VIMAL JOSEPH MANAVALAN

Turning data
into better
decisions.

I connect quality analytics, vehicle data, and practical automation to help teams see the bigger picture—and take the next right step.

Explore my work LinkedIn (opens in a new tab)
Tokyo / Yokohama, JapanData science & quality analytics
BUSINESS CONTEXTUD TRUCKSISUZU Current assignmentA little about me
01 / ABOUTA PRACTICAL PERSPECTIVE

Close to the data.
Closer to the
problem.

Good analysis starts with a better question.

I work at the intersection of data, automotive quality, and everyday business needs.

My background is in quality assurance and warranty analytics at UD Trucks, with a current assignment at Isuzu in Japan. I bring together claims, quality issues, and vehicle information to support investigation, reporting, and preventive-quality activities.

I enjoy turning complex requirements into something people can actually use: a clearer dashboard, a dependable report, a more consistent workflow, or a well-structured dataset. My focus is not just the analysis, but what happens after it.

Based in
Tokyo, Japan
Industry
Commercial vehicles
Core focus
Quality intelligence
Working style
Practical & collaborative
02 / EXPERTISEDOMAIN KNOWLEDGE MEETS TECHNOLOGY

A toolkit built
for real work.

From understanding a quality issue to getting the right information into the right hands.

01

Warranty & quality intelligence

Connecting claims, parts, symptoms, and quality issues to make investigation and recurring-problem analysis more consistent.

  • Warranty analytics
  • Issue matching
  • Quality KPIs
02

Business intelligence

Building usable data models, dashboards, and reporting workflows that help teams explore the questions behind the numbers.

  • Qlik Sense
  • Qlik NPrinting
  • Power BI
03

Workflow automation

Replacing repetitive reporting steps with structured processes, scheduled distribution, and practical exception handling.

  • Power Automate
  • SharePoint
  • Scheduled reporting
04

Connected-vehicle insights

Working with diagnostic codes, vehicle information, and telematics trends to support quality monitoring and investigation.

  • Vehicle data
  • DTC analysis
  • Trend monitoring
05

Low-code applications

Bringing issue status, ownership, and follow-up information together in applications designed around the people using them.

  • Power Apps
  • Dataverse
  • Quality workflows
06

Applied AI exploration

Exploring document extraction and AI-assisted text matching, with human review and clear boundaries around the results.

  • Azure AI
  • Document extraction
  • Human review
EVERYDAY TOOLKIT

Qlik Sense / Power BI / Power Automate / Power Apps / Dataverse / Excel

03 / SELECTED WORKPROBLEMS, APPROACHES & CONTRIBUTIONS

Less complexity.
More clarity.

A selection of the questions I work on—and the practical ways I approach them.

Illustrative visuals. No internal data.

Showing all 4 projects.

MONITORING & AUTOMATION01

Making quality monitoring dependable.

Supporting the reporting layer around an existing soot-monitoring system, so quality teams receive relevant information in a dependable daily routine.

  • Qlik Sense
  • NPrinting
  • Report automation
Inside the project

The context

An engineering-developed monitoring system needed reliable report distribution and ongoing operational support.

My contribution

Maintained and improved the automated email and reporting process, monitored scheduled tasks, and worked on the timeliness of the information being delivered.

The takeaway

A more dependable reporting routine and more timely access to monitoring information. The original monitoring logic was developed by the engineering team, not by me.

WARRANTY ANALYTICS02

Connecting claims to known issues.

Bringing warranty claims and quality records closer together through consistent data preparation, contextual matching, and reviewable results.

  • Qlik Sense
  • Data preparation
  • Matching rules
Inside the project

The context

Claims and quality records can describe related problems in different ways, making consistent comparison difficult.

My contribution

Worked on normalising vehicle identifiers, aligning relevant fields, and applying structured matching rules across claims and quality-issue records.

The takeaway

A structured basis for reviewing potential relationships and supporting recurrence analysis. Suggested matches support investigation; they are not automatic proof of a root cause.

LOW-CODE & QUALITY WORKFLOWS03

A clearer view of quality follow-up.

Developing a practical low-code application to organise quality issues, status, ownership, and follow-up information in one place.

  • Power Apps
  • Dataverse
  • Process design
Inside the project

The context

Quality follow-up involves different records, owners, status changes, and planned closure information.

My contribution

Worked on a Power Apps canvas application backed by Dataverse, with structured issue information and practical validation for follow-up data.

The takeaway

An application-focused approach to making quality information easier to maintain and navigate, without exposing internal records or screenshots.

APPLIED AI / EXPLORATION04

From documents to useful data.

Exploring ways to extract and structure technical-document information so it is easier to search, review, and use in analysis.

  • Azure AI
  • Document Intelligence
  • Human review
Inside the project

The context

Technical documents and recall information contain useful details that can be time-consuming to organise manually.

My contribution

Explored document-processing approaches, structured output fields, and validation steps for moving from unstructured text to useful analytical inputs.

The takeaway

A practical exploration of AI-assisted extraction, with source traceability and human validation kept central. This is not presented as a fully autonomous production system.

Project descriptions are intentionally high-level. Visuals are conceptual, not screenshots of company systems.

HOW I APPROACH THE WORK

The goal is not more data.
It is a clearer next step.

  1. 01

    Understand.

    Start with the real business question and the people who need the answer.

  2. 02

    Connect.

    Bring the right sources together and make the underlying data consistent.

  3. 03

    Simplify.

    Turn the analysis into a dashboard, report, or workflow that is easy to use.

  4. 04

    Improve.

    Listen to feedback, check the outputs, and keep the process dependable.

04 / EXPERIENCEPROFESSIONAL CONTEXT

Automotive context.
Data-driven contribution.

Career profile on LinkedIn (opens in a new tab)
Current assignmentYokohama, Japan
ISUZU On assignment from UD Trucks

Quality analytics & cross-company collaboration

Contributing to quality-related data work in the Isuzu environment, connecting business questions with reporting, analysis, and opportunities for automation.

  • Support the use of warranty and vehicle information for quality investigations.
  • Collaborate with stakeholders on data requirements, monitoring, and reporting.
  • Explore practical ways to reduce manual effort and improve information flow.
  • Quality analytics
  • Vehicle information
  • Stakeholder collaboration
Home organisationJapan
UD TRUCKS Quality assurance

Data science, warranty analytics & preventive quality

Working with warranty claims, quality issues, and vehicle data to support reporting, recurring-issue analysis, supplier-recovery workflows, and preventive-quality activities.

  • Develop and maintain Qlik-based analytical and reporting workflows.
  • Improve data preparation and matching across claims and quality records.
  • Automate report distribution and support dependable scheduled processes.
  • Qlik Sense
  • Warranty intelligence
  • Automation

LET'S CONNECT

JAPAN

Good conversations.
Better possibilities.

Let's exchange ideas about quality analytics, practical automation, and making data more useful.

Connect on LinkedIn (opens in a new tab)

LinkedIn is the best way to get in touch.

DATA. QUALITY. AUTOMATION.Clarity is a team effort.