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IBM Design

Asset Analytics Storytelling
  • STORYBOARDING

  • WIREFRAMING

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To be able to highlight the potential of Einstein Analytics in order to better visualise asset health of companies digitally.

Team 

1 designer, 1 product owner, 1 business analyst, 2 solution architects, 1 data scientist

My Role

Storyboarding, Use case sequencing, conceptualisation wireframing, low fidelity mock up

Timeline

Aug 2020 - jOct 2021

Project Goal  

This project highlights the capabilities of salesforce Einstein in enabling the client to improve understanding of asset health and performance, based on insights from historical data, to better target maintenance.

Problem Statement

APPROACH 

 

There are multiple resources needed to maintain asset health of machines at off shore oil rigs. To simplify this by using the analytics capabilities on their manually entered data through Salesforce Einstein, it was important to understand their present scenario scenario and pain points faced .

How might we enable bp to improve understanding of asset health and performance, based on insights from historical data, to better target maintenance work?

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DATA

Refreshable data sets provided by the source for real time analytics

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LENS

Choice of measures, dimensions and filters of the collected data to display desired information

Multimedia

DASHBOARD

The visualisation of data to generate timely and relevant insights

"To simplify understanding of concept with storyboards and build logical wireframes  with provided use cases. Further build low fidelity mock ups encompassing the solution and its value.

AS- IS SCENARIO

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•Technicians spot issues with equipment readings during their rounds

 

•Technicians make a note to raise issue in a separate system once they complete their work

•Supervisors schedule preventative maintenance without insight into real time asset health

 

•When assets fail a cascading effect leads to deferred production

•Cost consequence of unexpected / unanticipated repair work

Lack of insight around optimal equipment selection

Higher PoB required to meet maintenance demands

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DATA

DECODING USE CASES  

 

There were 9 use cases identified by the Data science team which were on the basis of data provided by the client. Grouping them into logical sequence and aligning them to the hierarchy of events helped build clarity on the flow of interfaces to be built.

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LENS

The team generated 09 unique capabilities that the PoC could feasibly demonstrate in Salesforce Einstein, based on our assumptions and discussions with the client.

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TO-BE SCENARIO

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•Technicians continue with their day-to-day work with better scheduled maintenance

•Collected data is fed into analytics solution used in control room

•Asset health is actively monitored

 

•Supervisors and M&E teams reflect on insights related to asset status, causes of failure, location, dependencies and service history

•Maintenance work is optimised to target priority assets

•On shore teams generate reports and make cross region comparisons

 

•Insights enable better asset selection decisions

•Long-run PoB reductions from reduced maintenance demands

Multimedia

DASHBOARD

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LOW FIDELITY MOCKUP

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A dashboard presents at a glance summary information (equipment info, RAG status, last service information etc). This prevents the need to refer to multiple source systems for all required information.

 

 

Clicking on assets in the table brings up 6 tabs with specific information relating to that asset.

The views allows for deeper investigation to identify potential deferral risks and monitoring of scheduled work.

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To comply with my non-disclosure agreement, I have omitted and obfuscated confidential information in this case study. All information in this case study is my own and does not necessarily reflect the views of IBM.