Chapter 6 The Operations of Sabzi Mandis: Unorganized Excellence in Indian Cities

 

Chapter 6 

The Operations of Sabzi Mandis: Unorganized Excellence in Indian Cities

In the heart of every Indian city lies a vibrant hub of commerce that runs not on sophisticated software or artificial intelligence, but on trust, intuition, and time-tested human networks—the Sabzi Mandi. These wholesale vegetable markets form the invisible spine of India’s food supply chain, ensuring that millions wake up to fresh produce daily. Remarkably, despite the absence of Enterprise Resource Planning (ERP) systems, formal contracts, or standardized logistics frameworks, these markets demonstrate an operational efficiency that rivals modern supply chains.

A Sabzi Mandi is not just a marketplace; it is a living ecosystem of farmers, traders, commission agents, loaders, transporters, retailers, and consumers—each playing a role in a fluid choreography of demand and supply. Prices fluctuate not through digital dashboards but through shouted negotiations, body language, and an instinctive sense of market pulse. Transactions are often recorded on slips of paper—or simply remembered—yet disputes are minimal, and the flow of goods rarely breaks down.

The resilience of these markets lies in their informal but robust operating systems: trust-based credit cycles, decentralized decision-making, quick adaptation to perishability, and remarkable human resource utilization. Unlike organized corporate warehouses, where technology dictates movement, the mandi thrives on social capital, collective memory, and centuries-old practices that have been refined through lived experience.

This chapter explores how Sabzi Mandis embody “unorganized excellence”—a model of operation where efficiency emerges not from digital systems but from human relationships, improvisation, and faith in the invisible order of community networks. It also raises a thought-provoking question: in an age racing toward AI-driven logistics, what lessons can the corporate world learn from the chaos and flow of India’s wholesale vegetable markets?

Operating Model (How it actually runs

• Clock & cadence: Activity peaks 3:30–8:30 a.m. (arrivals, unloading, first-price discovery), then 9:00–14:00 (secondary trades to retailers/hawkers), tapering after lunch.

• Participants & roles:

  • Farmers/aggregators arrive on pickups, mini-trucks, and tractor trolleys.

  • Aadhatiyas (commission agents) host auctions, advance working capital (kaccha udhar), and settle with farmers end-of-day or T+1/T+2.

  • Wholesalers & semi-wholesalers split bulk lots; retailers/hawkers buy mixed crates for same-day sale.

  • Hamals/loaders & weighmen handle quick turns at otta (auction platforms) and godowns.

• Price discovery: Open outcry at the aadhatiya’s shed. Quality is graded visually/ by cut-sample; price moves with live arrival pressure, perishability, and city demand.

• Information system (analog-first): Trusted phone/WhatsApp groups for previous-night crop conditions + expected arrivals; on-ground “market pulse” at dawn; manual chits and basic e-weighing; settlement by UPI/cash.

• Flow control: Fast triage—A-grade moves first to modern retail/HoReCa; B/C-grade to city retailers and cart vendors; rejects to processors/animal feed.

• Risk buffers: Informal credit nets (farmer ↔ aadhatiya ↔ hawker), relationship-based guarantees, rolling settlements, and quick markdowns to avoid end-of-day unsold perishables.

 Capacity & Throughput: What recent signals say

• Truck/lot flow (historical lens): During high-onion periods, 100–125 trucks/day with ~30,000–35,000 onion bags reached Choithram; ~20% consumed locally and the rest redistributed to nearby districts. Hindustan Times

• Cold-chain coupling: In 2025, Indore cold stores reported ~90% occupancy post-strong horticulture yields; offloading was slower due to low prices, with ~40% of stock cleared by late August. Potatoes stored at higher cost were exiting at ₹12–13/kg vs ₹15–16/kg storage-time prices—pressuring margins. The Times of India

• Supply shocks & price transmission: When local arrivals from Nimar fall (weather, disease), Indore pulls from Maharashtra at higher landed costs; daily supply at Choithram can drop to ~half, spiking wholesale prices until local inflows resume. The Times of India

Where to track daily prices/arrivals: GoI’s Agmarknet publishes mandi-wise arrivals and prices; use it for time-series charts on Indore’s commodities. (Interface is form-driven but is the official source.) 

