Being able to make changes quickly and innovate through a phased implementation approach can deliver near-term value by leveraging existing ERP and SCM infrastructure and tools, and forms the foundation for future projects.
Supply chains are constantly in motion so the first phase of supply chain nervous system adoption is obtaining a 360-degree streaming or near real-time view of the data that impacts supply chain assumptions and forecasts. Real-time analytics and simulation tools provide streaming or near real-time insight to stakeholders to any element that can impact the supply chain, including orders, package scans, inventory updates, in real time. Predictive data science models can be scored with streaming data science against this real-time feed of data and explored by supply chain management experts.
Virtualized data and real-time visibility is just the start. The next phase introduces key elements for scale: dynamic learning, data curation and automation. Dynamic learning is the secret sauce of supply chain innovation. Algorithms applied to streaming data yield smarter supply chain decisions and situational awareness. This algorithmic awareness is the pinnacle of supply chain innovation power. Data creation introduces a culture of curation to metadata management to trace lineage and manage assets and analytics assumptions. With real time analytics in place, automation with streaming data can begin. The best place to start is to automate insights that business users can use to better empower them to see and act on changing factors that impact the supply chain.
The scaling phase of this nervous system focuses on how to scale the center of excellence, enterprise architecture and cloud-hybrid architecture, and edge computing. Enterprise scaling is outside the scope of this paper, but considered insofar as the technology innovations below are expressly designed to future-proof the evolution of data, automation and AI in a global enterprise.
The accelerator includes a demonstration called Distribution Logistics. This shows demand planning for a chain of coffee shops in the Bay Area. Once demand is known for each store, then an optimization model allocates retail stores to distribution centers using constraints such as maximum capacity and minimizing total distance driven. Once stores are allocated, another algorithm groups them into delivery routes. This is again based on an optimization algorithm that takes into account maximum shift length and vehicle capacity. Driving directions are generated using TIBCO Geoanalytics web services. Then the progress of one day's worth of deliveries can be monitored in real-time as the vehicles move from store to store.
TIBCO software products and versions used
|TIBCO Enterprise Message Service||8.5.1|
|TIBCO Patterns Search||5.5.0|
|TIBCO Spotfire Analyst||10.9.0|
|TIBCO Spotfire Server||10.9.0|
|TIBCO Streaming Artifact Management Server||1.5.0|