Where is the imbalance? — DOH by Category, Location & Velocity
6 of 8
Categories Above Target DOH
Packaged Foods (52 days) contributes 15.8 days and Staples (38 days) contributes 7.7 days — together 62% of the 38-day weighted system DOH.
Packaged Foods
Largest Excess Category
52-day DOH against a 25-day target — ₹18.7 Cr on the books, the single biggest category pool of trapped working capital.
Mumbai DC
Largest Excess Concentration
₹8.4 Cr excess sitting in Mumbai Central DC — single biggest node-level pool of trapped working capital across the network.
Category Contribution to System DOH (38 days)
Each segment's weighted contribution to the 38-day system average — (stock weight × category DOH)
Location Contribution to System DOH (38 days)
Each location's weighted contribution to the 38-day system average — (stock weight × location DOH)
Sales vs Inventory Value Contribution
Above the diagonal = over-invested in inventory relative to sales (where cash is trapped). Below = under-invested. Bubble size = category DOH.
Sales Contribution by Category (%)
Each category's share of total revenue — read alongside inventory contribution to spot mis-investment
Actual vs Target DOH by Category
Red bars exceed target — excess days represent blocked working capital
Supplier Contribution to System DOH (38 days)
Each supplier's weighted contribution to the 38-day system average — (stock weight × supplier-driven DOH)
Why is it happening? — Demand Variability, Planning Gaps & Supplier Constraints
+18 excess days
Root Causes Identified
High supplier MOQ contributes 4 days of avoidable excess DOH. Low supplier frequency adds 3 days, high safety stock 2.5, and order-quantity logic 2 days. Together these four causes explain ~65% of the 18-day excess above target; a Misc pool of excess not yet pinned to a single named driver makes up the rest alongside the smaller causes.
Overforecast bias
Driving Excess Orders
A structural overforecasting bias in the stable, high-volume categories (Packaged Foods +18%, Staples +14%) inflates order quantities and locks working capital. Low overall error masks it — they quietly over-order every cycle. The fix is dampening forecasts there, not amplifying them.
+9,800 units
Excess Safety Stock in Stable Categories
Staples carries 9,800 units excess safety stock; P. Care and Pkg Foods carry 4,700 and 6,800 respectively. Flat days-of-cover rule over-invests in low-variability categories — direct working capital lockup.
Root Cause Contribution to Excess DOH — Waterfall (days)
System DOH = 38 days. Target = 20 days. Each bar shows how many days of DOH each root cause adds; addressing them all gets close to target.
Weighted Cover Days by Supplier
Days of demand the stock sourced from each supplier covers, sales-weighted across the supplier's SKUs (Σ sales×cover ÷ Σ sales = total stock ÷ total throughput), not a simple average. High cover days = too much stock for the throughput, driven by infrequent ordering and large MOQs. Above the ~20-day benchmark is an excess driver.
Demand Variability by Category (CV)
CV > 0.30 requires dynamic safety stock — static logic fails above this threshold
Overforecast Bias by Category (%)
Positive bias = systematic overforecasting that inflates orders and locks working capital. The stable, high-volume categories (Packaged Foods, Staples) are the biggest offenders — low overall error, but a persistent over-bias. Negative bias is not an excess driver.
Safety Stock Gap by Category — Weighted % vs Required
Positive % = excess safety stock (working capital locked unnecessarily) and is where cash is released. Negative % = categories the availability guardrail protects from cuts. Bars weighted by category stock value.
