Unlocking Intelligence and Adapting at Speed with Machine Learning

Everything seems to work better with intelligent insights. Our time and resources are precious, and we need to use them wisely; as our world is more connected, technology can be used to address our needs with cognitive precision, making our lives run smoother and feel more complete.

Perhaps one of the best examples of these intelligent experiences is the navigation systems that come standard in most modern car models. From the perspective of the user, the dashboard appears smooth and straightforward. Our cars are connected to maps, and maps are linked to traffic reports. The route is created based on the car’s position and map information, and the arrival time is estimated. Drivers never see this behind-the-scenes minutia – instead, they just experience the pleasure of a worry-free trip, knowing the decisions being made are based on a broader understanding of real-time traffic flows than they can’t see or respond to.

Much like our cars, businesses need to run smoothly, as well as compete, innovate, and adapt faster. By supporting continuous learning, adaptation, and digital transformation, companies have a distinct opportunity to reinvent themselves with real-time insight. This model provides data that is not only more resilient and complete but also more unified and reliable.

Such deep connectivity empowers businesses to leverage connected systems to understand supply-chain dynamics deeply and respond to them quickly. Drivers can’t wait for delayed turn-by-turn directions, and neither can intelligent supply chains.

Driving with Insight

Understanding the twists and turns of the supply chain enables businesses to adapt to changing dynamics in real time. By detecting patterns and deriving alternate courses of action that would have been impossible to conceive years ago, machine learning can help monitor and predict demand in much the same way navigation systems manage expected arrival times. It can more accurately predict every move and step in the process.

The data from this predictive model empowers supply chain managers to assess delivery outcomes, capacity, inventory, and constraints to make adjustments that help ensure customers receive their orders as promised. Businesses can unlock this intelligence – often trapped in a hyperconnected environment of device sensors, robotics, and 3D printing – by embedding machine-learning capabilities from the core of their systems to the edge of every point of connection.

With a next-generation intelligent enterprise resource planning (ERP) suite, such as SAP S/4HANA, businesses can build this foundation to lead the way to new business models and highly adaptive processes. This unified data model supports machine learning by providing an intuitive platform that processes data in real time, consolidates transactions, and generates actionable recommendations to make decisions faster.

By breaking down data silos, the in-memory computing architecture of the intelligent supply chain immediately combines high-volume transaction processing and real-time analytical processes to enable game-changing capabilities such as:

  1. Detecting new opportunities faster to increase business value from existing systems by eliminating redundant data layers typically required by traditional relational database management systems
  2. Transacting processes and analyzing data all in one application to evaluate, report, simulate, and predict the outcome of any solution, innovation concept, or decision in real time
  3. Addressing decision support with custom-built redundant data layers such as secondary indexes, aggregate tables, and multiple versions of inventory information
  4. Enhancing the speed of insight and throughput for critical processes by simplifying the data model
  5. Innovating new business models and technologies on a broad scale across all line-of-business and industry solutions with streamlined data and processes
  6. Accelerating implementations on an economical scale with unified solution models
  7. Addressing many industry-specific perspectives from within a flexible core technology landscape
  8. Automating processes with signal-based data processing, text mining and predictive or simulation capabilities to take advantage of the Internet of Things (IoT), Industry 4.0, unstructured data, and Big Data
  9. Providing guided configuration capabilities based on learned experience models
  10. Delivering extensions through a consistent cloud platform compatible with the core

Machine learning gives supply chain leaders a distinct opportunity to accelerate and automate processes from order to delivery. With a digital framework for real-time data, on-the-fly analytics, and predictive simulations, businesses can go a step further in their transformation. They will not only help ensure their strategic goals are fully aligned, but also become a unified front that protects the bottom line, reputation, and future growth of the business.

David Sweetman is a senior director of Global Marketing at SAP.

This story originally appeared on the Digitalist.