# Formatted Paper Outline

**Format Applied:** Modified IMRaD (Conceptual/Theoretical Framework)
**Target Venue:** Logistics or SCM Journal (e.g., *Journal of Supply Chain Management*, *IJPE*, or Conference Proceedings)
**Word Limit:** 3,000 - 4,500 words
**Citation Style:** APA 7th Edition

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## Formatting Requirements

### Manuscript Specifications
- **Font:** Times New Roman 12pt or Arial 11pt
- **Line Spacing:** Double-spaced throughout (including abstract and references)
- **Margins:** 1 inch (2.54 cm) on all sides
- **Page Numbers:** Top right corner, starting on Title Page
- **Running Head:** Shortened title (max 50 chars) in ALL CAPS (top left) - *Required for professional APA manuscripts*

### Section Heading Levels (APA 7th)
- **Level 1:** **Centered, Bold, Title Case** (Main Sections)
- **Level 2:** **Left-Aligned, Bold, Title Case** (Subsections)
- **Level 3:** **_Left-Aligned, Bold Italic, Title Case_** (Sub-subsections)

### ⚠️ CITATION REQUIREMENTS - CRITICAL

**Communicate to Crafter Agents:**
- **Default Style:** APA 7th Edition.
- **In-text:** Use (Author, Year) for parenthetical; Author (Year) for narrative.
- **Specific Sources:** The Architect identified **Cramer (2024)** and **Mendler (2024)**. These must be cited correctly to represent the SCM and Engineering "silos."
- **[VERIFY] Tag:** Use only if specific technical details of Bayesian formulas need checking.

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## Formatted Structure

### Title Page
**Title:** **Sensing Sustainability: Integrating Bayesian Sensor Optimization into Agri-food Supply Chain Planning**
**Author Note:**
- Name(s)
- Affiliation (Department, University)
- ORCID iD
- Conflict of Interest Disclosure
- Correspondence Email

### Abstract
**Heading:** **Abstract** (Bold, Centered)
**Format:** Single paragraph, no indentation.
**Length:** 200–250 words
**Content Flow:**
1.  **Context:** The "perishability vs. availability" trade-off in agri-food chains.
2.  **Problem:** The disconnect between high-level SCM planning (Cramer, 2024) and technical sensor optimization (Mendler, 2024).
3.  **Objective:** Propose a "Bayesian-Integrated SCM Framework."
4.  **Mechanism:** Explain how sensor data updates inventory parameters via posterior variance reduction.
5.  **Implications:** Impact on waste reduction and food security.

**Keywords:** _Agri-food supply chain, Bayesian optimization, IoT sensors, Perishability, Sustainable operations._

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## 1. Introduction
**Length:** ~500 words

### 1.1 Background: The Perishability Paradox
- Discuss the global challenge of food waste vs. food security.
- Introduce the difficulty of managing inventory that degrades stochastically.

### 1.2 Problem Statement
- **The Gap:** SCM models often treat quality degradation as a fixed curve, ignoring real-time deviations. Conversely, engineering models optimize sensors but don't link data back to ordering policies.
- **The Silos:** Reference the disconnect between operations research and sensor engineering.

### 1.3 Research Question and Objectives
- **RQ:** How can Bayesian sensor optimization logic be integrated into agri-food supply chain planning to reduce perishability-related waste?
- **Objective:** To develop a conceptual framework that bridges these disciplines.

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## 2. Literature Review: Two Worlds Apart
**Length:** ~1,000 words
**Focus:** Establishing the theoretical silos.

### 2.1 SCM Perspectives on Perishability
- Review standard inventory models for perishable goods (EOQ for decaying items).
- Discuss **Cramer (2024)**: Focus on the emphasis on specialized planning but the lack of real-time data integration mechanisms.
- *Gap identified:* Models are robust but static.

### 2.2 Engineering Perspectives on Sensor Networks
- Review IoT and sensor placement literature.
- Discuss **Mendler (2024)**: Focus on Bayesian optimization for sensor placement (maximizing information gain).
- *Gap identified:* Focus is on data quality, not decision-making utility in an SCM context.

### 2.3 Synthesis
- Argument that sustainability goals require merging these streams.
- Introduction of the "Value of Information" concept in this specific context.

