FAQ
Common Challenges and Solutions in Custom Software Development
Key Challenges & GWIT’s Solutions
1. Unclear or Frequently Changing Requirements
User Story Mapping → Prioritizes core requirements and aligns stakeholder expectations.
Rapid Prototyping → Validates feasibility early using tools like Figma/Axure.
Change Control Process → Implements "freeze points" in development phases, with formal approval required for late-stage changes.
2. Quality Control Issues
Test-Driven Development (TDD) → Mandates unit test coverage as a code merge requirement.
Automated Testing Pipeline → Integrates Selenium + Jenkins for regression testing, reducing post-launch defects by 80%+.
3. Poor User Experience (UX)
User Journey Mapping → Optimizes interaction flows before development begins.
A/B Testing & Usability Testing → Involves real users in iterative feedback loops to refine UI/UX.
GWIT’s Core Principles:
✔ Validate Requirements Early
✔ Transparent & Controlled Processes
✔ Build Quality In from the Start
Common Challenges & Solutions in Warehouse Inventory Management Software
Key Challenges & GWIT’s Solutions
1. Inaccurate Inventory Data
Barcode/RFID Integration → Tracks items end-to-end, reducing errors to <0.3%.
Dynamic Cycle Counting → Implements ABC analysis (e.g., frequent counts for high-value "A" items).
2. Overly Complex Operations
Smart Form Engine → Auto-fills fields (e.g., SKU specs, batch numbers) via scanning.
RPA Automation → Guides staff with standardized workflows, cutting training time by 50%.
3. Multi-Warehouse Coordination Issues
Distributed Database (TiDB) → Ensures real-time sync across locations.
AI-Driven Alerts → Predicts safety stock thresholds and triggers mobile notifications for anomalies.
4. System Performance Bottlenecks
Microservices Architecture → Isolates core modules (orders, inventory, reporting) for scalability.
Redis Caching Layer → Boosts query speeds, handling 5,000+ concurrent users with sub-second response.
Advanced Capabilities
Real-Time Analytics → Apache Flink processes in/outbound data flows for AI-powered decisions.
Inventory Optimization AI → Generates automated procurement and transfer recommendations.
Low-Code Customization → Visual platform lets users design reports/approval workflows without coding.
Technical Excellence:
✔ Modular Development → 3-week iterative release cycles
✔ Automated Ops + Canary Deployments → Minimizes upgrade risks
✔ Future-Ready Architecture → Supports 99.99% uptime and unmanned warehouse expansion
Common Issues in SaaS Application Management Systems and Solutions
For the issue of data silos and system fragmentation, the GWIT SaaS technology team has adopted a unified data platform architecture: constructing standardized data models and integrating ETL tools to clean data from heterogeneous systems. Additionally, pre-built industry connectors are provided: offering out-of-the-box API templates (such as integrations with DingTalk, WeChat Work, and OA systems).
To address the phenomenon of multi-tenant resource contention, the SaaS technology backbone of the GWIT team has proposed dynamic resource quotas: automatically allocating computing resources (CPU/memory elastic scaling) based on tenant SLAs.
For issues related to user permission configuration errors leading to unauthorized operations, or the lack of field-level permissions resulting in sensitive data leakage risks, the GWIT technology team has proposed the ABAC (Attribute-Based Access Control) dynamic authorization model: dynamically adjusting permissions based on environmental attributes (IP address, time, device).
The GWIT SaaS technology team also provides suggestions for the SaaS project implementation roadmap:
Short-term:
Deploy an API gateway for unified interface management and integrate with mainstream third-party systems.
Implement a hybrid RBAC (Role-Based Access Control) + ABAC permission model and complete encryption of sensitive data.
Medium-term:
Build a low-code platform to support 80% of customization needs and reduce the proportion of code changes.
Launch a chaos engineering framework to achieve 99.95% availability.
Long-term:
Implement a multi-cloud architecture to support seamless migration across AWS, Azure, and Huawei Cloud.
