Optimize Inventory with Safety Stock Formula
U.S. supply chains are now facing more unpredictable demand and lead times. This article offers a detailed guide on the safety stock formula for continuous review systems. It bridges statistical models with ordering policies, helping leaders reduce stockout risks while managing inventory costs.
It explains how safety stock strategy is linked to reorder points and EOQ. The discussion highlights the trade-offs between service levels and carrying costs. It references APICS guidance by Peter L. King and methods by Edouard Thieuleux, along with software practices by Acctivate Inventory Management and Brian Sweat. Each source is grounded in proven operations and measurable results.
The article covers basic rules, such as safety days times average demand and the Average–Max method. It also explores Z-score equations for demand and lead time risks. It clarifies the difference between cycle service level and fill rate, scales variability to lead time windows, and sets policy by ABC/XYZ class. For low-volume or skewed demand, it compares Poisson, Gamma, and Binomial options and discusses operations levers like expediting and make-to-order.
The aim is to empower supply chain and finance teams to apply safety stock optimization. This will lead to better working capital and fewer backorders. Expect clear steps, safety stock management tips, and analysis to support executive decisions.
What Is Safety Stock and Why It Matters for Supply Chain Resilience
Safety stock inventory acts as a shield, protecting against unexpected demand spikes or lead time changes. APICS defines it as a critical buffer that absorbs forecast errors and supply variability. It ensures revenue stability and preserves cash, making it a cornerstone of a disciplined safety stock strategy.
Definition of safety stock (buffer stock) and its role
Safety stock serves as a buffer, bridging the gap between expected consumption and replenishment risks. It mitigates demand and lead time variability during the reorder cycle, as emphasized by APICS and Stéphane Thieuleux. This buffer is essential for maintaining supply chain resilience.
At a targeted cycle service level, most cycles rely on cycle stock. Some may draw from the buffer, while a small portion faces the risk of stockouts. The safety stock formula helps determine the exact amount needed to stabilize fill rates and revenue.
Balancing customer service rate and inventory cost
Increasing the service target raises the Z-score, leading to higher safety stock levels at a non-linear rate. This improvement in availability comes at the cost of increased carrying costs and tied working capital, as noted by Acctivate. It’s a delicate balance.
Effective safety stock management involves setting differentiated targets based on item criticality and profit margins. A well-thought-out strategy weighs the importance of margin, variability, and lead time against capital constraints.
Common pitfalls: using safety stock to mask data and process issues
Excessive buffers often hide underlying issues such as weak master data, poor forecast quality, or unstable supplier performance. Thieuleux points out that inflating the buffer does not address these root causes and can harm cash flow.
Robust practices combine the safety stock formula with data governance, reliable planning systems, and supplier collaboration. This approach ensures a safety stock level that supports service without unnecessary excess.
| Decision Focus | What It Controls | Metric Impacted | Operational Action |
|---|---|---|---|
| Cycle Service Target | Probability of no stockout per cycle | Fill rate, line-item availability | Set Z-factor and compute via safety stock formula |
| Demand Variability | Forecast error and seasonality | Safety stock level in units | Use rolling standard deviation and item stratification |
| Lead Time Variability | Supplier, transport, and planning delays | Reorder point and buffer width | Measure actual receipts and include variance in calculation |
| Working Capital | Cash tied in inventory | Carrying cost and cash conversion | Cap buffers for low-velocity SKUs; review monthly |
| Data Quality | Accuracy of demand and lead time inputs | Service reliability and holding cost | Clean master data; audit forecasts; align with suppliers |
Key Drivers: Demand Variability and Lead Time Uncertainty
Inventory buffers are driven by two main factors: volatile demand and uncertain lead times. A thorough safety stock analysis is essential. It balances both to determine an accurate safety stock amount. This amount must align with the safety stock formula and the measured safety stock lead time.
Demand uncertainty examples and forecast quality differences
Not all items have the same sales patterns. For instance, household staples like toilet paper have consistent weekly sales with minimal forecast error. On the other hand, weather-sensitive goods like umbrellas experience significant demand spikes during storms.
This variability impacts the safety stock calculation. As forecast error increases, the safety stock formula adjusts. It incorporates the demand standard deviation and a chosen service factor. This method ensures service levels remain reliable during demand fluctuations.
