Sold by: Mphasis
Deployed on AWS
Process mining based process discovery and bottleneck analysis solution
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 |
Dimensions summary
You pay by the hour for the compute instance that runs the process discovery model. Charges depend on which AWS machine learning instance you pick and how long it runs. Pricing splits into two modes. Batch inference processes data in scheduled jobs. Real-time inference serves live requests. Batch mode covers general-purpose, compute-optimized, and GPU instance families. Real-time mode adds memory-optimized, burstable, and additional GPU families for more instance choices. Larger instance sizes carry higher hourly rates. You scale cost by choosing a smaller or larger instance and by controlling total run hours.
Top-of-mind questions for buyers
One HostHrs unit equals one hour that a chosen AWS machine learning instance runs the process discovery model. Billing counts the time the instance stays active. Each instance type carries its own hourly rate. You multiply your instance's rate by the number of hours it runs.
Batch inference runs the model in scheduled jobs, so you accrue HostHrs only while a job processes data. Real-time inference serves live requests, so the instance stays running and accrues HostHrs continuously until you stop it. Real-time also offers more instance families, including memory-optimized and burstable types.
Charges apply per hour while the instance runs. A stopped instance stops accruing software HostHrs charges. For batch mode, charges stop once the scheduled job finishes. For real-time mode, the instance keeps billing until you shut it down. Underlying AWS resource fees may still apply separately.
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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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AWS infrastructure support
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