解決方案構想的核心理解

解決方案構想是連接業務需求與技術實現的關鍵橋梁，從您描述的內容和我的理解，這個階段的核心點包括：

核心要點解讀

1. 轉化視角：將業務視角的需求轉變為解決方案視角

   • 業務能力探索階段識別了"做什么"(What)
   • 解決方案構想階段確定"怎么做"(How)

2. 利益相關者共識：確保各方(業務方、技術方、用戶等)對解決方案有一致認可

   • 這解決了業務與技術之間常見的"斷層"問題

3. 新工作方法的構想：描述系統實施后，業務流程將如何運作

   • 不僅是技術實現，也包括業務流程的重塑

4. 解決方案架構藍圖：初步勾勒系統的整體架構

   • 包括組件、交互、依賴等高級設計

解決方案構想的具體步驟

1. 接收業務環境輸入：

   • 是的，這些是從第一階段(業務能力探索)總結出的業務需求、流程和限制條件
   • 包括業務目標、流程圖、能力用例等

2. 構想演練：

   • 通過研討會或工作坊形式
   • 讓技術人員和業務人員共同參與
   • 繪制新系統下的業務流程

3. 解決方案概念形成：

   • 產出可視化模型(如系統上下文圖、組件圖)
   • 描述用戶旅程和系統交互
   • 定義技術約束和非功能性需求

4. 驗證與調整：

   • 向利益相關者展示并獲取反饋
   • 評估解決方案與業務目標的匹配度
   • 必要時進行調整

具體例子說明

假設一個電子商務公司想改進庫存管理系統：

1. **第一階段(業務能力探索)**輸出：

   • 業務目標：減少庫存成本，提高訂單處理速度
   • 關鍵流程：接收訂單→檢查庫存→更新庫存→發貨
   • 痛點：庫存不準確，手動處理慢

2. 第二階段(解決方案構想)：

   • 構想：自動化庫存管理系統，與訂單系統實時集成
   • 新工作流程：訂單自動觸發庫存檢查→智能預警→自動補貨建議
   • 系統架構：訂單微服務、庫存微服務、預測引擎、儀表板
   • 非功能需求：99.9%可用性，毫秒級響應

3. 第三階段才會進入具體的類設計、API設計等細節

解決抽象性的建議

解決方案構想之所以感覺抽象，是因為它處于業務和技術的交界處，既不是純業務分析，也不是具體技術設計。可以通過以下方式使其更具體：

1. 使用可視化工具(如系統上下文圖、組件圖)
2. 創建用戶旅程地圖，展示新系統下用戶如何完成任務
3. 開發原型或模擬界面，展示核心功能
4. 編寫具體場景，描述新系統如何處理特定業務案例

解決方案構想的價值在于它確保了我們不會直接從業務需求跳到代碼實現，而是先有一個清晰的藍圖和共識，避免開發出不符合業務期望的系統。

Examples of Business-to-Solution Transformation

Let me provide several concrete examples of how businesses move from identifying problems to constructing solutions through the “solution envisioning” phase.

Example 1: Retail Inventory Management

Business Capability Exploration:

• Current Process: Store managers manually count inventory weekly and place orders
• Pain Points: Stock-outs occur between counts; excess inventory ties up capital
• Business Goals: Reduce stock-outs by 40%, reduce excess inventory by 25%

Solution Envisioning:

1. Conceptual Solution: Real-time inventory tracking system with predictive ordering

2. New Work Methods:

   • Electronic point-of-sale data automatically updates inventory
   • AI analyzes sales patterns and predicts future demand
   • Managers review and approve system-generated orders
   • Suppliers receive automated restock requests

3. Solution Architecture:

   • Central inventory database connected to POS systems
   • Prediction engine using machine learning
   • Supplier integration portal
   • Mobile app for managers to approve orders

4. Stakeholder Benefits:

   • Store Managers: Less time counting, more focus on customer service
   • Finance: Less capital tied up in inventory
   • Operations: Fewer emergency shipments
   • Customers: Better product availability

Software Capability Design:

The technical team would then design the specific components, APIs, database schema, etc.

Example 2: Healthcare Patient Scheduling

Business Capability Exploration:

• Current Process: Patients call to schedule appointments; staff manually manages schedule
• Pain Points: 30% no-show rate, long wait times for appointments, staff spends 40% of time on phone
• Business Goals: Reduce no-shows, decrease scheduling staff workload, improve patient satisfaction

Solution Envisioning:

1. Conceptual Solution: Multi-channel scheduling system with automated reminders

2. New Work Methods:

   • Patients book appointments online, via app, or through chatbot
   • System automatically sends reminders with confirmation requirement
   • Waitlist feature fills canceled slots automatically
   • Staff focuses on exceptions and complex scheduling

3. Solution Architecture:

   • Central scheduling engine with provider availability rules
   • Patient portal with authentication
   • Notification system (SMS, email, push)
   • Integration with EMR (Electronic Medical Records)
   • Analytics dashboard for staff

4. Stakeholder Alignment:

   • Physicians: Fewer gaps in schedule, better prepared patients
   • Admin Staff: Less routine calls, more value-added work
   • Patients: Convenient booking, shorter wait times
   • Management: Higher resource utilization