Lead time components and sources of variability
Lead time encompasses multiple stages, not just one. It includes review, purchase order confirmation, production, and more. Each stage can affect the overall lead time.
Global shipping adds complexity. For example, China-to-Europe routes average 40 days but face numerous risks. These risks include raw material shortages, factory capacity issues, and IT outages. The variability in these lead times is critical for safety stock analysis.
How variability influences safety stock level and stockout risk
Uncertainty in demand or lead time increases stockout risk. Statistical buffers adjust based on the driver’s standard deviation and Z-score. They are time-adjusted to match the safety stock lead time window.
When demand is unpredictable but lead times are stable, demand-based buffers work best. But if lead time variability is high, lead-time-based buffers are more effective. Higher variability means larger stockouts, which can lower fill rates. This highlights the need for precise safety stock calculations based on error and horizon length.
Linking Safety Stock to Reorder Point and EOQ
In a continuous review policy, the reorder point marks when to order more stock. It’s calculated as on-hand plus on-order inventory minus a target level. Didier Thieuleux states that ROP = Safety Stock + Average Demand × Lead Time. This equation shows safety stock as a buffer against lead time fluctuations.
EOQ, or Economic Order Quantity, determines the optimal order size to balance costs under stable demand. It dictates how much to buy. The reorder point, on the other hand, determines when to buy. Together, they optimize safety stock by balancing cost with service reliability under real-world uncertainty.
Thieuleux points out that using a fixed ROP without a buffer underestimates risk. The safety stock formula compensates for demand and supply noise. It allows the reorder point to handle demand spikes without stockouts. APICS emphasizes the importance of consistent units: daily demand must match lead time in days for accurate safety stock calculations.
Practitioners often formalize this relationship in policy. They set fixed EOQ quantities to release when inventory reaches the ROP with a buffer. This maintains a steady procurement rhythm while the safety stock absorbs variability. The outcome is a harmonized set of rules that aligns service goals with financial management.
- Core linkage: ROP = buffer plus expected demand over lead time, with the safety stock formula setting the buffer magnitude.
- EOQ role: EOQ fixes lot size; the reorder point triggers release, uniting cost control with reliability.
- Data discipline: Align time units before any safety stock calculation; inconsistent units distort both cycle stock and buffer.
When executed with precise parameters and consistent calendars, the reorder point and EOQ form a unified system. The safety stock calculation ensures service levels during uncertainty, while EOQ controls costs. This approach aligns with APICS standards and Thieuleux’s framework.
Foundational Methods: From Safety Days to Average–Max
Foundational heuristics are essential for teams starting safety stock management when data is scarce. They convert historical operations into a usable safety stock formula. This formula aids in making daily decisions for managing safety stock in complex portfolios.
Basic approach: safety days × average demand
A straightforward method calculates Safety Stock as Safety Days × Average Demand. For instance, 5 days × 100 units/day equals 500 units. The reorder point is then ROP = Safety Stock + Average Demand × Lead Time, as explained by Olivier Thieuleux.
This method is quick and straightforward but lacks a statistical basis. To improve, teams often use ABC classification. This ensures high-value items have fewer safety days than low-value ones. This approach enhances safety stock management without increasing inventory unnecessarily.
Average–Max equation and calculation logic
The Average–Max safety stock formula is another popular choice: Safety Stock = (Maximum Lead Time × Maximum Usage) – (Average Lead Time × Average Usage). Acctivate illustrates it in daily terms, and Thieuleux shows how to convert monthly to daily; consistency in units is critical.
In Thieuleux’s example, average sales are 33/day, with a maximum of 39.5/day. Average lead time is 35 days, with a maximum of 40 days. The calculation results in about 427 units of safety stock, with a reorder point near 1,578 units. Its simplicity and logic make it a favorite among planners at the start of safety stock management.
Handling outliers and capping extremes to avoid overstock
These methods can overreact to rare events. A single long delay or a demand spike can significantly increase safety stock inventory, as Thieuleux and Acctivate point out. To mitigate this, practitioners often cap maximum lead time or maximum usage by a percentage.
Capping helps control inflation but introduces arbitrariness. It doesn’t reflect a service target. A structured review, such as trimming extreme values, validating data quality, and aligning caps with ABC classes, keeps the safety stock equation practical. It also helps manage safety stock management costs effectively.