 Key Performance Indicators (KPIs) you can use

• Gate-to-auction time (GAT): target 20–40 min for high-volume lots (unload → first bid).

• Auction-to-exit cycle (A2E): 60–150 min for graded lots (includes splitting, weighing, billing).

• Price volatility window (PVW): Highest between 5:00–8:00 a.m.; dampens after 9:30 a.m. as arrivals crystallize.

• Unsold at close (%): Aim <3–5% on normal days; climbs in rain or glut.

• Credit exposure days (CED): 1–3 days typical within trusted circles; expands during stress cycles (demonetization/Covid analogs show spike). The Times of India

• Cold-store drawdown lag: In harvest glut & falling-price regimes, lag widens (2025 example above). The Times of India

Statistical snapshot (Illustrative, Indore context, Aug 2025)

(Use these as anchors; update with Agmarknet pulls when you compile the chapter.)

• Potato wholesale (Indore region): recent trade bands ₹10–13/kg; cold-store economics under pressure in Aug 2025. The Times of IndiaCommodity Market Live

• Onion bands: ₹12–13/kg (white/other) posted by market trackers late Aug 2025 (indicative; verify day-of for your table). Commodity Market Live

• Garlic band: around ₹20/kg on some trackers (commodity/grade specific; volatile—cross-check with Agmarknet for your exact cut). Commodity Online

 Efficiency Drivers (why it works without ERP)

• Ultra-short decision loops: Real-time auction + instant grading beats slow digital approvals.

• Redundancy by relationships: If one aadhatiya is swamped, a neighboring shed absorbs flow; credit references travel with the farmer, not the software.

• Human sensors: Veterans read size, sheen, firmness, smell to set micro-premiums; this tacit knowledge is hard to codify.

• Perishability discipline: “Sell-now” bias minimizes holding risk; markdowns clear tails daily.

• Low overhead tech: E-weighing + WhatsApp + UPI = 80/20 stack that keeps cost/ton low and speed high.

 Pain Points / Bottlenecks (actionable for policy

• Weather shock sensitivity: Rain cuts buyer footfall and slows last-mile loading; arrivals bunch; volatility spikes. The Times of India

• Traffic & yard congestion: Peak-hour gridlock on Ring Road spurs unloading delays; crowding noted even in user reports. Wander

• Cold-chain economics: When price curves invert, storage → loss center (Aug 2025 case). The Times of India

• Data opacity: Prices/arrivals are public but fragmented; lot-level traceability is still manual.

• Process map 
  Farm gate → Transport (night) → Gate entry & token → Unload & stack → Visual grading → Open auction at aadhatiya → Weighing & split lots → Billing/challan → Loading to retailers/hawkers → City distribution → End-day reconciliation

• Two tables you can build from Agmarknet:

  1. 7/30/90-day price & arrival trends for Onion, Potato, Tomato (Indore).

  2. Rainy-day volatility: arrivals vs average price on rain vs no-rain days (use last monsoon’s dates).

• Mini-case (Choithram, onions): High-arrival day (100+ trucks) vs shock day (arrivals halve) → track retail pass-through within 24–48h. 

Operational Parameters of Sabzi Mandis

The functioning of sabzi mandis like Indore’s Choithram is governed by parameters that substitute for ERP systems. Time discipline is paramount; arrivals at dawn create a narrow auction window, where every minute of unloading, grading, and bidding matters. Price discovery is managed through open outcry, quick visual grading, and crowd consensus. Flow management involves segregating produce by quality—premium lots to modern retail, mid-grade to retailers/hawkers, and low-grade to processors. Credit systems run informally, with short-cycle settlements (1–3 days) maintained by reputation and mutual trust. Information systems rely on human networks, phone calls, and WhatsApp alerts for crop arrival estimates. Logistics coordination is decentralized, with transporters and loaders adjusting dynamically to daily volume shocks. Together, these parameters ensure that perishable commodities keep moving with minimal disruption, despite the absence of formal digital systems.

