Apromore Announces New Capabilities for AI-Driven Process Optimization

MELBOURNE, Australia–(BUSINESS WIRE)–Apromore, a leading provider of AI-driven process mining and simulation solutions, today announced new capabilities for enterprises to leverage predictive analytics to boost the effectiveness of customer-facing processes. Customer Experience (CX) and Operational Excellence (OPEX) teams can now easily spot friction points, errors and rework in customer interactions and make better decisions about steps to take to keep operational margins in check. The Apromore platform’s fully no-code interface allows analysts to build predictive dashboards without the need for technical resources, enabling teams to preempt performance degradation and to drill down into problematic customer touchpoints in near-real time, improving business resiliency and adaptability.

Marlon Dumas, Chief Product Officer at Apromore, said, “We continue to deliver on our vision of AI-driven process optimization, with an emphasis on capabilities that business teams can use in self-service mode, to drive day-to-day operational and customer excellence.” Dumas continued, “our AI-driven process optimization technology is already driving massive benefits in leading financial services organizations in Australia, the US and Europe. The latest evolutions in our product have been field-tested and helped our customers to enhance CX and OPEX metrics simultaneously.”

A leading financial organization uses Apromore to re-platform from a legacy system to Salesforce. Using simulation capabilities in the Apromore process platform, the organization determined the optimal number of resources to support the new products against different requirements and ensured superior customer experience prior to deployment. A utility company uses the Apromore platform to identify best practices and consolidate SAP enterprise resource planning (ERP) systems from mergers and acquisitions to improve operational excellence. The optimized procure-to-pay process will be migrated to S/4 HANA.

Key new capabilities

Custom Predictive Dashboards for Proactive Process Management – Predictive, no-code dashboards enable business stakeholders to visualize the predicted performance of the process in the near term. For example, users can now easily configure charts in a custom dashboard to display the number of cases predicted to end up with a negative outcome per country or type of customer and compare these metrics against historical ones to inform strategies for improvement.

New Metrics to Mitigate Process Defects and Monitor Customer Journeys – The Apromore platform’s new capabilities allow analysts to build custom dashboards in a handful of clicks to display performance statistics for each first-touch resource and first-touch activity. Process owners can then simulate the impact of introducing added resources or touchpoints and reduce the risk of ineffective changes. Additionally, the Apromore platform now provides an easy way to measure the impact of process errors. Rework, particularly rework arising from errors during the performance of tasks, is a common source of inefficiency in business processes. To help managers locate and quantify inefficiencies from rework, Apromore now supports common rework metrics such as total cost of rework and total rework duration, which are available on process maps, dashboards, filters, and KPIs (key performance indicators). The metrics are contextualized to support decisions on how to slash down the time and cost impact of rework, e.g., via automation.

AI-Driven Assessment of Process Automation ROI – The Apromore platform now generates a data-driven Process Automation Assessment Report (PAAR) to support better automation decisions with a detailed analysis of the impact of proposed automation. Apromore is introducing new AI techniques to discover and tune simulation models and run simulations of what-if scenarios where RPA (Robotic Process Automation) bots assist or replace human work. The outcome is a structured report that shows the impact of automating different activities in a process, with metadata that shows the effects of each automation intervention on the end-to-end process.

Two-Phase Approval Process for improved governance and auditing – Now it is possible to implement a stringent approval process for publishing assets including process models, which can be customized based on organizational needs, e.g. by involving two levels of approval. This capability supports regulated industries requirements in terms of assets governance and auditing.

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