Statistical Safety Stock: Service Levels, Z-scores, and Normal Distribution
In a normal distribution, the safety stock formula transforms a desired service level into a Z-score. This Z-score scales the variability over the lead time. It supports safety stock optimization by linking risk tolerance to measurable dispersion in demand or lead time.
Cycle service level vs. fill rate and why they differ
Cycle service level measures the share of replenishment cycles without stockouts. Fill rate, on the other hand, measures the share of demand volume fulfilled. When variability is low, fill rate often exceeds the service level because shortfalls are small. At higher volatility, stockout sizes grow, so fill rate can trail the same service level despite the same Z-score.
In practice, safety stock analysis should specify which metric is being targeted. Retailers like Walmart often track fill rate for OTIF performance. Manufacturers may aim for a cycle service level to stabilize production schedules.
Typical Z-score targets (e.g., 90%, 95%, 98%) and nonlinearity
Typical mappings used in planning include 90% → Z ≈ 1.28, 95% → Z ≈ 1.65, 98% → Z ≈ 2.05, 99% → Z ≈ 2.33, and 99.9% → Z ≈ 3.09. The relationship is nonlinear: each incremental rise in service level demands a disproportionately larger Z-score, which expands safety stock.
This nonlinearity matters for safety stock optimization across product tiers. High-margin items may warrant higher service level targets. Long-tail items may hold a lower Z-score to reduce carrying cost.
Time scaling: why standard deviation must match lead time window
For demand-driven variability, the safety stock formula uses the standard deviation of demand over the full lead time. If σ is weekly and lead time is three weeks, scale by the square root of time: σLT = σweekly × √3. The result ensures that Z-score coverage reflects the entire performance cycle.
When lead time variability dominates, an alternative model applies: Safety Stock = Z × σLead time × Average Demand. Selecting the correct construct is part of rigorous safety stock analysis. It keeps calculations consistent with the real driver of uncertainty.
| Target | Metric | Z-score | Variability Focus | Safety Stock Formula | Use Case |
|---|---|---|---|---|---|
| 90% | Cycle service level | ≈ 1.28 | Demand | Z × σDemand over lead time | Stable suppliers, moderate demand swings |
| 95% | Cycle service level | ≈ 1.65 | Demand + lead time | Z × √(σDemandLT² + (Avg Demand² × σLead time²)) | Omnichannel items with mixed risk sources |
| 98% | Fill rate | ≈ 2.05 | Demand | Z × σDemand over lead time | High-margin SKUs requiring fewer lost sales |
| 99% | Cycle service level | ≈ 2.33 | Lead time | Z × σLead time × Average Demand | Long import lanes with variable transit |
| 99.9% | Fill rate | ≈ 3.09 | Demand | Z × σDemand over lead time | Critical spares with near-zero tolerance for backorders |
Embedding these choices into planning systems supports consistent safety stock optimization. Clear definitions of service level, an appropriate Z-score, and correct time scaling keep the safety stock formula aligned with actual risk.
Applying the safety stock formula with demand uncertainty
When demand is unpredictable but lead time remains consistent, a specific safety stock approach is viable. This method focuses on the variability of orders, disregarding transit or processing delays. It ensures a precise safety stock level and a justifiable reorder point, aligning with a stable performance cycle.
When demand dominates and lead time is stable
APICS and Pierre Thieuleux suggest a demand-only model when lead time variance is minimal. This model buffers against demand fluctuations while using average lead time for the reorder point. It results in a safety stock level that effectively limits stockouts, adhering to the chosen service level.
Inputs needed: demand standard deviation, lead time, service factor Z
- Demand standard deviation measured in the same time unit as the lead time window.
- Total lead time, including the review period or performance cycle.
- Service factor Z tied to the policy target for cycle service level.
With these inputs, the safety stock calculation scales demand volatility across the lead time. It converts policy into numbers using the Z-score. This maintains consistency in the safety stock formula across items, supporting a unified safety stock strategy.
Worked example structure and resulting impact on reorder point
- Compute demand σ in the base time unit.
- Scale σ to lead time using σLT = σ × √(Lead Time in base units).
- Select Z per policy (e.g., 90% to 98%).
- Calculate Safety Stock = Z × σLT.
