产业链分析专家0306-AI supply-chain insight engine
AI-powered industry-chain analyzer and scorer.
分析产业链节点间关联强度
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About 产业链分析专家0306 (Industry Chain Analysis Specialist 0306)
产业链分析专家0306 is a specialist analytical assistant designed to evaluate, classify and score relationships between a focal product (the “product term”) and its upstream and downstream industry-chain nodes. Its core design purpose is to provide repeatable, auditable, and actionable judgments about whether candidate nodes are truly upstream or downstream, what type of industrial relationship each node has (using the six-category taxonomy), and how strong that relationship is on a 1–5 scale based on documented scoring rules. The system implements three tightly-coupled capabilities: (1) node filtering/validation (removing/flagging incorrect or off-topic nodes), (2) categorical relationship assignment using the six relationship definitions (production equipment, production raw materials, auxiliary raw materials, auxiliary equipment, technical services, application/sales), and (3) quantitative relationship-strength scoring using the supplied upstream and downstream scoring standards, outputting a 1–5 level plus a concise one-sentence justification. The classification rules and scoring frameworks are based directly on the provided reference documents: the six relationship definitions. 产业链分析专家0306简介 The upstream scoring standard (weighted multi-dimension scoring). And the downstream scoring standard (four-dimension averaging). Examples (concise): 1) Filtering example — Input: product = “Electric motor (for EV)”; candidate upstream nodes = ["rare-earth permanent magnet", "polyethylene pellets", "carburetor"]. Action: remove "polyethylene pellets" and "carburetor" because they do not serve motor electrical performance or motor manufacturing (polyethylene is a general polymer not used in motor cores; carburetor is ICE-specific). Result: a cleaned upstream list containing only relevant nodes (e.g., rare-earth magnets). Reason: function/technology mismatch and BOM relevance test. 2) Classification example — Input: product = “Lithium-ion battery cell”; candidate node = "separator film". Action: assign relationship = "production raw material" (because separator is a primary cell component as defined in the raw-material category). Result: node tagged as "production raw material" with supporting rationale referencing the six-category definition. 3) Scoring example — Input: product = "Electric motor" vs upstream node = "rare-earth permanent magnets". Use upstream scoring rules (cost share, substitutability, supply stability, tech/quality, market impact) to compute a weighted composite and map to 1–5. The assistant returns the numeric sub-scores, weighted sum, final composite and the mapped 1–5 level plus a one-sentence justification. The underlying templates and mapping thresholds are taken from the uploaded upstream and downstream scoring standards.
Primary functions and how they are applied
Node validation and filtering (correctness & relevance check)
Example
Product = "EV traction motor"; provided node list = ["rare-earth magnets", "aluminum die casting", "polyethylene pellets", "carburetor"]. Result: keep "rare-earth magnets" and "aluminum die casting"; drop/flag "polyethylene pellets" and "carburetor" with reasons (function mismatch, unrelated end-use).
Scenario
During BOM ingestion for a manufacturing client, the procurement team uploads an automated supplier/parts list from multiple legacy sources. 产业链分析专家0306 runs a relevance pass that removes mis-tagged items and returns a cleaned, prioritized upstream candidate list so procurement focuses on real suppliers rather than noise.
Relationship classification into the six standardized categories
Example
Product = "Lithium-ion battery cell"; nodes evaluated = {"graphite anode": "production raw material", "electrode coating line": "production equipment", "electrolyte additives": "auxiliary raw material", "formation testing system": "auxiliary equipment", "cell testing & certification services": "technical services", "EV OEM": "application/sales"}.
Scenario
An R&D/product engineering team mapping their supply ecosystem for a new cell design asks for an explicit node-type map. The assistant outputs a node table classified by the six categories (production equipment, production raw materials, auxiliary raw materials, auxiliary equipment, technical services, application/sales) so engineering, procurement and compliance teams share a single taxonomy for downstream workflows.
Quantitative relationship-strength scoring and one-sentence justification (1–5 level)
Example
Upstream scoring example (graphite anode -> battery cell): using upstream weights (cost share 30%, substitutability 25%, supply stability 15%, tech/quality 20%, market impact 10%) assign sub-scores: cost share=3, substitutability=3, supply stability=3, tech/quality=4, market=3. Compute: 3×0.30=0.90; 3×0.25=0.75; 3×0.15=0.45; 4×0.20=0.80; 3×0.10=0.30 → sum = 0.90+0.75+0.45+0.80+0.30 = 3.20. Mapping: 3.20 falls in 3.0–3.49 → Level 4 ("strong"). Downstream scoring example (battery cell -> EV OEM): application directness=4, market dependence=4, tech service demand=4, substitutability=4 → sum 4+4+4+4=16; average 16/4=4.00 → average 3.5–4.0 → Level 5 ("very strong").
Scenario
A private-equity team performing commercial due diligence for an acquisition requests a ranked list of top-10 upstream and downstream dependencies by systemic criticality. 产业链分析专家0306 returns a scored list, mapped 1–5, with the numeric detail for each scoring dimension, plus a one-sentence actionable reason (e.g., "Level 5 — critical: >30% cost share, no practical substitute, and directly impacts performance"). Outputs can be exported to CSV or Excel to feed risk heatmaps and sensitivity models.
