Cracking the Code: Market & Competitor Insights for FMCG Warehouses
Introduction
In FMCG, market success is won (or lost) in the last mile: competitive intensity around each warehouse, the distribution reach to retail, and how reliably product moves from hubs to shelves. This post shows how Terno AI turns the Noodles warehouse dataset into market and competitive insight, using conversational prompts to profile competition by zone, quantify the role of hub distance, and compare operational efficiency across locations. The result is a set of targeted actions to deepen market penetration and reduce distribution risk.
Terno AI session: https://monal.app.terno.ai/chat/share/7ed9c946-70d2-4e4e-a06c-56cdb87f5c93
Dataset & Method
The attached dataset (25k warehouse records) spans: warehouse attributes (capacity, location type, zone), competition density (Competitor_in_mkt), distribution reach (retail shop and distributor counts), distance to hub (dist_from_hub), and operations (refill requests, breakdowns, flood impact/proof, regulatory checks, and product throughput in tons).
I used Terno AI with prompts (below) to produce descriptive summaries, distribution views, and simple diagnostic comparisons.
Insights from Terno AI (Prompt-by-Prompt)
Prompt:
“Describe the attached dataset structure, including column types, missing values, and summary statistics.”
Prompt emphasizes operational readiness: it not only catalogs columns and gaps but also flags which fields are updated in near real-time versus stale, which are audit-prone, and which may need enrichment (e.g., more granular workforce shift data). That operational metadata matters because models and dashboards must rely on timely inputs for routing and refill decisions. Knowing refresh cadence and accuracy informs whether to use the dataset for strategic planning only or for near-term operational triggers too.
Insight:
Prompt:
“Explain Competitor_in_mkt distribution across warehouses and zones. Visualize top 5 zones with highest competition.”
Mapping competitor density reveals where market share is contested and price/promotional pressure will be highest. Visualizing the top zones shows where defensive tactics (stock availability, promotions, trade spend) must be concentrated. For go-to-market decisions, high-competition zones may require more aggressive OTIF, temporary price concessions, or exclusive promotions, while low-competition zones provide an opening to expand distribution and gain share. The visualization aids sales and regional leadership in prioritizing territory investments.
Insight:
Prompt:
“Examine dist_from_hub impact on refill requests and product throughput. Plot correlation matrix.”
This prompt tests whether logistical distance is a driver of refill frequency or throughput loss. The correlation matrix also surfaces other related relationships (e.g., distance correlating with transport issues or breakdowns). If distance shows a material effect, route redesign or additional micro-hubs may be warranted; if not, investment can be focused elsewhere. The result supports quantitative decisions around hub placements, last-mile partnerships, and delivery batching strategies.
Insight:
There is no meaningful linear relationship between how far a warehouse is from its hub and either its recent refill‐request volume or its product throughput.
Prompt:
“Compare warehouses near vs far from hubs for operational efficiency and market reach.”
A median-split comparison reveals whether proximity to hubs delivers operational advantage (fewer refills, higher throughput) and greater market reach (more retailers served). If “near” warehouses consistently outperform, the company may prioritize clustering stock near hubs; if the effect is muted, it may instead optimize distribution patterns or invest in transport. This informs long-term network design and whether to pursue decentralized vs hub-and-spoke strategies.
Insight:
It shows that being farther from the distribution hub does not degrade refill efficiency nor reduce the combined retail-shop + distributor network.
Prompt:
“Visualize effect of regulatory checks (govt_check_l3m) on warehouse performance.”
Regulatory inspections can be disruptive but also enforce standards that prevent long-term failures. Visualizing their impact shows whether frequent checks correlate with lower throughput or instead with fewer breakdowns and higher compliance. If checks are neutral or positive, operations should institutionalize documentation practices; if negative, work with authorities to streamline inspection windows. For compliance teams, it helps schedule checks and prepare remediation plans without affecting commercial performance.
Insight:
There is no material effect of regulatory‐check frequency on either monthly throughput or refill‐request activity.
Prompt:
“Analyze flood_impacted and flood_proof warehouses in high competitor zones. Provide visual comparison.”
By focusing on the intersection of weather risk and competitive intensity, this analysis reveals where resilience has the highest strategic value — i.e., maintaining supply where rivals may falter during floods. If flood-proof warehouses in competitive zones maintain availability and win shelf share, the business case for targeted infrastructure investment is clear. Sales and risk teams can then prioritize flood-proofing or contingency stock in these zones to protect revenue and reputation.
Insight:
Prompt:
“Provide insights on market gaps, distribution inefficiencies, and high competition zones. ”
This prompt synthesizes prior analyses to pinpoint white spaces (e.g., zones with low competitor presence but poor distribution reach), inefficiencies (e.g., refill clustering in certain hubs), and red-flag zones where competition and supply risk coincide. These actionable insights guide expansion (where to grow), remediation (which routes and hubs to optimize), and defense (where to secure inventory and promotions). Executives can use this to align commercial and operations plans for targeted market moves.
Insight:
Prompt:
“Recommend strategic actions to improve market penetration, reduce operational risks, and optimize distribution.”
This final prescriptive prompt generates a prioritized roadmap: expand retail/distributor coverage in low-competition areas, invest in flood-proofing for high-value competitive zones, implement predictive maintenance for breakdown-prone sites, and re-balance inventory to reduce refill hotspots. Each action should be accompanied by expected impact, cost/benefit, and owner — turning analytics into accountable execution. Management uses this as a strategy blueprint to optimize ROI across capex, opex, and commercial investments.
Insight:
Conclusion
Terno AI transforms routine warehouse tables into a market & competition console: ranking zones by rivalry, validating what does (and doesn’t) drive refills and throughput, and spotlighting resilience as a differentiator when competition is fierce. For the noodles category—where availability is everything—the playbook is clear: defend in high-competition zones with service and resilience, expand in low-competition zones with reach and coverage, and iterate weekly with AI-assisted measurement.
Terno AI session: https://monal.app.terno.ai/chat/share/7ed9c946-70d2-4e4e-a06c-56cdb87f5c93
