Where is the imbalance? — DOH & Availability 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.
Fresh & Frozen
Availability Context (out of scope)
Fresh Produce at 68.4% availability and Frozen at 72.1% — flagged for visibility. Stockout root-cause analysis is not part of this diagnostic; this engagement is focused on excess inventory release.
Mumbai DC
Largest Excess Concentration
₹5.2 Cr excess sitting in Mumbai Central DC — single biggest 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)
Actual vs Target DOH by Category
Red bars exceed target — excess days represent blocked working capital
Availability % by Category
90% threshold — below this line is active stockout risk
DOH vs Availability — Category View
Bubble size = stock value (₹ Cr). Bottom-right = high stock, low availability (worst)
Sales Contribution by Category (%)
Each category's share of total revenue — read alongside inventory contribution to spot mis-investment
Sales vs Inventory Value Contribution
Above the diagonal = over-invested in inventory relative to sales. Below = under-invested (stockout risk). Bubble size = category DOH.
Supplier Contribution to System DOH (38 days)
Each supplier's weighted contribution to the 38-day system average — (stock weight × supplier-driven DOH)
Location Contribution to System DOH (38 days)
Each location's weighted contribution to the 38-day system average — (stock weight × location DOH)
Top 10 SKUs by Stockout Frequency — Last 90 Days
| SKU | Category | Stockout Days | Est. Lost Sales (₹ L) | Avg DOH | Velocity (units/day) | Priority |
|---|---|---|---|---|---|---|
| Amul Full Cream Milk 1L | Dairy | 28 | ₹8.4 | 14 | 142 | HIGH |
| Farm Fresh Spinach 500g | Fresh Produce | 24 | ₹5.1 | 8 | 89 | HIGH |
| Britannia Brown Bread | Packaged Foods | 19 | ₹3.7 | 18 | 74 | HIGH |
| Epigamia Greek Yogurt | Dairy | 17 | ₹4.2 | 11 | 56 | HIGH |
| McCain Frozen Fries 1kg | Frozen | 15 | ₹2.9 | 11 | 38 | MED |
| Lay's Classic Salted 26g | Packaged Foods | 13 | ₹6.8 | 22 | 218 | MED |
| Ensure Nutrition Powder | Health | 12 | ₹9.1 | 19 | 31 | MED |
| Country Delight Butter | Dairy | 11 | ₹3.3 | 13 | 44 | MED |
| Tata Salt 1kg | Staples | 10 | ₹2.1 | 24 | 167 | LOW |
| Dabur Honey 500g | Health | 9 | ₹3.8 | 21 | 52 | LOW |
Why is it happening? — Demand Variability, Planning Gaps & Supplier Constraints
+18 excess days
8 Root Causes Identified
High supplier MOQ contributes 4 days of avoidable excess DOH. Low supplier frequency adds 3.5 days, with incorrect order quantity logic and overforecasting adding 2.5 days each. Together these four causes explain ~70% of the 18-day excess above target.
Overforecast bias
Driving Excess Orders
High WMAPE in 4 of 7 categories — combined with a structural overforecasting bias on the planning side — inflates order quantities and locks working capital. The fix is dampening forecasts in stable categories, 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 — address all 8 causes to get close to target.
Supplier Fill Rate & Delay Frequency
Low fill rate + high delay = primary stockout driver
Demand Variability by Category (CV)
CV > 0.30 requires dynamic safety stock — static logic fails above this threshold
Forecast Accuracy (WMAPE) by Category
Weighted MAPE — lower is better. Categories above the 25% threshold need demand-sensing or rolling forecasts; static reorder points fail above this line.
Safety Stock Gap by Category — Weighted % vs Required
Positive % = excess safety stock (working capital locked unnecessarily). Negative % = insufficient cover (active stockout risk). 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 — availability driven by supply |
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 | Forecast Accuracy & Excess Order Risk
Forecast error (WMAPE) creates excess inventory when it manifests as overforecasting — orders sized for a demand that doesn't materialise. Categories with high WMAPE and a positive bias systematically over-buy. The table below shows category-level forecast error; for this engagement, the focus is the overforecast direction, which directly inflates working capital. The 'Stockout Days' column is shown as context only — those are not in the scope of this diagnostic.
| Category | Forecast Method | MAPE (current) | Target MAPE | Stockout Days (90d) | % Due to Forecast Error | Lost Sales Est. | Recommended Fix |
|---|---|---|---|---|---|---|---|
| Fresh Produce | Static weekly avg | 58% | 15% | 24 days | 71% | ₹3.6 L | Demand-sensing with weather/event signals |
| Frozen Foods | Static weekly avg | 42% | 20% | 15 days | 60% | ₹1.7 L | Rolling 2-week forecast with promo calendar |
| Dairy & Chilled | Supplier-led | 38% | 18% | 28 days | 55% | ₹4.6 L | Collaborative forecasting with Amul & Epigamia |
| Beverages | Static monthly | 31% | 18% | 12 days | 40% | ₹1.2 L | Seasonality-adjusted monthly forecast |
| Packaged Foods | Static monthly | 22% | 15% | 19 days | 35% | ₹1.3 L | SKU-level forecast for top 50 SKUs |
| Staples | Static monthly | 14% | 12% | 10 days | 20% | ₹0.4 L | Maintain current — minor improvement needed |
| Personal Care | Static monthly | 11% | 10% | 6 days | 15% | ₹0.3 L | Maintain current — within acceptable range |
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. The high-CV categories (Dairy, Fresh, Frozen) are shown for context — their under-protection is a stockout-side issue and out of scope for this engagement.
