Sold by: MphasisÂ
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.
Highlights
- This solution takes workflow logs as an input and generates process maps at path and time level.
- The solution can be used for happy path identification, deviations from STP, Bottleneck Analysis and Compliance analysis in any process based industry like in loan approval process or procurement process.
- Mphasis Optimize.AI is an AI-centric process analysis and optimization tool that uses AI/ML techniques to mine the event logs to deliver business insights. Need customized Machine Learning and Deep Learning solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $16.00 |
ml.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $8.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $16.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $16.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $16.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $16.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $16.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $16.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $16.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $16.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
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:
- CASE_ID: Unique identifier of a request/journey e.g. E-comm order iD, loan ID etc.
- ACTIVITY_ID: Activity Identifier/Activity Name performed for each CASE_ID e.g. INVOICE GENERATION, KYC etc.
- 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 throughputResources
- Input MIME type
- text/csv, text/plain, application/json
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
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