Primary target user groups and why they benefit
Manufacturers, Procurement & Supply-Chain Teams (BOM engineers, procurement managers, operations leads)
Why they need it: they must ensure continuity of supply, optimize costs, and prioritize supplier relationships. How they use it: clean and validate BOM/supplier lists; identify which upstream inputs are high-risk (high cost share, low substitutability), produce prioritized mitigation plans (dual-sourcing, inventory hedging, qualification of substitutes). Example benefit: converting a chaotic supplier list into a ranked "critical supplier" list with clear, auditable reasons and per-node mitigation suggestions, reducing procurement time and focusing negotiation effort on the top 10% of risk contributors. The assistant uses the upstream scoring standard and the six-category taxonomy to deliver these outputs.
Investors, Strategy & Due-Diligence Teams, Industry Analysts, Consultants
Why they need it: to evaluate strategic exposure, concentration risk, and market dependencies that affect valuation or strategic decisions. How they use it: map target companies' supply-chain exposures, quantify node importance (1–5), create scenario stress tests (supply shock, price spike, substitute emergence) and produce one-sentence justifications that support investment memos. Example benefit: an investment analyst can quickly identify that a target’s critical upstream material is "Level 5 — critical" and therefore recommend contractual or CapEx adjustments (e.g., vertical integration or long-term contracts). The assistant structures findings to feed investor models and strategic playbooks, citing the downstream and upstream scoring frameworks where appropriate.
How to use 产业链分析专家0306
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Prepare inputs and prerequisites
Define the target product keyword(s) and prepare candidate upstream/downstream node lists (BOM lines, supplier names, component keywords). Provide context: region, market segment, timeframe, and priority (cost, quality, stability). Acceptable formats: CSV, XLSX, or plain text lists; optional: technical docs or BOM files to improve accuracy. Prerequisite: basic familiarity with the product and its market; optional domain files speed up higher-quality outputs.
Configure analysis and run
Choose analysis scope (upstream, downstream, or both), select or accept default scoring standards, and set optional weight adjustments for scoring dimensions. The system will filter incorrect nodes, classify each node into one of six relation types, and compute a 1–5 strength rating with a one-sentence rationale. The built-in upstream and downstream scoring standards areHow to use 产业链分析专家0306 used by default for consistency.
Review outputs and refine
Outputs include: filtered node list, relation-type labels (six-category taxonomy), numeric strength scores (1–5), and a concise reason for each score; you can export CSV/JSON/PPT and request sensitivity or weight-adjusted re-runs. Relation-type classification follows the six-category definitions used in the tool.
Apply insights and iterate
Use generated insights for supplier selection, risk assessment, sourcing strategy, product roadmap, or investment memos. Tips for optimal results: provide clear context (region/timeframe), include complete BOM lists where possible, run comparative scenarios (e.g., alternate suppliers or substitute materials), and iterate—small input changes quickly reveal sensitive nodes.
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Five common Q&A about 产业链分析专家0306
What is 产业链分析专家0306 and what problems does it solve?
It is an AI-driven supply-chain analysis assistant that maps a product to its upstream and downstream ecosystem, filters incorrect nodes, classifies relationship types, and scores the strength of each node’s linkage (1–5) with a concise rationale—helping teams prioritize suppliers, assess risk, and guide sourcing or investment decisions. Relation categories follow the tool's six-category taxonomy.
What inputs are required and what formats are supported?
Minimum input: a product keyword plus a list of candidate nodes (components, raw materials, services, channels). Supported formats: CSV, XLSX, and plain text. Optional but helpful inputs: BOMs, supplier lists, regional market constraints, and business priority weights (cost vs. stability). The clearer and more granular the list, the better the node-filtering and scoring quality.
How does the tool define and categorize upstream/downstream relationships?
Relationships are classified into six types: Production Equipment, Production Raw Materials, Auxiliary Materials, Auxiliary Equipment, Technical Services, and Application/Sales — each with clear definitions and examples to place a node correctly as upstream or downstream. This taxonomy is the foundation for filtering and labeling nodes.
How are strength scores calculated and what do they mean?
The tool uses separate upstream and downstream scoring frameworks. Upstream scoring evaluates dimensions like cost share, substitutability, supply stability, technical/quality impact, and market impact with weighted aggregation; downstream scoring evaluates application directness, market dependency, technical-service demand, and substitutability. The aggregated average maps to a 1–5 strength level (1 = very weak, 5 = very strong). Default dimension weights and thresholds follow the built-in upstream and downstream scoring standards.
How can I consume and integrate the results into workflows?
Exportable outputs (CSV/JSON/PPT) let you plug scores and rationale into supplier shortlists, risk matrices, investment memos, and dashboards. Use the tool’s sensitivity runs to test alternate weights or substitute materials, then iterate inputs and re-run to produce scenario-based decision decks for stakeholders.