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MuleSoft Accelerator for Manufacturing

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Use case 3 - Total productive maintenance

Easily sync equipment data between OSIsoft PI System, Amazon Redshift, Tableau, and Salesforce Service Cloud to resolve manufacturing equipment failures.

Contents

See also
- Mappings
- Downloadable assets

Overview

Two key manufacturing success metrics - operational efficiency and overall equipment effectiveness (OEE), directly depend on the health of the production equipment in use. Collecting, analyzing, and actioning factory floor data on an ongoing basis can be labor intensive and inefficient. This can result in either over-maintenance or worse, under-maintenance of expensive production equipment. Preventing equipment breakdowns in a capital-intensive business can have a significant impact on the bottom line.

The purpose of this solution is to leverage factory data to manage equipment maintenance in a timely and efficient manner. The solution helps manufacturing organizations deliver on their OEE goals by reducing downtime, improving capital expenditure, and avoiding accidents to make the workplace safer.

As a result, this solution streamlines maintenance of factory equipment in a manufacturing environment to result in improved overall equipment effectiveness and operational efficiency.

Use case description

This Accelerator sends data from OSIsoft PI System (a data historian that collects data from sensors, intelligent electronic devices (IEDs), distributed control systems, programmable logic controllers (PLCs), and lab instruments) to Amazon Redshift, a data warehouse product. Next, the Accelerator uses the in-built functionality in Tableau, a data visualization tool, to visualize the data. Once the equipment data is analyzed, business users can define logic in Tableau to trigger work order creation in the Service Cloud. Analyzing critical floor equipment data in near real-time improves the health of production equipment and drives overall operational efficiency.

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Glossary

TermDefinition
OSIsoft PIHistorian Database
Total productive maintenance (TPM)TPM (Total Productive Maintenance) is a holistic approach to equipment maintenance that strives to achieve timely resolution of manufacturing production issues
Amazon RedshiftAmazon Redshift is a data warehouse product which forms part of the larger cloud-computing platform Amazon Web Services.
Historian DatabaseA service that collects real-time data streams from industrial assets, smart devices, proprietary legacy equipment–remote or mobile." and logs to a database.

High-level architecture

Total productive maintenance

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Activity diagrams

Send shop floor data to Redshift

The diagrams below illustrate the sequence of steps to send shop floor machine data to Redshift in near real-time.

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Create work order

The diagrams below illustrate the sequence of steps to create work order in Service Cloud from Tableau dashboard.

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Use case considerations

Functional considerations

  • OSIsoft PI is the system of record for the shop floor machine data
  • Consumers can create work orders by clicking a link on the dashboard.

Technical considerations

This section lists the technical considerations and constraints on the solution design.

  • Tableau will call experience API for work order creation and pass required values as query parameters to use Tableau inbuilt functionality to call an API
  • OSIsoft PI is a system of record for machine data
  • Amazon Redshift API will use bulk operation fro inserting data into Redshift
  • Tableau will use OOTB capabilities for Slack and Email integration

Assumptions and constraints

  • OSIsoft PI has basic authentication enabled
  • Tableau will enable a link to call experience API for case creation

Systems involved

  • Salesforce Service Cloud
  • OSIsoft PI
  • Redshift
  • Tableau

Goals

Shop floor Machine data is available in the Amazon Redshift DB for Tableau consumption

Customers will have ability to create case in ServiceCloud from logic that is entered into Tableau by a business user leveraging Tableau’s in-built alerts functionality

Customers can click on the create case link and select the conditions and create the case in the Salesforce Service Cloud.


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Before you begin

bulb.png The Accelerators setup guide provides general information on getting started with the accelerator components. This includes instructions on setting up your local workstation for configuring and deploying the applications.

Processing logic

The processing logic below applies to total productive maintenance for a customer's shop floor. The machines keep on sending data to OSIsoft PI and OSIsoft PI keeps the data in a time series database. The process api is retrieved at a scheduled interval

The primary handling and orchestration of machine data retrieval and insert into Redshift will be implemented in the machine data process and respective system APIs.

The logic of this process for sending data to Amazon Redshift can be described as follows:

  1. A scheduler triggers a process API and calls the OSIsoft PI system API and retrieves the data from OSIsoft PI for a described time frame.
  2. The process API sends data received in the last step to Redshift system API and system API inserts the data to Amazon Redshift using bulk data insert.

The logic of creating case in ServiceCloud is described as follows:

  1. The customer clicks the create case/work order link on the dashboard and selects data from the drop down.
  2. This data from last step is sent to Tableau Case Experience API.
  3. Tableau Case Experience API calls the Service Cloud System API and creates the case in Service Cloud.

Successful outcome

After successfully completing the processing, the following conditions will be met:

  • Shop floor machine data is available in Amazon Redshift for Tableau consumption
  • Customers can click on the create case link to create a work order in Salesforce Service Cloud
  • Customers will have the ability to create alerts (email and Slack)using Tableau's in-built alerts functionality

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Mappings

Source type mapping

The following table summarizes how representations of a machine data from each system will be mapped to machine data types.

Source SystemObject TypeTarget TypesMapping Notes
OSIsoft PIMachine DataMachine_ID
Machine_Name
MachineTypeLocation
Measured_Entity
Measured_Value - e.g. 80, 1.88
Measured_Unit - e.g. Fahrenheit Hertz
Source - e.g. OSI-PI, IoT etc
Created_Datetime
Created_By
Updated_Datetime
Updated_By
Comments

Target type mapping

The following table summarizes how representations of a machine data from each system will be mapped from machine data.

Target Systemobject TypeTarget TypesMapping Notes
Amazon RedshiftMachine DataMachine_ID
Machine_Name
MachineType
Location
Measured_Entity
Measured_Value - e.g. 80, 1.88
Measured_Unit - e.g. Fahrenheit, Hertz
Source - e.g. OSI-PI, IoT etc
Created_Datetime
Created_By
Updated_Datetime
Updated_By
Comments

Downloadable assets

System APIs

Process APIs

Experience APIs

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Reviews

TypeCustom
OrganizationMuleSoft
Published by
MuleSoft Solutions
Published onNov 8, 2022
Asset overview

Asset versions for 1.3.x

Asset versions
VersionActions
1.3.0

Categories

Industry Vertical
ManufacturingNo values left to add
Solution
Accelerator
ManufacturingNo values left to add

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