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Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Mphasis Optimize.AI Process Discovery

By: Mphasis Latest Version: 3.9

Process mining based process discovery and bottleneck analysis solution

Product Overview

Workflows such as customer journeys and service delivery are complex multi-step processes and process managers are tasked with meeting process KPIs such as Cycle time, Queue lengths, Failure rate, Compliance metrics etc. which require continuous optimization of such processes. This solution helps analyse the process by mining workflow logs and generates process maps at path and time level. These process maps can be used to identify happy path, deviations from STP, compliance deviations and process bottlenecks which can help identify process interventions for optimization.

Key Data

Version
By Mphasis
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Input

Supported content types: text/csv

The solution requires workflow logs as input. E.g. customer journey in form of web server logs, process logs from workflow management systems such as PEGA.

Following are the mandatory flelds:

  1. CASE_ID: Unique identifier of a request/journey e.g. E-comm order iD, loan ID etc.
  2. ACTIVITY_ID: Activity Identifier/Activity Name performed for each CASE_ID e.g. INVOICE GENERATION, KYC etc.
  3. TIMESTAMP: Timestamp for a unique CASE_ID/ACTIVITY_ID combination.

Output

Content type: application/json

The output provides two separate images as described below:

1.Process Flow - Path Level Analysis:This image provides the activity flow during process execution through a network graph. Each node of the network represents an action and each edge represents the frequency of requests between 2 activities.

This information helps the process manager: a. Understand the activity flow during process execution b. Identify the happy path c. Identify the exception scenarios d. Identify the deviations from Straight Through Processing

2.Bottleneck Analysis - Time Level Analysis:This image output provides the time flow during process execution within a process through a network graph. Each node represents an action and each edge represent total time (sum of processing time for starting action and waiting time between the actions)

This information helps the process manager: a. Identify the bottlenecks within a process from recent historical data b. Do Root Cause Analysis to uncover the cause of bottlenecks
c. Identify the exception scenarios d. Identify the deviations from SLA/Standard Operating Procedure e. Optimize systems/resources to improve process throughput

Resources

Additional Resources

End User License Agreement

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Support Information

Mphasis Optimize.AI Process Discovery

For any assistance, please reach out at:

https://www2.mphasis.com/AWS-Marketplace-Support-LP.html

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Refund Policy

Currently we do not support refunds, but you can cancel your subscription to the service at any time.

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