- Compute Reorder Point = Safety Stock + (Average Demand × Lead Time).
| Case | Base Unit | Demand σ | Lead Time | Z (Service) | σ Scaled to LT | Safety Stock | Average Demand × LT | Reorder Point |
|---|---|---|---|---|---|---|---|---|
| Thieuleux monthly example | Month | 141.4 units | 1.15 months | 1.28 (90%) | 141.4 × √1.15 ≈ 151.7 | 1.28 × 151.7 ≈ 194 units | Average demand × 1.15 | 194 + Average demand × 1.15 |
| APICS warehouse case | Week | 10 rolls | 8 days (T1 = 7-day base) | 1.65 (95%) | 10 × √(8/7) ≈ 10.7 | 1.65 × 10.7 ≈ 18 rolls | Average demand × (8/7) weeks | 18 + Average demand × (8/7) |
Both examples illustrate how the safety stock formula elevates the reorder point by a calculated buffer. By ensuring time units match and applying the formula strictly, teams establish a safety stock level that reflects demand risk. This approach maintains a disciplined safety stock strategy.
Lead Time Risk and Combined Uncertainty Models
When lead times vary, the safety stock equation must adapt. A lead-time-only approach uses Z times the standard deviation of lead time and average demand. This creates a buffer if lead-time variability is low, as APICS and Pierre Thieuleux suggest. It’s suitable for stable suppliers with high process capability.
For dual uncertainty, APICS advises combining independent variances. The safety stock equation becomes Z times the square root of demand variance over lead time plus a term from lead-time variance scaled by average demand. This method handles safety stock lead time risk and demand swings together, avoiding double counting. It’s a step toward optimizing safety stock in multi-sku portfolios.
Thieuleux found that the combined, independent model can create a higher buffer than single-source models when both risks are significant. In his example, the result was 267 units, surpassing buffers based on demand or lead time alone due to dual coverage. This approach aligns with disciplined safety stock management, where independence allows for statistical aggregation.
If demand and lead time move together, such as during peak demand, Thieuleux suggests an additive structure. This equals the demand-only term plus the lead-time-only term. It results in the largest buffers and should be used only when correlation is proven with data. The right model choice depends on sample size, seasonality controls, and observed joint behavior of demand and safety stock lead time.
The operational choice affects reorder points, working capital, and service performance. Teams can test variance-combination versus additive models on historical data. This supports safety stock optimization by linking model fit to measurable results. It ensures planning parameters align with supplier reliability and market volatility.
Independent vs. Dependent Variability: Which Equation to Use
Choosing the right safety stock equation hinges on whether demand and lead time are independent or correlated. A solid safety stock strategy begins with data-driven insights. It then aligns with the dominant risk pattern. This approach optimizes safety stock across replenishment cycles.
Independent case: combining variances under normality
APICS suggests combining variances under normality and independence. The safety stock equation involves the service factor Z, demand variance over lead time, and lead-time variance scaled by average demand. This method captures the rarity of simultaneous extremes, leading to a leaner strategy.
This structure is suitable when lead times are stable and forecast errors don’t affect lead time. It’s common for stable SKU families with reliable carriers, like items moving via UPS or FedEx on fixed transit schedules. It enables safety stock optimization with lower buffers at the same service target.
Dependent case: additive approach when demand and lead time correlate
When demand spikes slow suppliers or stretch logistics capacity, dependence is evident. APICS and Thieuleux recommend an additive form: Z times demand variability over lead time plus Z times lead-time variability scaled by average demand. This equation increases the buffer due to the co-occurrence of demand and lead-time shocks.
Apply this approach where seasonality, shared bottlenecks, or constrained capacity tie volume to response time. It adjusts the safety stock strategy to cover compounding risk and supports optimization during intense peaks.
When dependence happens and why it raises safety stock
Dependence often emerges during holiday peaks, product launches, or global upswings that crowd ports and carriers like Maersk or DHL. It can also arise when contract manufacturers face queueing under large retail promotions from Walmart or Target. In these periods, higher demand lengthens lead time, linking the two drivers.