Example 3: Manufacturing Quality Control

Business Capability Exploration:

• Current Process: Random sampling and manual inspection of products
• Pain Points: 3% defect rate reaching customers, high rework costs, slow feedback loop
• Business Goals: Reduce customer-facing defects to <1%, reduce QC labor costs by 20%

Solution Envisioning:

1. Conceptual Solution: Vision-based AI quality inspection system

2. New Work Methods:

   • Cameras capture images of each product on the line
   • AI system identifies defects in real-time
   • Defective items automatically routed for rework
   • QC specialists focus on edge cases and system training
   • Engineers receive defect pattern reports to address root causes

3. Solution Architecture:

   • Camera array integration with production line
   • Edge computing for real-time processing
   • Deep learning models for defect detection
   • Feedback loop to production systems
   • Knowledge management for continual improvement

4. Validation Approach:

   • Parallel run with manual inspection before full implementation
   • Weekly review of false positives/negatives
   • Progressive expansion from simple to complex products

Example 4: Financial Services Loan Application

Business Capability Exploration:

• Current Process: Paper-based loan applications, manual underwriting, 7-day approval cycle
• Pain Points: Slow process, document errors, inconsistent risk assessment
• Business Goals: Reduce approval time to 24 hours, improve risk assessment accuracy

Solution Envisioning:

1. Conceptual Solution: Digital loan origination platform with automated underwriting

2. New Work Methods:

   • Customers complete applications online with guided assistance
   • System automatically verifies identity and retrieves credit reports
   • Machine learning model performs initial risk assessment
   • Underwriters review only exceptions or high-value loans
   • Digital signatures and automated disbursement

3. Solution Architecture:

   • Secure customer portal with document upload
   • Integration with credit bureaus and banking systems
   • Rules engine for basic qualification
   • Risk scoring model using customer data
   • Document management system with compliance controls
   • Workflow system for manual reviews

4. Business Impact Metrics:

   • Application completion rate: target 85% vs current 60%
   • Processing time: target 4 hours vs current 3 days
   • Staff productivity: target 3× increase in applications processed

Key Takeaways for Solution Envisioning

In all these examples, notice how the solution envisioning phase:

1. Bridges the gap between business problems and technical solutions
2. Describes the future state in terms business stakeholders understand
3. Considers the entire ecosystem, not just software components
4. Focuses on new work methods, not just technology
5. Aligns stakeholders before technical design begins
6. Creates measurable expectations for solution success

The solution envisioning phase creates a shared mental model that both business and technical teams can understand, ensuring the eventual software design truly addresses the business needs.

那么，解決方案構想是針對業務問題。 是需要我們花時間 ，想出好方法的。在這個過程中，應該不會涉及設計模式把。設計模式是后面detail code desgin時候，去想的，對嗎

解決方案構想與設計模式的關系

您的理解基本正確。解決方案構想階段主要關注業務問題的整體解決方案，而不是具體的代碼實現細節。但我想稍微補充和澄清一些點：

解決方案構想階段的關注點

1. 業務視角的系統輪廓：定義系統將如何解決業務問題
2. 高層架構決策：系統的主要組件和它們之間的關系
3. 技術策略：選擇技術棧、集成策略、部署模型等
4. 用戶體驗流程：系統如何支持新的工作方式

設計模式在不同階段的應用

您觀察得很準確，具體的設計模式通常是在詳細設計階段應用，但實際上設計模式可能在不同層次上出現：

解決方案構想階段 - 架構模式

• 不會涉及具體的代碼級設計模式（如工廠模式、觀察者模式等）
• 可能會考慮架構級模式，例如：
  • 微服務架構 vs 單體架構
  • 事件驅動架構 vs 請求-響應架構
  • CQRS (命令查詢職責分離)
  • 分層架構

軟件能力設計階段

• 詳細的代碼級設計模式（GoF 設計模式）
• 類結構、繼承關系
• 接口定義
• 具體實現策略

從您的代碼看實際應用

從您共享的 CMakeLists.txt 文件來看，您的項目已經進入到實現階段，包含了具體的構建指令和庫鏈接。在之前的解決方案構想階段，您可能已經做了這些決策：

1. 將功能模塊化為多個章節庫（Chapter01Lib, Chapter02Lib 等）
2. 決定使用動態鏈接庫（DLL）而非靜態鏈接
3. 確定了各模塊間的依賴關系

這些是解決方案構想的一部分，而具體每個庫內部如何實現（使用什么設計模式）則屬于軟件能力設計階段。

總結

1. 解決方案構想：處理"我們要構建什么樣的系統"和"系統如何工作"的大局問題

   • 關注業務成果
   • 定義高層架構
   • 通常不涉及具體設計模式

2. 軟件能力設計：處理"如何編寫代碼來實現這個系統"的細節問題

   • 應用設計模式解決特定問題
   • 定義類、接口和組件
   • 確定具體的數據結構和算法

解決方案構想是為了確保我們構建的是正確的系統（building the right system），而設計模式則幫助我們正確地構建系統（building the system right）。