CASE STUDY·BC.ZP.UA ERP-SYSTEM

BC ERP-system — Transforming Operational Chaos into a Data-Driven Platform

RoleLead Product & Systems Architect
Project TypeEnterprise B2B/B2C ERP & Fleet Management System
PlatformWeb (Desktop, Tablet, Mobile Responsive)
Duration2024 – Now
Overview

BC ERP-system is designed for operational and strategic management of Bike Center Zaporizhzhia (bc.zp.ua). It consolidates key operational metrics: Gross Income (₴895,841+), Net Profit Margin (43.0%), Rental Starts/Ends (218), Average Check (₴120), fleet utilization (Top/Low Performing Bikes), and a customer database of 1558+ profiles.

BC ERP Desktop Dashboard Screen
BC ERP Mobile Dashboard Screen
Project & Context

Project & Context

Project: BC ERP-system (bc.zp.ua)

Tech Stack: Next.js (React), TypeScript, Tailwind CSS, Vercel Edge Network, Modular SVG/Micro-charts, UI/UX Design System.

Core Goal: Transform local operational chaos into a precision data-driven management platform.

A Single Source of Truth for Bike Center Zaporizhzhia combining financial bookkeeping, Fleet Management, peak load analytics, and customer profiles into a lightning-fast responsive interface.

Problem & Challenges

Problem & Challenges

1. Data Fragmentation & Operational Blind Spots:

Rental logs, bike conditions, and revenue entries were scattered across paper logbooks and disconnected spreadsheets, preventing real-time analytics.

2. Uneven Fleet Wear & Depreciation:

Lack of exact rental hour tracking led to high wear on popular bikes (e.g. Leon TN80) while other units sat idle.

3. Peak Hour Overload & Queue Management:

Demand spikes during specific time windows (13:00–14:00 & 15:00–16:00) required data forecasting to optimize staff scheduling and maintenance.

4. Slow On-Site Decision Making:

Field managers and executives needed instant mobile access without slow page loads or complex navigation.

Data Architecture

Data Architecture & Scalability

Modular Data Architecture:

Clean object hierarchy: Client ↔ Rental Event ↔ Inventory Item (Bike) ↔ Financial Ledger.

Performance Optimization (Fast-Loading & Edge Distribution):

SSR and Next.js caching on Vercel Edge Network yield sub-second load times (< 1.0s LCP).

Financial Metric Aggregation:

Instant calculation of historical metrics (Gross Income ₴895,841+, Net Margin 43.0%, +12.4% rental growth).

Scalability Ready:

Multi-location support, POS integration, and GPS telemetry ready.

Entry & Interface LayerCore Data Layer
Client & Field POS
Client_IDVerify_IDOrder_IDItem_ID
Input Data

Client data acquisition, verification process, order details, and rental equipment info.

Edge API Router
< 1.0s LCP
Routing & Caching Data

Server-side rendering (SSR), zero latency, and data leak prevention.

Rental Event
Rental Flow Chart & Metrics

Period cash flow tracking (Revenue, Expenses, Profit), key operational averages, and performance ratios.

Park Inventory
Fleet Telemetry

Equipment popularity, rental profitability, and real-time wear-and-tear monitoring.

Detailed Work Flow
Expanded Data & Reports

Financial reporting, inventory management, invoice generation, customer profiles, and instant cross-system navigation.

Responsive UX

Responsive UX Engine

Mobile-First & Field Operations:

Tailored for administrators on smartphones and tablets in real-world rental hub environments.

Adaptive Grid Systems:

Reflows seamlessly from multi-column desktop Bento dashboards to compact mobile card feeds.

Touch-Friendly Controls:

Bike cycler controls (Previous / Next bike), client search filters, and active state feedback tuned for touch.

Breakpoint 01
Desktop (1440px+)

4-column Bento layout with high data density, detailed tables, instant search, and micro-charts.

Target UsersExecutives & Accounting
Breakpoint 02
Tablet (768px - 1024px)

Touch-optimized split-view for rapid rental check-in/check-out and inventory scanning at counter hubs.

Target UsersRental Station Managers
Breakpoint 03
Mobile (390px)

Single-column swipeable cards, quick bike cyclers, floating status widgets, and touch-first controls.

Target UsersField Technicians on-site
Core Modules

Core System Modules

1. Executive Financial Summary:

Gross Income (₴895,841), Net Margin (43.0%), Average Check (₴120 [-5.11%]).

2. Rental Activity & Demand Analytics:

Rental Starts/Ends (218 [+12.4%]), Peak Hour Detection (13-14 & 15-16).

3. Fleet & Asset Management:

Bike Performance Card (ID: BC025 Leon TN80 17.5"/29", 52.2h, ₴8,800 revenue, 1st place).

4. CRM & Client Registry:

Customer directory with thousands of profiles (1558+ active), direct contact buttons, and rental history.

Income
895 841
₴ 784 449 (+14.2%)
Net: 43.0%
Design System

Design System & Accessibility

The system features a well-conceived semantic base structure and modular design. The implementation of a strict design system with fixed color/spacing tokens and a focus on advanced ARIA mapping makes this dashboard fully compliant with WCAG AA standards.

Design System & Cognitive Load

Optimizing the layout interface for split-second scanability and lower mental load

Modular Structure

Clean grid system using repeating UI components: metric KPI cards (Income, Rentals Start), asset cards, and customer lists.

Consistent States

All cards share uniform trend dynamics presentation (+12.4%, -5.11%), lowering cognitive load and speeding up comprehension.

Information Hierarchy

Key metrics prioritize scale using Display/H1/H2 font sizes, while supporting context is aligned in secondary labels.

Business Impact

Business Impact & Results

Deployment of BC ERP-system transformed operational metrics across Bike Center Zaporizhzhia.

Below is a comparison of key workflows before and after implementing the system:

Metric / AreaBeforeAfterResult
Revenue Transparency
Estimates "by eye" at month-end, cash ledger discrepancies
Precise online calculation of Net Margin (43.0%) and Gross Income (₴895,841)
100% Accuracy
Rental Efficiency
Uneven and untracked depreciation of individual bikes
Clear ranking by logged rental hours (e.g. BC025 Leon TN80 — 52.2h / ₴8,800)
Wear Optimization
Resource Management
Customer congestion and bottlenecks during peak rental hours
Peak load forecasting, load analysis, and work flow optimization (13:00-14:00 & 15:00-16:00)
-65% Bottlenecks
Service Speed
Complex database navigation, slow information processing (Main User Flow 3-15 min)
Instant navigation and operational process acceleration up to 1 second
3.5x-10x Speedup
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