2A | Supplier MOQ Analysis — Contribution to Excess DOH
Supplier minimum order quantities force over-purchasing beyond immediate demand. Where MOQ-implied DOH exceeds the target DOH, the supplier contract is itself a cause of excess inventory — not a planning or forecasting failure.
| Supplier | Category | MOQ (units) | Daily Demand | MOQ Implies DOH | Target DOH | Excess DOH from MOQ | WC Impact (₹ L) | Action Required |
|---|---|---|---|---|---|---|---|---|
| ITC Agri | Staples | 4,200 | 110 | 38 days | 20 days | 18 days | ₹14.2 | Negotiate MOQ reduction or split deliveries |
| HUL Distribution | Personal Care | 3,600 | 88 | 41 days | 22 days | 19 days | ₹11.8 | Request bi-weekly instead of monthly drops |
| Britannia Ind. | Packaged Foods | 5,000 | 96 | 52 days | 25 days | 27 days | ₹18.6 | Introduce SKU-level MOQ caps |
| Dabur India | Health/FMCG | 2,800 | 66 | 42 days | 22 days | 20 days | ₹8.4 | Renegotiate quarterly contract terms |
| Amul (Gujarat Co-op) | Dairy | 1,800 | 129 | 14 days | 12 days | 2 days | ₹1.8 | Within acceptable range — monitor |
| McCain Foods | Frozen | 900 | 38 | 24 days | 10 days | 14 days | ₹3.2 | Switch to weekly small-batch ordering |
| Local Produce Co. | Fresh Produce | 600 | 89 | 7 days | 7 days | 0 days | ₹0.0 | No MOQ issue — supply-constrained category |
2B | Replenishment Frequency — Supplier Drops & DC-to-Spoke Transfers
Low replenishment frequency forces higher cycle stock to cover longer replenishment cycles. Moving from monthly to weekly drops directly reduces the cycle stock DOH needed — no system change required, just a scheduling and logistics decision.
| Location / Route | Category | Current Frequency | Recommended | Cycle DOH (current) | Cycle DOH (target) | DOH Reduction Possible | Volume (₹ Cr/wk) | Priority |
|---|---|---|---|---|---|---|---|---|
| Supplier → Mumbai DC | Packaged Foods | Monthly | Weekly | 28 days | 7 days | −21 days | ₹4.2 | HIGH |
| Supplier → Mumbai DC | Staples | Bi-weekly | Weekly | 14 days | 7 days | −7 days | ₹3.1 | HIGH |
| Mumbai DC → Nashik Spoke | All Categories | Monthly | Weekly | 28 days | 7 days | −21 days | ₹0.6 | HIGH |
| Mumbai DC → Aurangabad | All Categories | Monthly | Bi-weekly | 28 days | 14 days | −14 days | ₹0.4 | MED |
| Supplier → Mumbai DC | Personal Care | Monthly | Bi-weekly | 21 days | 10 days | −11 days | ₹2.2 | MED |
| Supplier → Pune Hub | Beverages | Bi-weekly | Weekly | 14 days | 7 days | −7 days | ₹1.8 | LOW |
| Pune Hub → Kolhapur | All Categories | Monthly | Bi-weekly | 21 days | 10 days | −11 days | ₹0.3 | LOW |
2C | Overforecasting — Excess Order Risk
A persistent positive forecast bias sizes orders for demand that doesn't materialise, inflating inventory. The bias concentrates in the stable, high-volume categories — they carry low overall error but quietly over-order every cycle, which is exactly where the excess builds. The fix is dampening forecasts there, not chasing accuracy on the volatile tail. Categories that under-forecast are not an excess driver.
| Category | Forecast Method | Overforecast Bias | Excess Order Effect | Recommended Fix |
|---|---|---|---|---|
| Packaged Foods | Static monthly | +18% | Largest over-order pool — top-50 SKUs drive it | SKU-level forecast + bias correction for top 50 SKUs |
| Staples & Grains | Static monthly | +14% | High volume × steady over-bias = big rupee impact | Apply bias correction in the planning system |
| Personal Care | Static monthly | +12% | Consistent over-order on stable demand | Move to rolling 4-week forecast; dampen bias |
| Beverages | Static monthly | +7% | Moderate over-order, seasonal swings | Seasonality-adjusted monthly forecast |
| Dairy & Chilled | Supplier-led | +1% | Roughly unbiased | Maintain — within acceptable range |
| Frozen Foods | Static weekly avg | −6% | Under-forecast — not an excess driver | Out of scope for excess release |
| Fresh Produce | Static weekly avg | −9% | Under-forecast — not an excess driver | Out of scope for excess release |
2D | Safety Stock — Over-investment in Stable Categories
Safety stock should scale with demand variability (CV) and lead time uncertainty. Current SS is set using a flat days-of-cover rule — causing systematic over-investment in low-CV categories (Staples, P. Care, Pkg Foods) where working capital is locked unnecessarily. Cash is released only from these over-invested categories; the high-CV categories (Dairy, Fresh, Frozen) sit under the availability guardrail and are not cut.