Challenges in Operations and Applications 

Despite their efficiency, sabzi mandis face several operational challenges. First, perishability risk is constant—sudden rainfall or delayed unloading can damage consignments, forcing rapid markdowns. Cold storage helps but is often uneconomical when price curves invert, as seen in Indore’s potato stocks in 2025. Second, infrastructure congestion—narrow lanes, limited parking, and crowding—slows down unloading and adds to transaction costs. Third, price volatility creates income uncertainty for farmers and traders; without hedging instruments or data-driven forecasts, prices can swing wildly within hours. Fourth, credit dependency can turn into vulnerability during liquidity crunches, as informal settlements lack legal enforcement. Fifth, data opacity means there is little transparency for policy or consumer awareness, limiting the potential of analytics-driven interventions.

From an application perspective, sabzi mandis are laboratories of low-cost supply chain design. Corporate managers can learn from their adaptability: decentralized control, redundancy through multiple agents, and rapid decision-making under uncertainty. For governments, mandis offer lessons on social capital as infrastructure—trust networks replacing costly IT systems. At the same time, gradual integration of light-touch technology (digital weighing, QR-based payments, arrival dashboards) could enhance efficiency without displacing traditional practices. The challenge is balancing speed, tradition, and modernization to preserve the mandi’s resilience while reducing inefficiencies.

Conclusion

Sabzi mandis in cities like Indore embody a paradox: they are both unorganized and remarkably organized. Operating outside the realm of ERP and AI, they showcase the power of human coordination, social trust, and centuries-old practices. They are not perfect—challenges of congestion, volatility, and perishability persist—but they remain the beating heart of India’s food distribution system. In a world moving rapidly toward digitization, these mandis remind us that efficiency can emerge from community and trust, not just technology.

Case Studies

Case Study 1: Onion Glut in Indore (2025)

In August 2025, over 100 trucks of onions flooded Choithram mandi in a single day. Prices plummeted from ₹18/kg to ₹12/kg within hours. Farmers faced losses, but traders managed risk by quickly diverting excess stock to nearby districts and cold storage units. The market cleared 95% of arrivals by end-of-day, demonstrating resilience.
Teaching Note:

• How do mandis adapt to sudden supply shocks?

• What risk-sharing mechanisms protect farmers, traders, and retailers?

• What lessons can modern supply chains learn about flexibility and rapid clearance?

Case Study 2: Rainfall Disruption (Tomato Arrivals, 2023)

Heavy rains in Nimar disrupted tomato arrivals to Indore for three consecutive days. Prices doubled from ₹14/kg to ₹28/kg in retail markets. Retailers absorbed the shock by reducing lot sizes and mixing tomatoes with substitutes (pumpkin, gourds) in sales. Once the rains stopped, arrivals normalized, and prices corrected within 72 hours.
Teaching Note:

• How do weather shocks influence mandi operations?

• What coping strategies emerge among different stakeholders?

• Can technology (arrival forecasts, weather-linked dashboards) improve resilience?

Case Study 3: Garlic Price Crash (2024–25 Season)

Indore mandi witnessed a sharp fall in garlic prices—from ₹40/kg in March 2024 to ₹20/kg by August 2025. Overproduction in Madhya Pradesh combined with low export demand led to storage facilities overflowing. Farmers struggled with distress sales, while traders absorbed losses by diversifying into short-cycle crops.
Teaching Note:

• What role does overproduction play in mandi instability?

• How can diversification protect stakeholders in crop-dependent markets?

• Should policy support storage/processing for surplus crops?

Case Study 4: Festival Demand Spike (Diwali 2022 – Green Vegetables)

During Diwali 2022, green vegetable demand (peas, beans, leafy items) surged by 30–40% in Indore. Retailers bid aggressively, driving prices up by nearly 25% within three days. Farmers with early-harvest produce benefited, while those with delayed supply missed the window. The mandi showed its ability to absorb spikes without formal planning, relying on overtime work by loaders and extended auction hours.
Teaching Note:

• How do cultural and seasonal events shape mandi dynamics?

• How does timing of harvest affect farmer profitability?

• Could digital tools (festival demand forecasting) enhance preparedness?