Key to Implementation: The GWIT technology team recommends that customers prioritize solving issues related to data interoperability and permission control. By establishing standardized interfaces and dynamic permission models, customer trust can be quickly built. Subsequently, the architecture can be gradually upgraded.
Solving Data Integration Challenges for Retailers Using SaaS CRM
The GWIT technology team has detailed the key technical implementation details:
Real-time Heterogeneous Protocol Conversion
Protocol Adapters Layer
Using Apache Camel to implement multi-protocol conversion:
// Example of converting SAP IDoc to JSON
from("sap-idoc:queue:ORDERS")
.unmarshal().idoc()
.convertBodyTo(Json.class)
.to("kafka:orders?brokers=localhost:9092");
Supports over 20 protocols, including SAP JCo, EDI, and AS2.
Smart Field Mapping: Establishing a dynamic mapping rule library (e.g., mapping CRM field "mobile" to ERP field "TEL_NUMBER").
Automated Data Flow Processing
Real-time Data Pipeline
Stage | Technology | Performance Metrics
Data Ingestion | Debezium CDC | Throughput: 100,000 records/second
Stream Processing | Apache Flink | Latency: <50ms
Persistent Storage | Cassandra + Redis | Write QPS: Over 50,000
Typical Processing Logic:
-- Detecting abnormal orders
INSERT INTO error_orders
SELECT * FROM orders_stream
WHERE total_amount < 0
OR customer_id NOT IN (SELECT id FROM crm_customers);
3.Business Process Automation Orchestration
BPMN Visual Modeling
Achieves automated execution of cross-system business processes.
Compensation Transaction Design
Implementation of the SAGA Pattern:
Step | Forward Action | Reverse Compensation Action
CRM Customer Creation | crm.createCustomer() | crm.deleteCustomer(customerId)
ERP Sales Order Generation | erp.generateSalesOrder() | erp.cancelOrder(orderId)
Logistics Capacity Booking | logistics.bookTransport() | logistics.cancelBooking()
Transaction success rate increased to 99.97%.
The GWIT technology team's solution for multi-system integration has been successfully implemented and validated in retail enterprises such as Watsons and Miniso, reducing operational costs by over 35% on average. It is recommended to start the implementation using the Spring Cloud + Apache Flink technology stack.
Common Challenges in Enterprise IoT System Development and Solutions
GWIT Technology Team's IoT Construction Solutions:
Security Protection Technology Stack
Zero Trust Security Architecture
Device Identity Authentication: Implementing device fingerprint uniqueness verification by combining TLS mutual authentication with the national cryptography SM9 algorithm.
Dynamic Data Encryption: Using AES-256 and quantum key distribution technology to ensure the security of the transmission link.
Threat Detection System: Building a behavior analysis engine based on the MITRE ATT&CK framework to detect abnormal operation chains in real time.
Data Processing Architecture Upgrade
Hybrid Computing Architecture
Edge Layer: Using Apache Kafka Edge combined with a WebAssembly stream processing engine (latency <50ms).
Fog Computing Layer: Supporting tens of millions of data points with TDengine/InfluxDB time-series database clusters.
Cloud Layer: Implementing cross-system federated data analysis with a digital twin platform to support real-time decision-making feedback.
Intelligent Maintenance System
OTA Upgrade Management: The GWIT technology team uses differential upgrade technology (BSDiff algorithm) to transmit only the differential data packages, reducing network bandwidth usage.
Predictive Maintenance: Utilizing an LSTM neural network-based Remaining Useful Life (RUL) prediction model for equipment, the team can provide early warnings of failures up to 30 days in advance, reducing maintenance costs by 35%.
Implementation Highlights:
GWIT's technology team has successfully implemented the most advanced technology combination of Zero Trust Architecture + 5G TSN + Digital Twin in enterprises such as BMW, achieving end-to-end deterministic communication and millisecond-level response.