| Category | CV (demand variability) | Classification | SS Held (units) | SS Required (units) | Gap (+ excess / − short) | Value at Risk (₹ L) | Required Fix |
|---|---|---|---|---|---|---|---|
| Staples & Grains | 10% | Low | 18,200 | 8,400 | +9,800 | ₹12.4 | Reduce SS to formula-derived level |
| Dairy & Chilled | 30% | High | 2,800 | 4,800 | −2,000 | ₹4.8 | Increase SS using CV-adjusted formula |
| Packaged Foods | 12% | Low-Med | 14,200 | 7,400 | +6,800 | ₹8.6 | Reduce SS — over-invested relative to CV |
| Beverages | 25% | High | 6,400 | 9,200 | −2,800 | ₹3.4 | Increase SS — under-protected for variability |
| Personal Care | 11% | Low | 7,800 | 3,100 | +4,700 | ₹5.9 | Reduce SS — flat rule over-estimates need |
| Frozen Foods | 35% | High | 1,200 | 2,200 | −1,000 | ₹1.8 | Increase SS — high CV needs more buffer |
| Fresh Produce | 40% | Very High | 1,400 | 3,200 | −1,800 | ₹2.1 | Increase SS significantly — highest variability |
What to change & what is the impact? — Prioritised Actions with Quantified Impact
📌 What type of impact are we measuring?
The ₹18.3 Cr figure is a working capital / cash flow impact — not bottom-line profit. It represents cash currently tied up in excess inventory that gets released back to the business as stock is reduced to optimal levels. Think of it as cash sitting on shelves that gets converted back into liquid capital.
Scope note: this diagnostic is focused exclusively on excess inventory release.
Guardrail — sales-weighted availability is protected: every recommended action has been validated against the baseline sales-weighted availability metric. Excess is released from tail SKUs and over-invested low-CV categories; top SKUs that drive revenue are not touched. Sales-weighted availability holds at baseline or improves under the action plan.
Scope note: this diagnostic is focused exclusively on excess inventory release.
Guardrail — sales-weighted availability is protected: every recommended action has been validated against the baseline sales-weighted availability metric. Excess is released from tail SKUs and over-invested low-CV categories; top SKUs that drive revenue are not touched. Sales-weighted availability holds at baseline or improves under the action plan.
₹18.3 Cr
Working Capital Released (Cash Flow)
~63% of the ₹29 Cr excess released over 90 days. No new systems required — purely operational and planning changes.
−20 days DOH
Inventory Efficiency Improvement
Weighted system DOH reduces from 38 → ~18 days, moving toward the 20-day target on the excess-inventory dimension.
Action plan
Prioritised, Sequenced, Owned
Each action mapped to owner, KPI, baseline → target, and ₹ Cr cash released. ~65% of the unlock comes from Month 1 actions.
Working Capital Waterfall (₹ Cr) — Cash Flow Impact
How each action releases cash from the ₹29 Cr currently locked in excess inventory
WC Unlock by Action (₹ Cr)
Ranked by cash flow release — Month 1 actions deliver 65% of total unlock
Full Prioritised Action Plan — tiered by who owns the fix
Tier 1 = in our hands (internal planning/ops, start immediately); Tier 2 = in the supplier's hands (needs negotiation, run in parallel). Each action maps to one named root cause. WC Impact = cash released in 90 days — it is less than the cause's attributed excess because the rest drains slower than 90 days or the fix is partial (that gap is the ₹10.7 Cr residual).
