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  KPI overview.

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  Efficiency Operation information for a given product in a given route.

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  Configurable KPI weightage

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  Weightage index per product

Inspiration

Application of Theory of Constraint (ToC) to the available production data in BMS API’s. Identify the “Most inefficient” operation for a given route of operations based on various data parameters available. Initiate a wave of innovation by pointing out improvement areas.

What it does

Identifies the most restrictive bottleneck which can be worked upon and eliminated thereby increasing the overall efficiency and throughput of the whole line without we touching other machines. It can be a good starting point for Six Sigma Projects

How we built it

We fetch various parameters related to the operation from various BMS API’s on a daily basis So, as to create a historical snapshot for ready reference. The parameters we collect include : classic yield, First pass yield, Cycle time, Percentage completion, Scrapped percentage, Defects per unit, asset downtime. We collect this information for all the products and their routes and operations. Then we have certain weightage points given to each of the parameters. Beneficial parameters (more the better. Like yield) have positive weightage. Parameters like defects, wastage etc have negative weightage. (lesser the better) Home screen shows the data for all products of the user for yesterday data for quick reference. From the other screen the user can request information for operations for a specific product for a specific route for a given date range. The micro service then takes the average for each of the parameters values for the date range. And then multiplies the average value by the weightage parameter. Then we do a sum of all weightages for a given operation to get a weightage index.� Finally we sort the operations based on the weightage index to find out the one with smallest value. This is the operation that is our least efficient operation and highlighted on the UI.

The UI also allows the weightage values to be changed as per need of a specific industry to make it reusable across industries.

Challenges we ran into

Absence of referential integrity among all the modules-EPI,EA& WIP. Lack of proper documentation consumed lot of time in the understanding of the APIs.

Accomplishments that we're proud of

Complete exploration of the API Pointing out the errors in the API to the GE team Able to come up with a web application which points the most inefficient operation in a given route for a given product.

What we learned

Brilliant Manufacturing API & the various terminologies used in the manufacturing industries. Brilliant Manufacturing API helped us explore the various challenges faced in the manufacturing industries, thereby motivating us to develop solutions which can help the industry to reduce the inefficiency by leveraging Predix platform.

What's next for Operation Constraint Analysis (OCA)

Adding more parameters like waste,idle time etc and developing better algorithm on the basis of those parameters. Taking a step further to suggest ways to reduce the bottleneck

You can access the web application at the following link: https://constraintanalysis-webapp.run.aws-usw02-pr.ice.predix.io
Login credentials: username: oca_user password: oca_user

Built With

• predix