Sold by: Mphasis
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 |
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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
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- 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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