Service Level Calculations

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

The Erlang C formula has historically been the backbone of contact center staffing calculations. But many WFM professionals use it without fully understanding its assumptions — or its limitations. Here’s a quick breakdown: What Erlang C does: It calculates the number of agents needed to meet a target service level, based on call volume, average handle time, and desired answer time. What it assumes: Calls arrive randomly (Poisson distribution), callers wait indefinitely (no abandonment), and all agents handle calls at the same rate. Where it falls short: It doesn’t account for call abandonment, multiple contact channels, or skill-based routing. It also doesn’t factor in shrinkage, which means raw Erlang C output always underestimates actual staffing needs. The takeaway: Erlang C is a starting point, not a complete staffing strategy. To build a workforce plan that actually works, you need to layer in real-world variables, align your forecasting with organizational goals, and build processes that your team can sustain. This is exactly what a structured WFM accelerator program is designed to do — identify your goals, align WFM principles to them, implement the right strategy, and mentor your team for long-term success. → Learn more about Accelerator Program: https://lnkd.in/ewicvA6w #ErlangC #WorkforceManagement #WFM #ContactCenter #Forecasting #CapacityPlanning #Staffing #CX #WFMStrategy