Because correlation thickens the tail of the distribution, buffers must expand to protect service. Selecting the dependent model only when data confirms linkage keeps inventory disciplined while aligning the safety stock strategy with real-world exposure.
| Model Assumption | Safety Stock Equation (Structure) | Risk Mechanism | Typical Use Case | Impact on Inventory |
|---|---|---|---|---|
| Independent (Normality, uncorrelated) | Z × sqrt[(σ demand over lead time)^2 + (σ lead time × average demand)^2] | Variances combine; extremes rarely align | Stable carriers and suppliers; routine SKUs with steady forecasts | Lower buffer for a given service level; supports safety stock optimization |
| Dependent (Positive correlation) | (Z × σ demand over lead time) + (Z × σ lead time × average demand) | Shocks co-move; tail risk inflates | Peak seasons, promotions, port congestion, capacity constraints | Higher buffer to sustain service; conservative safety stock strategy |
Advanced Practices: Classification, Low-Volume Items, and Optimization
Effective safety stock management requires adapting to product criticality, volatility, and financial impact. It’s essential to set service targets and methods based on demand behavior and strategic role, not a single blanket rule.
APICS suggests setting group Z-scores by strategic importance, profit margin, or dollar volume. This aligns safety stock optimization with enterprise goals, improving the reliability of safety stock calculation across diverse portfolios.
ABC/XYZ service targets and method selection guidance
Thieuleux recommends ABC/XYZ classification to align service levels and method choice. High-value, stable items (A/X) warrant higher targets and statistical methods that rely on a robust safety stock formula. Low-volume or erratic items (C/Z) favor Average–Max with caps to avoid excess.
When demand is intermittent, safety stock analysis should prioritize cost-to-serve and practical lead-time buffers. This approach reduces stockouts without forcing the same technique on every SKU.
- A/X or B/X: use statistical methods with calibrated Z and periodic safety stock calculation.
- B/Y or C/Y: blended approach; cap extremes and monitor forecast error trends.
- A/Z or C/Z: Average–Max or heuristics with strict ceilings and review of expediting options.
Limits of normal distribution and alternative distributions
Normal-based models can understate right-tail risk, miss seasonality, and struggle with low sales volume. Thieuleux notes these limits and advises shifting methods when data skew or intermittency is present.
Poisson and Binomial fit discrete, low-count demand. Gamma captures skew with a long tail. Specialized approaches, including the “McKinsey” method for sporadic demand, can raise resilience where the standard safety stock formula falls short.
- Seasonal items: ensure the forecast and σ reflect the season window before safety stock calculation.
- Intermittent items: apply Poisson or Croston-style variants, not strict normal.
- Skewed demand: consider Gamma to model tail risk more accurately for safety stock optimization.
Process improvements, expediting, and software support
Operational levers can complement inventory buffers. APICS cites cases where limited expediting, such as selective air freight, reduced total cost for expensive items while preserving service. Make-to-order or finish-to-order can shift buffers upstream where customers accept lead time.
Inventory software like Acctivate automates safety stock analysis by ingesting historical and real-time signals, detecting pattern shifts, and recalculating buffers. Dynamic updates improve safety stock management while reducing manual effort and bias.
| Practice | When to Use | Method Implication | Risk/Cost Trade-off | Example Application |
|---|---|---|---|---|
| ABC/XYZ Targeting | Mixed portfolios by margin and volatility | Group Z-scores and tiered service | Higher service where profit justifies | A/X parts receive 98% CSL; C/Z capped |
| Average–Max with Caps | Low-volume, erratic items | Heuristic safety stock calculation | Controls overstock from spikes | Slow movers with sporadic orders |
| Normal-Based Statistical | Stable demand, adequate history | Z × σ under lead-time scaling | Efficient but tail-light | Fast movers with steady cadence |
| Poisson/Gamma/Binomial | Intermittent or skewed demand | Tail-aware safety stock formula | Improves stockout protection | Service parts and promotional items |
| Selective Expediting | High-cost items with peak risk | Substitute for excess buffers | Pay per event, reduce holding | APICS case using air freight peaks |
| MTO/FTO Shifts | Customers accept lead time | Lower finished-goods buffers | Lead-time risk moved upstream | Configurable products with options |
| Software Automation | Large SKU counts, dynamic markets | Continuous safety stock optimization | Higher accuracy, lower labor | Acctivate recalculating nightly |
Conclusion
An effective safety stock strategy combines statistical precision with practical limitations and a clear service policy. The reorder point must include the right safety stock formula to protect against demand fluctuations and lead time risks. With stable lead times, a demand-only buffer, sized by the Z-factor and the standard deviation over the lead time, is efficient.
When both demand and lead time variability are significant and appear independent, combine their variances. If data shows dependence, use the additive form described by APICS and Bernard Thieuleux to size the safety stock level correctly.
