🇮🇳India

अनुमानों पर आधारित अशुद्ध निर्णय और स्टॉक ओवरराइड (Inaccurate Demand Planning & Stock Imbalance)

2 verified sources

Definition

Furniture companies using annual inventory data cannot detect demand shifts mid-year. A sofa style goes out of favor (e.g., a particular color/fabric), but the company continues purchasing based on stale projections, resulting in 200+ units in dead stock. Conversely, a popular office desk model runs short because the system underestimated demand. Without frequent cycle counts, the supply chain team cannot adjust replenishment levels dynamically. Search result [1] states that cycle counting 'helps catch discrepancies early' and enables 'better planning.' Manual counts conducted quarterly or semi-annually create blind spots lasting 2–3 months.

Key Findings

  • Financial Impact: ₹10–20 lakhs annually per manufacturer (estimated: carrying cost of excess inventory = 15–20% of inventory value annually; for ₹50L inventory value, excess/dead stock of 10–15% = ₹5–7.5L carrying cost + write-offs; typical furniture company sees 5–10% SKU obsoletion annually).
  • Frequency: Ongoing – Quarterly/semi-annual inventory reviews highlight accumulated imbalances.
  • Root Cause: Infrequent cycle counts (annual/semi-annual) delay discrepancy detection; lack of real-time visibility into SKU-level demand; absence of ABC-based forecasting (source [1] recommends ABC categorization for prioritized counting).

Why This Matters

The Pitch: Furniture manufacturers in India waste ₹10–20 lakhs annually on excess/dead stock due to poor demand planning. Real-time cycle count data enables accurate SKU forecasting, reducing inventory carrying costs by 15–25% and freeing working capital.

Affected Stakeholders

Demand Planner, Procurement Manager, Finance Manager, Supply Chain Director, Product Manager

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Financial Impact

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Current Workarounds

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Methodology & Sources

Data collected via OSINT from regulatory filings, industry audits, and verified case studies.

Evidence Sources:

Related Business Risks

इन्वेंटरी गणना के कारण ऑपरेशनल क्षमता में हानि (Warehouse Operational Bottlenecks from Cycle Counting)

₹3–8 lakhs annually per warehouse (estimated: 15–20 delayed orders/week × ₹30k average order value × 4% margin loss × 50 weeks/year = ₹3–6L; also includes overtime costs for off-hours counting, ₹1–2L/year).

खराब गुणवत्ता लागत

₹2-5% of production costs in rework and refunds; heavy losses from customer complaints

निरीक्षण पुनर्कार्य लागत

20-40 hours/month overtime per production line; 5-10% cost overrun on rush rework

वारंटी दावों पर रीवर्क लागत

₹2-5 लाख per 100 claims in repairs/replacements; 20-40 hours/month manual processing per team

फर्जी वारंटी दावों से धोखाधड़ी हानि

1-3% of warranty provisions (₹2-5 लाख/year for mid-size firm); gift vouchers/refunds on invalid claims

BOM आधारित GST ITC समरूपता असफलता

18% GST on disallowed ITC (₹5-15 लाख/year); ₹10,000-50,000 penalty per return

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