CPG Управління запасами 101 – Скільки запасів потрібно мати та переваги та недоліки надмірного запасу

Recommendation: Establish safety stock targets by SKU class. Fast moving dairy lines receive 14 days of supply; slower items receive 7 days. Regional adjustments apply to Brazil markets; use rolling forecast to adapt quickly, minimise exposure to demand shocks, maximise service reliability. today.

Operational model: Cross-functional teams align on replenishment signals; marketing insights, shopper data, manufacturing capacity feed a single workflow; communication improves forecast accuracy, investment in data integration, automation reduces lead times; expected benefit includes lower stockouts, improved cash flow, faster response. This framework should cut stockouts, improve cash flow, speed response. some gains appear quickly across markets. Likely returns include improved service levels. Today these steps drive regional resilience for dairy categories in brazil.

Shopper experience: Use marketing feedback, trade promotions and regional promotions to tailor stock targets for your shoppers. Skippy benchmarks show fast-moving items benefit from higher service levels; dairy SKUs in price-sensitive regions require tighter controls. By streamlining processes, teams create Lean cost structure whilst preserving availability. Today adoption boosts shopper satisfaction, supports regional growth, strengthens company position in Brazil.

Risk management: Monitor spoilage risk for dairy lines; excess stock triggers markdowns; keep costs down through dynamic replenishment triggers. Track service levels, days of supply, turn rate; if a SKU misses target, trigger review within 24 hours by regional teams in Brazil. This approach drive efficiency, reduces downtime, improves margins.

Етапи реалізації: Taking concrete actions today; apply a two-tier model with strategic targets by category; run weekly reviews by regional teams; leverage manufacturing data to align supply. Outcome: improved capital efficiency; better service; best position against rivals in Brazil. Start with a pilot; scale towards leading investment, leadership plus cross-functional collaboration.

Inventory Management Essentials for CPG: Balancing Stock Levels, Overstocking Impacts, and Practical Questions

Recommendation: Adopt a weekly demand-to-supply review to calibrate safety stock and align manufacturing calendars with forecasted demand, ensuring timely replenishment.

To analyse rising trends, leverage inputs from the field, consumers, and analysts; analysing data indicate gaps across channels, and those insights lead to planogram updates and growth.

Align incentives across teams by instituting a planogram-driven approach; leveraging automating dashboards provide timely visibility across field and e-commerce, особливо when manufacturing changes occur.

Account leadership should review weekly to meet peak demand, reducing deductions and keeping their Retailers aligned with targets; this drives satisfaction amongst consumers.

Regularly review growth metrics with analysts; the field and marketing teams should align on planogram updates., when changes occur, to meet expectations and maintain seamless execution across channels.

To institutionalise best practices, adopt a standardised planogram, institute automated scorecards, and analyse results for your organisation; this approach reduces risk and supports growth, across channels with seamless execution.

Set your starting stock using EOQ and reorder point calculations you can apply today

Apply precise maths to set initial stock across items in your store, minimising capital tied to goods while maximising service to consumers. A simple spreadsheet can translate demand patterns into concrete orders and trigger points for replenishment.

1. Get inputs for each item:
   • item name
   • annual demand D (units/year)
   • fixed order cost S (£ per order)
   • holding cost per unit H (£ per unit per year)
   • lead time L (days)

   Compute daily demand d = D/365 and prepare a quick sheet to run the numbers, covering grocery staples, foods and other essentials you carry in store.

2. EOQ calculations:
   • Rice bags (staple item): D = 2,000/year; S = 50; H = 1; EOQ ≈ sqrt(2×2000×50/1) ≈ 447 units
   • Tinned beans: D = 6,000/year; S = 25; H = 0.60; EOQ ≈ sqrt(2×6000×25/0.60) ≈ 707 units
   • Pasta sauce jars: D = 4,000/year; S = 40; H = 0.90; EOQ ≈ sqrt(2×4000×40/0.90) ≈ 596 units

   These values become your monthly or quarterly order sizes, aiding capital planning and supplier negotiation.

3. ROP and safety stock:
   • Rice bags: d ≈ 2,000/365 ≈ 5.48/day; L = 10 days; ROP ≈ 55 units
   • Beans: d ≈ 6,000/365 ≈ 16.44/day; L = 7 days; ROP ≈ 115 units
   • Pasta: d ≈ 4,000/365 ≈ 10.96/day; L = 5 days; ROP ≈ 55 units
   • Safety stock: if variability is moderate, SS ≈ 0.10–0.20 × EOQ; for higher risk, move towards 0.25 × EOQ, vice versa adjustments when lead times or prices shift
4. Translate to monthly plans and KPIs:
   • Translate EOQ and ROP into monthly orders to align with store cycles for lean rotation.
   • Track indices like fill rate, stock turnover and capital tied to stock, with a focus on impacting service levels
   • Coordinate with packaging teams to support foods and ready-to-eat items; ensure packaging looks appealing and helps preserve quality
5. Technology, robotics and implementation:
   • Implement online dashboards connected to EPOS and supplier portals to create real-time signals
   • Leverage robotics and other technologies in warehousing to maximise speed and accuracy
   • Plans should create a strong foundation for growth and extend towards global suppliers and online channels; this approach became standard for many companies expanding beyond a single shop.
   • Work with farmers and other companies to shorten lead times and improve stock accuracy across categories.

