Sold by: Mphasis
Deployed on AWS
Process mining based operational KPIs
Overview
Operational efficiency insights are locked within process execution data stored in the form of workflow/event logs. Improvement of operational efficiency requires continuous monitoring of specific operational KPIs. The current solution mines operations log data to describe a process in terms of Time and Flow deviations for users’ transactions/requests for a given time duration. These KPIs capture the details of variation in process behaviour and help identify the interventions needed to manage efficiency and cost.
Highlights
- The solution takes operational log data and measures the following KPIs: o Percentage(%) deviations from Straight through processing (STP) and Happy Path o Average throughput time o Average no activities per case o Number of cases with high throughput time o Percentage(%) contribution of Top 5 paths
- In a traditional operations management system, calculation of these “must have” KPIs can be highly inaccurate, time and resource consuming. The solution provides “on the fly” calculation of these KPIs with minimal cost and human intervention. These KPIs can help operation executives to manage the process behavior like in order fulfillment 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 each host used to run this AI process-mining model. Pricing is organized around two run modes and the AWS instance type you pick. Batch mode processes data in scheduled jobs. Real-time mode serves live requests. Within each mode, you choose from many instance families, such as general-purpose, compute-optimized, memory-optimized, and GPU types. Larger instance sizes carry higher hourly rates. Real-time mode offers more instance choices, including memory-optimized and local-storage options. Your total cost depends on which mode, which instance, and how many host hours you run.
Top-of-mind questions for buyers
One host hour is one hour that a single AWS instance runs the model. Billing measures elapsed run time on the instance type you select. If you run two instances for one hour, that counts as two host hours. Partial hours meter to the running time of each active instance.
Software charges accrue only while an instance actively runs the model. Batch mode meters host hours during scheduled jobs, so stopped instances between jobs do not add software charges. Real-time mode meters host hours while the endpoint stays running. Underlying AWS storage or resource fees may apply separately.
Batch mode meters host hours only during scheduled processing jobs, suiting periodic event-log analysis. Real-time mode meters host hours while the endpoint stays available to serve live requests, so charges continue as long as the endpoint runs. Both bill per host hour on the instance type you choose.
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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: application/json NOTE- csv file can be converted to json using the function given in notebook Following are the mandatory fields:
-
ACTIVITY_ID: Activity Identifier/Activity Name performed for each CASE_ID e.g. INVOICE GENERATION, KYC etc.
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CASE_ID: Unique identifier of a request/journey e.g. E-comm order ID, loan ID etc.
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TIMESTAMP: Timestamp for a unique CASE_ID/ACTIVITY_ID combination.
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Straight Through Path (STP): STP is standard flow of process as defined in Standard Operatring Procedure (SOP).
Output
Content type: application/json The output generates the following KPIs:
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Business defined Straight Through Path (STP).
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Percentage deviation from STP – Determines what % of cases are deviating from business defined Standard Operating Procedure (SOP).
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Happy Path - Determines the Happy Path i.e. the most taken path.
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Percentage deviation from Happy Path – Determines the % deviations from Happy Path.
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Average Throughput Time – Determines average cycle time across requests.
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Average number of activities per case – Determines average number of activities per case indicating possible rework/deviation statistics.
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Number of cases with high Throughput Time – Determines closure rate of cases i.e. cases with Throughput Time more than twice of standard deviation from median.
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Percentage contribution of Top 5 paths - Determines % distribution of cases across different paths.
Invoking endpoint
AWS CLI Command
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.csv --content-type text/csv --accept application/ out.csvResources
-
- Input MIME type
- application/json, text/plain, text/csv
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
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AWS infrastructure support
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