OpsIQ
PORTFOLIO PROJECT
Live Demo
by Monil Raval · Product & Operations

Operations Intelligence Dashboard

Real-time simulation of Amazon EU fulfilment KPIs — the type of visibility I identified as missing and am building tools to solve.

Live Simulation Auto-Refreshing Amazon EU Context
Units Per Hour
Current Throughput
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Defect Rate
Error % This Shift
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Scan Compliance
Barcode Scan Rate
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Headcount Active
Associates On Floor
of 40 capacity
Throughput — Last 12 Hours units/hour
Defect Rate — Last 12 Hours % errors
Bottleneck Heatmap — By Station
Normal Warning Critical
Live Alerts
💡
Product Insight: This dashboard represents the tool I identified as missing after 400+ shifts at Amazon EU. Shift leads currently rely on lagging reports and verbal handovers. A real-time KPI system like this — connected to scanner data — would reduce throughput variance by an estimated 15–25% and allow proactive staffing decisions 45 minutes earlier per shift.

Built by Monil Raval — Product Owner & Operations Professional

This tool demonstrates operations data analysis, KPI framework design, and product thinking — all from direct Amazon EU experience. Available for Product Owner, Operations Analyst, and Digital Transformation roles across Germany.

Shift Demand Simulator

Model staffing needs for any shift scenario. Based on patterns observed across 400+ Amazon EU shifts.

Interactive Predictive Model
Shift Parameters
Simulation Output
Recommended Staff
Target UPH
Risk Level
Buffer Needed
Predicted Hourly Demand Curve
Adjust parameters above to see how demand curves shift. Peak windows indicate when buffer staff should be activated.
🎯
Why this matters for hiring managers: I built this simulator because I observed that staffing decisions at Amazon are made reactively — team leads request backup only when throughput is already dropping. A predictive model like this, even a simple rule-based one, would allow proactive staffing 60–90 minutes earlier, reducing throughput dips. This is the operational insight behind ShiftSense AI.

Bottleneck Finder — A3 Analysis

Structured root cause analysis of 3 real process gaps I documented at Amazon EU — presented in A3 format.

Real Observations Lean A3 Format
📋
Context: Over 400+ shifts at Amazon EU Graben, I systematically documented 3 recurring process bottlenecks using observation, informal interviews with team leads, and KPI pattern analysis. Below is my structured A3 analysis — the same format used in Lean and Six Sigma environments at Bosch and Toyota.
🔴 Bottleneck #1 — Shift Handover Information Loss
DEFINE — Problem Statement

During shift transitions (early→late, late→night), critical operational context — active bottlenecks, equipment issues, associate performance flags — is communicated verbally with no standard template. Information loss rate is estimated at 40–60% per handover.

MEASURE — Observed Impact
Repeat issues per shift
3.2 avg
Recovery time (min)
22 min
Throughput dip at handover
-18%
ANALYSE — Root Causes (5-Why)
Why 1: Incoming TL doesn't know active bottlenecks
Why 2: No structured briefing document exists
Why 3: Outgoing TL has no time to write one mid-shift
Why 4: No system prompts the handover summary
Root: No standard handover SOP or digital tool exists
IMPROVE & CONTROL — Solution
Proposed Fix: 5-field digital handover form (active bottlenecks, equipment flags, associate notes, volume forecast, priority action) completed by outgoing TL 20 minutes before shift end. Accessible on shared tablet at station entry point.
Estimated impact: -60% repeat incidents, +12% throughput in first 90 minutes of shift
🟡 Bottleneck #2 — Reactive Staffing Decisions
DEFINE — Problem Statement

Staffing adjustments (pulling associates from one station, requesting backup) happen only after throughput has already dropped below target. The average detection-to-action lag is 45–60 minutes, meaning each bottleneck costs approximately 45 UPH × 40 people × 0.75hr of lost output.

MEASURE — Observed Impact
Avg detection lag
52 min
Lost output per event
~1350u
Events per week (est.)
3–5x
🔧
Solution Direction: Predictive demand model (ShiftSense AI) that forecasts volume peaks using historical patterns, day-of-week, and seasonal factors — enabling staffing decisions 60 minutes earlier. Even a simple moving-average model would reduce reactive events by ~40%. This is the exact hypothesis my GitHub project is testing.
🔵 Bottleneck #3 — Inbound-to-Stow Handoff Delays
DEFINE — Problem Statement

Units processed through inbound receiving create temporary queue buildups before the stow team begins processing them. During peak volume windows, this queue can represent 2–4 hours of stow backlog, causing downstream pick operations to operate on incomplete inventory.

IMPROVE — Proposed Solution

Dynamic cross-training rotation: 20% of inbound associates cross-trained on stow, enabling automatic rebalancing when queue exceeds 90-minute threshold. Triggers a visual alert (floor screen or tablet notification) with recommended reallocation.