| 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 |
2E | Supplier Fill Rate & Delay Frequency
| Supplier | Category | Avg Lead Time | Promised LT | LT Variance | Fill Rate % | Delay Frequency | Stockout Impact |
|---|---|---|---|---|---|---|---|
| Local Produce Co. | Fresh Produce | 1 day | 1 day | — | 74.3% | 61% | HIGH |
| McCain Foods | Frozen | 8 days | 7 days | +1 day | 82.1% | 52% | HIGH |
| Britannia Ind. | Packaged Foods | 4 days | 3 days | +1 day | 88.3% | 41% | HIGH |
| Amul (Gujarat Co-op) | Dairy | 3 days | 2 days | +1 day | 91.2% | 34% | HIGH |
| Epigamia | Dairy/Health | 4 days | 3 days | +1 day | 85.6% | 38% | MED |
| ITC Agri | Staples | 6 days | 5 days | +1 day | 94.8% | 18% | MED |
| Dabur India | Health/FMCG | 5 days | 4 days | +1 day | 96.4% | 12% | LOW |
| HUL Distribution | Personal Care | 5 days | 4 days | +1 day | 97.1% | 9% | LOW |
What to change & what is the impact? — Prioritised Actions with Quantified Impact
📌 What type of impact are we measuring?
The ₹18 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. Stockout-driven lost-sales recovery is a separate workstream and is not in scope here.
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. Stockout-driven lost-sales recovery is a separate workstream and is not in scope here.
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.
8 actions
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
Action = short label. "What exactly to do" = specific steps the client team executes. WC Impact = cash flow released from excess inventory reduction.
| Action | What Exactly to Do | Owner | Timeline | KPI | Baseline | Target | WC Impact |
|---|---|---|---|---|---|---|---|
| Reduce reorder qty for slow-movers (>45 DOH) | 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 | 38 days | 22 days | ₹5.2 Cr |
| Right-size safety stock in low-CV categories | 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 | 21,300 | 5,000 | ₹3.2 Cr |
| Compress supplier lead times to reduce cycle stock | 1. Identify top 3 long-LT suppliers driving cycle stock buildup (ITC, Britannia, McCain). 2. Negotiate shorter lead time SLAs — shorter LT directly reduces the cycle stock buffer needed. 3. Where LT can't be reduced, negotiate smaller, more frequent drops. 4. Update planning parameters to reflect new LT assumptions. |
Procurement | Month 2 | Avg Lead Time | 6 days | 4 days | ₹3.0 Cr |
| Redistribute excess from Mumbai DC across network | 1. Build weekly stock visibility report across all DCs and spokes. 2. Identify SKUs over-concentrated at Mumbai DC where other nodes have demand cover gaps. 3. Execute weekly inter-node transfers to bring DC DOH down to target. 4. Stop incremental Mumbai DC ordering for SKUs being redistributed. |
Logistics Mgr | Month 1 | Mumbai DC DOH | 52 days | 28 days | ₹1.9 Cr |
| Liquidate dead stock (>90 DOH SKUs) | 1. Extract all SKUs with DOH >90 days (est. ₹2.6 Cr total). 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 | ₹2.6 Cr | ₹1.0 Cr | ₹1.6 Cr |
| Dampen overforecasting in mid-volatility categories | 1. Audit forecast vs actual for last 12 weeks; identify categories with persistent overforecast bias. 2. Apply bias correction to planning system for Beverages and Packaged Foods. 3. Move from static monthly to rolling 4-week forecast for top 50 SKUs. 4. Track WMAPE and bias direction weekly; reduce buffer ordering tied to inflated forecasts. |
Planning Team | Month 3 | Forecast Bias | +14% | ±3% | ₹1.3 Cr |
| Monthly S&OP with top 5 suppliers | 1. Schedule monthly joint review with Amul, Britannia, ITC, HUL, McCain. 2. Share 8-week forward demand plan at each meeting. 3. Review fill rate and delay data together — supplier accountable to data. 4. Agree on lead time commitments and MOQ flexibility for next month. |
SC Head | Month 2 | Fill Rate | 88% | 95% | ₹1.2 Cr |
| SKU rationalisation — tail 20% | 1. Rank all 3,580 SKUs by revenue contribution. 2. Identify bottom 20% (<0.1% revenue each) — flag for review. 3. Validate with category managers; mark seasonal exceptions to protect. 4. Phase out ~680 SKUs over 60 days; redirect shelf space to top performers. |
Category Mgr | Month 3 | Active SKU Count | 3,580 | 2,900 | ₹0.9 Cr |
| TOTAL (90 days) | ₹18.3 Cr cash |