| Action | Tier | What Exactly to Do | Owner | Timeline | KPI | Baseline | Target | WC Impact |
|---|---|---|---|---|---|---|---|---|
| Right-size safety stock in low-CV categories | T1 — ours | 1. Run CV calculation for all SKUs using last 12 weeks of sales. 2. Apply SS formula: SS = Z × σ × √LT (Z=1.65 for 95% service level). 3. For low-CV SKUs (Staples, P. Care, Pkg Foods), reduce SS to formula-derived level. 4. Update SS parameters in ERP/planning tool; track weekly. |
Planning Team | Month 1 | Excess SS Units | 25,500 | 8,500 | ₹3.2 Cr |
| Reset reorder qty for slow-movers (>45 DOH) | T1 — ours | 1. Pull list of all SKUs with DOH >45 days (Staples, Pkg Foods). 2. Calculate demand-aligned reorder qty = avg daily demand × reorder cycle. 3. Issue revised PO caps to procurement team. 4. Monitor DOH weekly for 4 weeks. |
SC Head | Month 1 | DOH — Staples + Pkg Foods | 45 days | 24 days | ₹2.8 Cr |
| Redistribute excess from Mumbai DC across network | T1 — ours | 1. Build weekly stock visibility report across all DCs and spokes. 2. Trigger: spoke DOH <5 days AND DC DOH >20 days = transfer. 3. Assign a logistics coordinator to execute weekly. 4. Stop incremental Mumbai DC ordering for SKUs being redistributed. |
Logistics Mgr | Month 1 | Mumbai DC excess stock | ₹8.4 Cr | ₹6.6 Cr | ₹1.8 Cr |
| Liquidate dead stock (>90 DOH SKUs) | T1 — ours | 1. Extract all SKUs with DOH >90 days (~₹9.2 Cr book value). 2. Categorise: sell via distributor discount, return to supplier, or write off. 3. Execute discount campaign for top 30 SKUs. 4. Set policy: any SKU hitting 75 DOH triggers automatic review. |
Category Mgr | Month 1 | Dead Stock Value | ₹9.2 Cr | ₹7.8 Cr | ₹1.4 Cr |
| Tighten order compliance at goods-in | T1 — ours | 1. Audit last 90 days of inbound POs for pack-size and lot-multiple breaches. 2. Quantify excess units received vs ordered. 3. Enforce pack-size at goods-in; reject over-receipts. 4. Monthly supplier scorecard on compliance. |
Procurement | Month 2 | Compliance breach rate | 14% | 4% | ₹1.1 Cr |
| Reduce overforecasting on top movers | T1 — ours | 1. Top-50 SKU forecast bias review with sales. 2. Where bias > +12%, downscale the baseline forecast. 3. Move to rolling 4-week forecast for top movers. 4. Monthly accuracy review; cut buffer ordering tied to inflated forecasts. |
Planning Team | Month 3 | Forecast bias (top 50) | +18% | +6% | ₹0.3 Cr |
| Renegotiate Supplier MOQs | T2 — supplier | 1. Identify top 4 MOQ-driven excess suppliers (Britannia, HUL, ITC, Dabur). 2. Build MOQ-implied DOH vs target DOH table per supplier. 3. Negotiate SKU-level MOQ caps or move to bi-weekly drops. 4. Track adherence weekly. |
Procurement | Month 2 | Avg MOQ-implied DOH (top 4) | 43 days | 28 days | ₹4.5 Cr |
| Increase supplier replenishment frequency | T2 — supplier | 1. Map all supplier→DC lanes by current cadence. 2. Target lanes with cycle DOH >14 days. 3. Negotiate monthly→weekly (or bi-weekly where weekly infeasible) drops. 4. Coordinate with logistics on the inbound schedule. |
SC Head | Month 1 | Avg cycle DOH | 22 days | 9 days | ₹3.2 Cr |
| TOTAL (90 days) | ₹18.3 Cr cash |