Estimated impact: -35% queue buildups, +8% overall pick accuracy during peak windows

RICE Prioritisation Engine

Interactive feature scoring tool — the exact framework I used at AGCO GmbH to prioritise PIM and e-commerce backlog. Try it yourself.

Interactive Tool AGCO Context
Score a Feature
640
RICE Score — Bulk product attribute import
Reach
1200
Impact
2
Confidence
80%
Effort
3w
Backlog — Prioritised by RICE Score

Based on the actual AGCO PIM platform feature set I managed. Names generalised for confidentiality.

📐
PO Insight: At AGCO, I used RICE alongside MoSCoW to handle the most common Product Owner challenge: 5 country teams each believing their request is the highest priority. RICE depoliticises the conversation — the score is the score. The breakthrough was getting all stakeholders to agree on the inputs before the scoring, not to argue the output afterwards.

Backlog Analyser

A simulated sprint backlog from the AGCO PIM project — demonstrating the structure, depth, and decision-making of a real product ownership cycle.

AGCO Context12-Month Backlog
Total Stories
127
Across 4 epics
Completed
94
74% delivery rate
In Sprint
18
Current sprint load
Backlogged
15
Deprioritised
Sprint Stories — Current Sprint
IDUser StoryEpicPointsPriorityStatusOwner
PIM-041As a DE product manager, I can bulk-update attribute values via CSV upload so that I don't need to edit 500+ products manuallyData Quality8P1In ProgressDev Team A
PIM-042As an FR regional admin, I can view a data completeness score per product so that I can prioritise missing attribute completionData Quality5P2ReviewDev Team B
EC-019As a UK e-commerce manager, I can publish a product to the UK channel only without affecting DE/FR listingsE-Commerce13P1In ProgressDev Team A
EC-020As a product owner, I can view a cross-channel publish status dashboard so that I can confirm release completeness across all 5 marketsE-Commerce5P2DoneDev Team B
GOV-008As an IT admin, I can configure role-based access per country team so that FR editors cannot modify DE product dataGovernance8P1TestingVendor
PIM-043As a vendor, I can submit product data via API endpoint so that manual re-entry is eliminated for our 200 SKU catalogueData Quality13P2DoneDev Team A
Deliberately NOT Built This Sprint — and Why

The most important Product Owner skill is knowing what NOT to build. Here are 3 features deprioritised this quarter with explicit reasoning — the kind of decision documentation that almost no PO candidate shows.

FeatureRequested ByRICE ScoreDecisionRationale
Multi-language auto-translation of product descriptionsFR & UK Teams82Deferred Q3High effort (13w), requires legal review of translated content. RICE score drops to 44 when confidence adjusted to 50%. Will revisit after data quality epic completes.
Custom product comparison widget for DE webshopDE Marketing120Deferred Q4Reach limited to DE channel only. Better served by UX team post-platform stabilisation. Creates tech debt risk if built before global template is finalised.
Automated competitor price monitoring integrationGroup Strategy95Separate EpicValuable but out of PIM platform scope. Escalated to e-commerce tech team as a separate product initiative. Documented and handed off with full requirements.

Sprint Roadmap — AGCO PIM Project

12-month delivery plan reconstructed from memory and notes. Demonstrates release planning, epic sequencing, and quarterly milestone management.

AGCO ContextDelivered
Epic Completion — By Quarter
Data Foundation & Governance
100%
Multi-Country Publishing
88%
E-Commerce Channel Integration
76%
Vendor API Integration
60%
Q1
Foundation
Discovery + Data Gov
Q2
Core Platform
PIM + Backlog Build
Q2→Q3
⚠ Scope Change
FR requirements added
Q3
Channel Launch
DE + UK live
Q4
Optimise
FR + API layer
Key Release Milestones & Decisions
Jan 2023

Project Kickoff — Discovery Phase

Ran 12 stakeholder interviews across DE, FR, UK business units. Documented 47 requirements, identified 8 conflicting priorities. Produced first prioritised backlog draft.

→ Output: Prioritised backlog v1.0 with 68 user stories
Mar 2023

Sprint 1 Review — Data Foundation Shipped

First release of data governance framework. Role-based access for DE and FR teams live. Attribute taxonomy standardised across 3,200 product SKUs.

→ Impact: Data entry errors reduced by ~40% (pre/post audit comparison)
May 2023

⚠ Scope Change — FR Requirements Added Late

FR business unit escalated 14 new requirements mid-sprint. Decision: absorbed 6 into current epic (low effort), deferred 8 to Q3 roadmap. Communicated impact to all stakeholders within 48 hours.

→ Decision doc: MoSCoW re-evaluation, stakeholder sign-off achieved in 3 days
Jul 2023

DE + UK Channel Launch — On Schedule

Multi-country e-commerce publishing live for DE and UK markets. Product visibility on both channels confirmed. First cross-market product data consistency achieved.