Heuristic methods, such as safety days multiplied by average demand and the Average–Max approach, provide quick solutions but require tight control of outliers. They do not always encode explicit service targets, as highlighted by Thieuleux and Acctivate. Cycle service level and fill rate are not interchangeable. Under high variability, achieving a high fill rate requires significantly larger buffers, a point emphasized in APICS guidance.
These distinctions are critical to safety stock optimization and should be applied before investing in excess stock. The normal distribution does not fit every item. Intermittent or skewed demand may require Poisson, negative binomial, or other models, along with complementary levers such as expediting, make-to-order, or forecast-to-order.
Continuous improvement—cleaner data, stronger forecasting methods, supplier collaboration, and software-enabled monitoring—ensures the safety stock level does not hide process issues or poor lead time control. Executed with discipline, safety stock optimization reduces stockout exposure, stabilizes service performance, and releases working capital. U.S. supply chains operating in volatile markets can use a documented safety stock strategy, grounded in the appropriate safety stock formula, to align inventory with service goals while improving cash efficiency.
FAQ
What is safety stock and how does it protect service levels?
Safety stock, or buffer stock, is extra inventory held to absorb demand variability and lead time uncertainty. It reduces stockout risk during the replenishment cycle and supports a targeted cycle service level. In continuous review systems, safety stock sits within the reorder point to cover volatility until the next order arrives.
What is the standard safety stock formula and when should it be used?
A commonly used statistical safety stock formula under normality and independence is: Safety Stock = Z × sqrt[(σDemand over lead time)² + (σLead time × Average Demand)²]. Use it when both demand and lead time vary and are not correlated. If demand dominates and lead time is stable, simplify to Safety Stock = Z × σDemand over lead time. If lead time variability dominates, use Safety Stock = Z × σLead time × Average Demand.
How do cycle service level and fill rate differ in safety stock analysis?
Cycle service level measures the share of replenishment cycles with no stockout. Fill rate measures the share of total demand volume fulfilled. Under high variability, fill rate can be lower than cycle service level because stockouts, when they occur, are larger. Both metrics guide safety stock strategy, but they are not interchangeable.
How should standard deviation be time-scaled to match lead time?
The standard deviation used in safety stock calculation must reflect the demand over the entire lead time. If σ is calculated on a base period (e.g., weekly) and lead time spans n such periods, scale as σLT = σbase × √n. This keeps the safety stock calculation consistent with the safety stock lead time window.
What is the Average–Max safety stock calculation and its limitations?
The Average–Max method sets Safety Stock = (Maximum Lead Time × Maximum Usage) − (Average Lead Time × Average Usage). It is simple and data-light but sensitive to outliers. One-off spikes or delays can inflate safety stock level. Practitioners often cap extremes, but this does not encode a clear service target.
How do reorder point and EOQ interact with safety stock management?
Reorder point (ROP) triggers replenishment at ROP = Safety Stock + Average Demand × Lead Time. EOQ sets an economical order size under deterministic cost trade-offs. In practice, EOQ governs order quantity while the safety stock equation embeds variability protection within the ROP, aligning cost and service objectives.
When should independent vs. dependent safety stock equations be applied?
Use the independent model (variance combination) when demand and lead time are uncorrelated. If data show that demand surges extend supplier lead times, apply the dependent, additive form: Safety Stock = (Z × σDemand over lead time) + (Z × σLead time × Average Demand). Dependence raises tail risk and increases safety stock inventory.
How should ABC/XYZ classification influence safety stock optimization?
Assign higher Z-scores to high-value or critical A items and moderate Z to B/C items. Match methods to demand patterns: statistical models for stable or medium-volume SKUs (X/Y), and cautious heuristics or alternative distributions for low-volume or intermittent Z items. This policy-driven safety stock strategy aligns service and working capital.
What are alternatives to the normal distribution for low-volume or skewed demand?
For intermittent or skewed demand, Poisson, Gamma, or Binomial models can improve safety stock calculation. These distributions better represent variability in low-frequency sales and reduce bias from normal assumptions.
How can operations levers and software improve safety stock optimization?
Expediting a small share of demand, or shifting select SKUs to make-to-order or finish-to-order, can lower safety stock cost. Inventory software such as Acctivate can automate safety stock analysis, maintain consistent units, and update parameters with real-time data, improving safety stock management across the network.