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## 3. The Bayesian-Integrated SCM Framework
**Length:** ~1,200 words
**Nature:** This is the core contribution (The "Method" / "Model").

### 3.1 Framework Overview
- **Narrative:** Description of the proposed closed-loop system.
- **Figure 1:** *Conceptual Diagram required.*
    - *Left side:* Physical Supply Chain (Farm -> Transport -> Retail).
    - *Middle:* Sensor Layer (Bayesian placement logic).
    - *Right side:* Cyber/Planning Layer (Inventory updates).

### 3.2 The Bayesian Updating Mechanism
- Explain the logic:
    1.  **Prior:** Historical spoilage rates.
    2.  **Likelihood:** Real-time sensor data (temp/humidity).
    3.  **Posterior:** Updated spoilage probability.
- *Note for Crafter:* Use descriptive logic; minimal complex math unless essential for the argument.

### 3.3 Integrating into Inventory Planning
- Explain how the "Posterior" feeds into Safety Stock calculations.
- Argument: Reduced variance = Lower safety stock = Less waste.

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## 4. Theoretical Analysis and Propositions
**Length:** ~800 words
**Nature:** "Results" section for a conceptual paper (Logical proofs/arguments).

### 4.1 Proposition 1: Variance Reduction
- *Argument:* Optimal sensor placement (via Mendler's logic) significantly reduces the standard deviation of quality prediction errors compared to random placement.

### 4.2 Proposition 2: The Safety Stock Effect
- *Argument:* As prediction uncertainty decreases, the required safety stock to maintain service levels decreases non-linearly.

### 4.3 Proposition 3: Sustainability Impact
- *Argument:* Tighter inventory control directly correlates to reduced expiration waste, bridging the gap to sustainability goals.

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## 5. Discussion
**Length:** ~700 words

### 5.1 Theoretical Implications
- How this framework extends Cramer (2024) by adding a dynamic data layer.
- How it extends Mendler (2024) by providing an economic use-case for sensors.

### 5.2 Managerial Implications
- Advice for Supply Chain Managers: Investing in sensors is not just for tracking; it is for *planning*.
- The trade-off between sensor cost and waste reduction savings.

### 5.3 Limitations and Future Research
- **Limitation:** This is a conceptual model; empirical validation is needed.
- **Future Work:** Simulation studies or pilot programs in cold chains.

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## 6. Conclusion
**Length:** ~300 words
- Restate the core thesis: Real-time data must drive planning parameters.
- Final strong statement on the convergence of Engineering and SCM for sustainability.

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## References
**Format:** APA 7th Edition
- Ensure **Cramer (2024)** and **Mendler (2024)** are fully formatted.
- Include 15-20 additional high-quality sources (mix of SCM and IoT journals).

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## Length Targets by Section (approx.)

| Section | Words | % of Total |
|---------|-------|------------|
| Abstract | 250 | 5% |
| Introduction | 500 | 12% |
| Lit Review | 1000 | 22% |
| Framework (The Model) | 1200 | 27% |
| Analysis (The Mechanism) | 800 | 18% |
| Discussion | 700 | 16% |
| Conclusion | 300 | ~ |
| **Total** | **4,500** | **100%** |

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## Style Guide for Crafters

### Tone
- **Formal & Objective:** Avoid colloquialisms.
- **Interdisciplinary:** Define technical terms clearly (e.g., "Bayesian updating" needs to be understood by an SCM manager; "Safety Stock" needs to be understood by an engineer).

### Tense Usage (APA)
- **Lit Review:** Past tense ("Cramer (2024) argued...").
- **Framework/Model:** Present tense ("The framework proposes...", "Figure 1 illustrates...").
- **Propositions:** Present tense ("We propose that...").

### Voice
- **Active Voice Preferred:** "This paper proposes..." rather than "It is proposed in this paper..."
- **Anthropomorphism:** Avoid attributing human actions to non-human entities (e.g., ❌ "The sensor thinks..." ✅ "The sensor data indicates...").

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## Next Steps

1.  **Crafter Agent** to draft Sections 1 and 2, focusing on the synthesis of Cramer and Mendler.
2.  **Crafter Agent** to draft Section 3, ensuring the "Bayesian" logic is accurate but accessible.
3.  **Reviewer** to check if the bridge between the two disciplines is logical.

**Save this outline as:** `outline_formatted.md`