→ Delivered on schedule, within agreed sprint capacity
Dec 2023

Project Close — Handoff & Retrospective

Final retrospective: 94 of 127 stories completed (74% delivery rate). Remaining 33 stories handed off with full documentation to successor team. Key learnings documented.

→ 74% delivery, 3 on-time releases, 0 critical production incidents

Career Impact Timeline

A chronological evidence trail — specific, honest, and quantified where possible. The artefact most portfolio sites never include.

Verified EvidenceHonest Scope
2022
Oct

Joined Robert Bosch GmbH — Stuttgart

Started as working student in Finance & Operations. First exposure to enterprise change management at scale. Assigned to SaaS rollout enablement workstream across 3 countries.

→ Role: Working Student / Intern | Scope: Support function
2023
Jan

Built Access Management Governance Framework at Bosch

Created the role-based access matrix, request-approval workflow, and audit trail process that became the permanent platform operating standard post-go-live. Covering ~200 users across Finance and Operations.

→ Framework still in use. Delivered within 6 weeks.
2023
Apr

Joined AGCO GmbH — Marktoberdorf as Product Owner

First Product Owner role. Accountable for PIM and E-Commerce platform backlog across 5 European markets. Immediately responsible for a 100-story backlog with 3 competing country stakeholder groups.

→ Role: Product Owner (Full-Time) | Scope: EU multi-country platform
2023
Jul

Delivered First Multi-Country Platform Release at AGCO

DE and UK e-commerce channels live with consistent product data. First time product information was synchronised across both markets from a single source of truth. Delivered on schedule after mid-sprint scope change was managed and resolved in 3 days.

→ On-time delivery. ~40% reduction in data inconsistency errors (pre/post audit).
2023
Oct

Completed MBA & Engineering Dual Degree — Hof University

Finished MBA in Operational Excellence alongside engineering qualification at Hof University of Applied Sciences. Applied coursework directly to AGCO product operations context.

→ Qualification: MBA + Engineering dual degree, Germany, 2024 (completed)
2024
Apr

AGCO Contract End — 12-Month Project Completed

94 of 127 stories delivered (74% rate). 3 on-time releases. 0 critical production incidents. Full documentation and handoff package produced for successor team. Key retrospective learnings documented.

→ 74% delivery rate, 12 months, 5 EU markets, 3 stakeholder groups aligned
2024
Dec

Joined Amazon EU — Graben Fulfilment Centre

Strategic decision to join at L1 Operations Associate level to build ground-up operational understanding of world-class fulfilment. Deliberately gaining the operational credibility that most Product Owners lack when working on logistics, warehouse, or supply chain products.

→ Role: L1 Operations Associate | Purpose: Operational intelligence for product career
2025
Jan

Launched ShiftSense AI & PRD Generator on GitHub

Published two open-source projects directly solving problems observed in professional roles. ShiftSense AI: predictive demand modelling for warehouse operations. PRD Generator: AI-assisted product documentation. Both with full READMEs and working code.

→ 2 live GitHub repos | Real problem origins | Applied AI tooling
2025
Mar

Built OpsIQ — This Interactive Portfolio Project

Designed and built a world-first interactive Product Operations Intelligence Suite as a portfolio artefact — demonstrating product thinking, data analysis, Lean methodology, and operations knowledge simultaneously in a tool a hiring manager can actually use.

→ You are using it right now.

About This Project

Why I built OpsIQ — and what it's designed to prove to a hiring manager in Germany.

What OpsIQ Is

OpsIQ is an interactive Product Operations Intelligence Suite — a portfolio project that does something no other PO or operations candidate portfolio does: it lets a hiring manager actually use the tools I would build.

Most portfolios are static. They describe work. OpsIQ demonstrates it. Every module — the live KPI dashboard, the shift simulator, the A3 bottleneck analysis, the RICE prioritiser, the backlog analyser — represents a real artefact from either my Amazon EU or AGCO experience.

This is the rarest combination a Product Owner candidate can offer in 2025: deep operations experience from the inside + product ownership at enterprise scale + the ability to build the digital tools that bridge both.

Who I Am ?

Monil Raval

Product Owner & Operations Professional — 4.5 years in Germany across Amazon, AGCO, and Bosch.

Currently

L1 Operations Associate at Amazon EU, Graben. Deliberately building operational credibility.

Previously

Product Owner — AGCO GmbH (PIM & E-Commerce, 12 months, EU multi-country).

Education

MBA Operational Excellence — Hof University 2024 · MSc IT — GLS University 2019.

Target Roles

Product Owner · Operations Analyst · Digital Transformation · Business Analyst — Germany.

Ready to discuss these projects in an interview

Every module in OpsIQ is backed by real professional experience. I can walk through any section in detail about the A3 analysis, the RICE prioritisation decisions, the backlog structure — because I lived these situations at AGCO and Amazon. Available for interviews immediately across Germany.