Safety Stock Policy Development

A few years ago, I interviewed a seasoned supply planner from a global FMCG giant. I asked him, "How do you ensure uninterrupted service when forecasts are often wrong?" He smiled and replied, "I don’t trust forecasts blindly. I trust buffers." That stuck with me. We often talk about safety stock like it’s just another calculation - based on service levels, variability, and lead time. But what we often miss is that safety stock is not a backup plan - it’s a confidence plan. When I worked with a food company in North India, we faced wild swings in demand during festive seasons. Despite best efforts, our forecast error remained in the 25–30% range. Initially, we adjusted demand. Then we tried pushing supply. Nothing worked consistently. Until we recalibrated safety stock - not as a static percentage, but as a dynamic lever. We used historical MAPE to segment SKUs: ↳ High forecast error items had higher safety stock, but only if they were fast-movers ↳ For low runners, we capped safety stock and focused on lead time reduction This single change lifted our service levels from 87% to 95% - without inflating inventory across the board. Here’s what I learned: Safety stock isn’t about covering up forecasting failures. It’s about strategically absorbing volatility where it matters most. It’s not "extra" inventory—it’s "essential" inventory. We often praise forecast accuracy, but sometimes, it’s the silent buffers - well-planned, SKU-specific safety stocks - that save the day. Would love to hear - how do you approach safety stock? Static formula or dynamic levers?

How do you pick the right service level? Most teams just guess. Some go with 95% because it “feels right.” Others copy industry benchmarks. But what if we could compute the economically optimal service level based on actual trade-offs? Here’s a simple way to think about it: ✅ Higher service level = fewer stockouts, but more inventory and write-off risk ⚠️ Lower service level = lower holding cost, but higher chance of lost sales, upset customers, and waste The trick is to model both: 👉 H: your per-unit carrying cost (including perishability, expiration risk, or discounting loss) 👉P: your per-unit stockout penalty (including lost margin and customer trust) Then, use the ratio H/P to compute an optimal safety stock policy. And importantly: do it using quantiles, not averages. The damage is always in the tail. For perishables like avocados, H grows non-linearly as stock gets close to expiration. So you need a smarter model that reflects that carrying cost increases as shelf life shrinks. If you can estimate lead time, forecast error, and tune the H and P inputs, you can create: 👉 Better safety stock by SKU 👉 Tiered service levels by segment 👉 Smarter trade-offs in constrained networks Goal: aim for more profitable service levels (over simply higher service levels). #SupplyChainOptimization #ServiceLevel #InventoryManagement #ProbabilisticModeling #DecisionIntelligence #OperationsResearch #BitBros #SupplyChain

Because lack of inventory threatens a company's life ... This infographic contains 7 safety stock formulas for challenging inventory times: ✅ # 1 Time-Based 🧮 Formula: Maximum Daily Use X Maximum Lead Times in Days – Average Daily Use X Average Lead Time in Days ❓ When to Use: when demand is likely to change over time ✅ # 2 With Lead Time Variability 🧮 Formula: Z x average demand X lead time standard deviation (σLT) 🗒️ Note: Z is the service level considering a standard normal distribution ❓ When to Use: high volume items, lead time variability, stable demand ✅ # 3 With Demand Variability 🧮 Formula: Z × demand standard deviation × sqrt (average delay) ❓ When to Use: high volume items, demand variability, stable lead time ✅ # 4 With Demand and Lead Time Variability - Independent 🧮 Formula: Z * sqrt((Average LT*(Demand Standard Deviation) squared + (Average Sales * Lead Time Standard Deviation) squared) ❓ When to Use: high volume items, demand and lead time variability; lead time does NOT impact the demand ✅ # 5 With Demand and Lead Time Variability - Dependent 🧮 Formula: Z * Demand Standard Deviation * Sqrt (Average LT) + Z * Average Sales * Lead Time Standard Deviation ❓ When to Use: high volume items, demand and lead time variability; lead time does impact the demand and vice versa ✅ # 6 With Demand and Lead Time Variability – Low Volume, Smooth 🧮 Formula: by applying Poisson distribution ❓ When to Use: low volume items, low fluctuations in demand ✅ # 7 With Demand and Lead Time Variability – Low Volume, Erratic 🧮 Formula: by applying Gamma or negative binomial distributions ❓ When to Use: low volume items, erratic and “bulky” demand Any others to add? #supplychain #salesandoperationsplanning #integratedbusinessplanning #procurement

TOC Jedi Insights: On Safety Stock… “The safest system isn’t the one with the most stock, but the one with the best flow.” Safety stock is meant to be a buffer, a last line of protection against uncertainty. But too often, it becomes the plan itself. What do we see? • Inventory targets built on static safety stock rules, rather than real demand signals. • Warehouses filled “just in case,” while the items customers actually want run out. • Teams measuring success by how much they hold vs. the plan, not by how well the flow meets demand. When safety stock becomes the strategy, two things happen: • Excess piles up where it isn’t needed. • Shortages still happen where it matters most. The system looks protected, but in reality, it’s brittle, slow to react, costly to run, and blind to change. True protection doesn’t come from bigger cushions, but from smarter flow. That means: • Placing buffers at the right points in the system. • Managing them dynamically, shrinking when stability rises, expanding where it matters and when variability hits. • Using visibility and replenishment rules to keep safety stock as a shield, not a substitute for planning. 💡 The TOC Jedi knows: safety stock should protect the flow, not replace it. When buffers are managed with flow as their guide, the system becomes both lean and resilient. Flow is the force. May the flow be with you.

Safety Stock Matrix   An easy way to bucketize Safety stock based on ABC and XYZ matrix.   Start with an ABC or pareto analysis for the SKUs, below are steps for ABC analysis. 1.   Sort cumulative sales of last 12 months in descending order of sales value. 2.   Allocate top 80% SKU as A, Next 15% as B and remaining 5% as C class. Second step is to calculate COV and categorize SKUs in XYZ categorization. 1.   Take measure COV for same set of sales data for a period of 24 months. 2.   Categorize X=30%, Y=31%-75%, Z=>75%, This range can be customized as per industry. COV is Coefficient of variance is a statistical measure of dispersion of data points in a data series around the mean. The main idea of the ABC XYZ analysis is to combine ABC and XYZ categories across two dimensions: we end up with a matrix of 9 categories. Then, we can classify items around 4 extremes: AX: High sales volumes, stable AZ: High sales volumes, very volatile CX: Low sales volumes, stable CZ: Low sales volumes, very volatile To use effectively the ABC XYZ matrix, we need to define an Inventory Management Policy: setting service level and safety stock targets. Roughly speaking, if you want a better service level, you need higher safety stock. For example, we can choose to hold more inventory for the A category, as A items are the major drivers of your business, and we want to maximize the service level for those products. For AX items, we can afford to hold less safety stock than AZ items, as we have better visibility over the demand. The same logic applies to B items. Regarding C codes, you can decide to hold low inventory for both CX and CZ categories, for two reasons: CX items have a low impact on the business, so we can afford to set a lower service level. Also, they are stable, so we require even less safety stock. CZ items are very volatile. We can think we need therefore a bit more safety stock, but we know by experience that most of the time It is not worth it: because they are both low-selling items and very unpredictable, CZ items are often a source of high stock levels and unnecessary headaches. I noticed that CZ items represent a big part of the total sleeping inventory of most companies. It might be wise to set lower service levels for this category.   This is just an example. There is no golden rule, it all depends on your own supply chain challenges. #inventorymanagement#supply chain