Aera and AWS: Decision Intelligence Drives Supply Chain Decision Agility
Across the manufacturing industry, businesses are experiencing increasingly frequent supply chain disruptions which extend the time to finish and ship products. The impact is felt downstream as customers are now being told to wait months for larger items such as refrigerators. Though demand for these goods is skyrocketing, businesses are unable to satisfy orders or accurately predict when the next shortages will take place. As a result, most struggle to remain competitive in the market.
In this increasingly uncertain and complex reality, businesses have grown organizationally siloed, and their technology architectures are fragmented. Despite data visibility and analytics improvements, decision making is often inaccurate and reactive, and leaders struggle to keep up with the speed of decisioning required. A new solution is needed – one that leverages AI to dramatically increase decision agility so that companies can maintain the pace of execution required today.
Decision Intelligence: Addressing Operational Latency
Decision Intelligence automates not the task, but the decision-making process itself. It does so by accessing data cross-functionally; applying AI to surface risks and opportunities and predicting business outcomes; digitizing the decision-making process to evaluate possible actions; and engaging users in the form of recommendations or executing the actions autonomously.
In doing so, Decision Intelligence addresses the three types of latency that are common to any operation run in today’s architectures:
- Access to Data — Analyzing and producing what-if scenarios is time-consuming, and this complexity grows exponentially when scaling the data inputs, constraints, and models involved.
- Data Analysis — Analyzing and producing what-if scenarios is time-consuming, and this complexity grows exponentially when scaling the data inputs, constraints, and models involved.
- Decision Execution — The execution of decisions made often involves manual work, such as updating parameters and records across multiple systems or communicating with external parties.
Aera and AWS: Collaborating to Deliver Decision Intelligence
The Aera Decision Cloud™ helps businesses vastly improve decision agility. Leveraging AWS’ cloud technologies, Aera ingests data from multiple ERP systems, harmonizes the datasets, augments them with external information such as weather or commodity prices, and makes the data readily available to virtually any use case at stake. The process takes only a matter of days or weeks to set up, and can be scheduled to continue populating information in real time from that point forward.
By digitizing the process and making use of artificial intelligence (AI), Aera can perform data analysis in order to augment and even automate decision making. With Aera, users can dig as deeply as needed into the data, down to the transaction level – as if they were looking at the source system from which the data was originally pulled.
Aera Decision Cloud™ integrates the four pillars of Decision Intelligence
Finally, once a decision is made — either by the user accepting a recommendation surfaced by Aera or letting Aera take action — Aera can write back the instructions necessary to carry out the decision in the source systems. From updating a safety stock level to sending a new purchase order to a supplier, the execution happens natively within Aera and AWS, eliminating the latency that is inherent to any manual activity.
With AWS’s flexibility and scalability, Aera breaks down the current barriers of decision making at scale in the enterprise, enabling thousands of decisions which leverage billions of data points to be made at machine speed and precision.
Decision Agility: More than a Competitive Advantage
Today’s leading businesses are harnessing the power of Aera and AWS as a competitive advantage to cut through increased complexity and uncertainty, speeding up how they react to disruptions. In tomorrow’s world, this level of responsiveness will be a necessity.