By starting with data-driven numbers you can quickly create a stock policy toward long-term performance, enabling retailers to maximise capital efficiency and better serve consumers. The aim is to maintain reliable availability during peak month demand while maintaining strong relationships with suppliers and packaging teams, creating a resilient supply chain that goes beyond the confines of a single retailer or market.

Define safety stock and target service level to reduce stockouts in peak seasons.

Set safety stock to cover demand variability during lead time and target a service level that reduces stockouts in peak seasons; this ensures supply continuity and is a strong way to help users avoid urgent changes, especially for high-demand materials and complex SKU families, delivering significant improvement in on-shelf availability.

Know historical demand patterns and variability; analyse data in a global context, adding seasonality and growth trends to achieve greater forecast accuracy and stronger protection. Use a simple model: safety stock = z × σLT, where σLT is the standard deviation of demand during lead time and z corresponds to the chosen service level (e.g., 1.65 for 95%). The final aim is to maintain a high on-shelf availability whilst avoiding excessive carrying costs. This approach helps determine whether a given item needs stronger protection during changes in supply and supports teams in managing revenue impact before peak periods.

Adopt better technologies and collaborate with teams across global supply networks. Campbell analytics platforms can boost visibility, enabling better decision-making and growth planning, while teams across global channels coordinate more effectively. This alignment helps ensure service level targets are embedded in replenishment rules, and it supports continuously improving stock performance across regions, delivering significant competitive advantage.

Apply the model item-by-item, re-evaluate weekly, and adjust thresholds to align with market changes and revenue goals across the global network.

Track the true costs of overstocking: cash drag, markdowns, and obsolescence

Action: cap core stock at six weeks of forecast demand; enforce a strict weekly review in the field to prune ageing lines and prevent new commitments that exceed shifting demand.

Cash drag is the hidden expense that gnaws at your return on investment across the entire value chain. When capital sits in slow-moving merchandise on a field rack or in a stockroom, the opportunity to deploy it elsewhere vanishes. With a realistic cost of capital around 8% annually, keeping £10 million of excess on hand for 90 days can erase roughly £200,000 of potential earnings. Global supply shifts, pandemic-era behaviour, and changing consumer tastes being really driven by external shocks amplify this effect for retailers and farmers alike.

Markdowns and obsolescence erode recovered value. For items no longer meeting demand, markdown depth can range from 20% to 50% of original cost; for slow movers it can reach 60% or more when end-of-season pressure mounts, leaving a substantial portion of investment unrecovered. In categories tied to machine-driven cycles and assembly lines, obsolescence grows when new models or lines shift the field. The risk rises where product life cycles compress, particularly in fashion, electronics, and seasonal lines.

Where to monitor? Indices published by industry bodies, alongside internal dashboards, illuminate ageing risk. Track the levels of ageing stock weekly, watch for rising age, and act before the curve accelerates. This measurement informs a shift in supply, pricing, and assortment strategy to align with real demand signals. They provide clear signals about where to intervene.

Your action plan must align across retailers and farmers; the field teams, they collaborate with assembly and merchandising to trim risk. This essential collaboration must propagate throughout the entire operation, ensuring used items move quickly through liquidation channels when necessary. A disciplined strategy ties procurement to forecast accuracy, enabling the field to act swiftly and reduce lack of liquidity across the network.

Benefit realised: stronger cash flow, lower exposure to misalignment, and improved experience for end customers. The weekly cadence, plus data from published indices where demand signals are known, supports a proactive change that could boost overall margins and align investor expectations.

Forecast demand with category-specific inputs to keep inventory lean and responsive

Forecast demand by category using inputs from POS data; promotions; seasonality; supplier timelines; set category-specific replenishment triggers. This informs investment planning. This should help a company balance available store stock. Timely reorders reduce shortages; store satisfaction rises.

Inputs include weekly EPOS by category; shoppers' feedback; planned campaigns; price elasticities; supplier lead times; logistics estimates; paper plans converted to digital forecasts. Such sources improve forecast accuracy; retailers gain much visibility for promotions; brand teams align with manufacturers.

Fresh categories require rapid turns; spoilage risk hinges on shelf-life; physical stores see arrivals; planning horizons shrink; non-perishables show steadier demand; forecasts adjust weekly. Maybe adjust weekly forecasts after review.

Benefits include greater service levels; higher satisfaction; stronger brand loyalty; retailers gain confidence.

Cross-functional planning teams coordinate with manufacturers; logistics align; benchmarks like Campbell Guide policies; Automating alerts provide timely signals; same-system dashboards reduce latency.

Weekly reviews compare forecasts to actuals; misses lead to capacity gaps; concerns persist about supplier reliability; this leads to risk of stockouts; service levels rise; prices stay competitive; optimal stock balances trade-offs.

Implementation hinges on investment in analytics; teams train; feedback loops continuously feed model updates; physical checks supplement digital signals; paper notes phase out.

Five pivotal questions to align retailers and CPGs on stock decisions

Implement weekly cross-functional reviews to align buying plans with real-time fulfilment, ensuring seamless availability while reducing paper trails; warehousing remains lean, actions must be measurable.

1) What drives forecasts to reflect shoppers’ plans, manufacturing lead times, warehousing constraints, seasonal times, promotions?

2) Where should brands invest to improve replenishment speed while maintaining service levels, balancing them with cost?

3) Which metrics reliably indicate stock health given weekly forecasts, availability, buying plans, shoppers’ behaviour?

4) What process structure yields seamless data flow from paper, digital sources, warehousing systems into a single weekly cycle?

5) What leads to improved availability while controlling time-to-fulfilment during times of manufacturing disruption?