While globalization has brought organizations immense benefits, the greater distances involved make today’s supply chains more volatile than ever. A port strike or a blocked canal on the other side of the world, or a viral social media trend, could trigger extreme stock-outs that hinder service levels. Safety stock can help mitigate such scenarios.
But what is safety stock, and how do organizations determine the necessary volume to avoid empty shelves without drowning in excess stock? The solution is summarized in three words: dynamic safety stock (DSS). DSS uses real-time data to fine-tune safety stock levels continuously. Learn all about it in this article.
What Is a Dynamic Safety Stock?
Dynamic safety stock (DSS) is an adaptive buffer inventory management strategy. It continuously adjusts safety stock levels in response to changing business conditions and market signals.
This strategy uses machine learning algorithms and real-time data. These algorithms analyze the data for attributes like demand variability to calculate the additional inventory each SKU requires at any given moment. Other key attributes include:
- Sales velocity
- Lead times
- Promotional impacts
- Supplier reliability
- Seasonal trends
This dynamic approach to safety stock management is critical today due to the increasing volatility of modern supply chains. For instance, a decade ago, supply reliability was relatively predictable. But today, it’s subject to supply chain disruptions like geopolitical instability (think America-China trade disputes) and natural disasters. Moreover, DSS equips supply chain managers with real-time, data-driven recommendations that enable them to make faster, highly adaptive inventory management decisions.
What Is the Difference Between Static and Dynamic Safety Stock?
Explore how static and dynamic safety stock strategies differ in this quick comparison table:
| Category | Static Safety Stock | Dynamic Safety Stock |
| Definition | Fixed approach to safety stock management. Buffer inventory volume is calculated based on historical averages. | Adaptive management strategy. Safety stock is continuously optimized based on live fluctuating internal and external conditions. |
| Calculation Method | Simple formulas using historical data (50% rule). | Real-time data feeds, algorithms, machine learning, and statistical analysis of variabilities through MEIO inventory optimization. |
| Updates | Quarterly or annual reviews. | Continuous real-time recalibration based on current conditions. |
| Market Response | Reactive. | Proactive. |
| Key Use Cases | Stable markets and limited SKU portfolios. | Volatile markets, seasonal products, complex supply chains, and large SKU portfolios. |
| Advantages | Hassle-free implementation. | Reduced stockouts and excess inventory, improved service levels, and cost efficiency. |
| Cons | Risk of stockouts or excess inventory during volatile periods. | Requires investment in technology and training. More complex to implement initially. |
What is the 50% Rule for Safety Stock?
The 50% rule for safety stock is one of the most popular methods used in static safety stock calculation. Enterprises multiply average daily sales by the average lead time and then divide the result by two to determine optimal buffer stock quantities.
Key Components of a Dynamic Safety Stock Strategy
A dynamic safety stock strategy comprises the following interconnected components:
- Demand Variability & Lead Time Uncertainty: A DSS strategy considers demand signals like point-of-sale data and weather conditions to predict how demand patterns might evolve. DSS strategies also continuously monitor supplier performance and supply chain conditions to assess how lead time variability affects inventory risk.
- Forecast Accuracy and Market Responsiveness: Dynamic systems track forecasting performance across products, time horizons, and market conditions. This allows organizations to adjust safety stock based on confidence in demand predictions. Additionally, DSS strategies react swiftly to market changes, which prevents stockouts during unexpectedsales opportunitiesand overstocking when demand suddenly drops.
- Service Level Objectives and Risk Tolerance: A DSS approach recognizes that different items require different availability levels based on profitability, customer expectations, and competitive dynamics.
AI and machine learning models continuously analyze these core components to identify and adjust safety stock calculations based on patterns human analysts might miss.
How to Calculate Dynamic Safety Stock
Now that you know thedynamic safety stock definition and its core components, how do you calculate DSS? Below is a step-by-step breakdown:
Data Collection
DSS demands both historical and forward-looking indicators. Critical data for supply chain optimization via DSS includes:
- Demand history
- Supplier performance
- Promotional calendars
- Market trends
- Seasonal patterns
Define and Calculate Key Variables
To accurately quantify and buffer against uncertainty, you must define variables directly impacting inventory levels. Define and measure the following critical inputs:
- Demand Variability: Use statistical measures like standard deviation and coefficient of variation across different periods to predict variance in customer demand.
- Lead Time Variability: Track the actual lead times for each supplier using standard deviation to quantify their reliability.
- Service Level Objectives: Establish target availability levels for different product categories based on business priorities, profit margins, customer expectations, and competitive requirements.
- Forecast Accuracy: Track mean absolute percentage error (MAPE) and bias indicators to measure how well your current forecasting methods predict actual demand across different products and time horizons. This approach will allow you to determine where predictions consistently over- or under-estimate demand.
Apply the Dynamic Safety Stock Formula
Calculate the dynamic safety stock using the formula:
Safety Stock = Service Factor (Z) x square root((Average Lead Time (L)x Standard Deviation of Demand(σd))^2 + (Average Demand (D)x Standard Deviation of Lead Time(σL))^2)
Apply Real-Time Adjustment Factors
Consider and integrate other real-time factors like current market conditions into your DSS calculation to determine the “when,” “why,” and “how to best respond” in a constantly changing environment.
Monitor and Recalibrate Continuously
As noted, DSS continuously evolves. Track actual performance against predictions and automatically adjust your calculation parameters as new data becomes available.
The Role of Forecasting Tools
Modern forecasting tools, often powered by AI and machine learning, provide a continuous stream of accurate, real-time data for calculating dynamic safety stock. In other words, with the right inventory optimization platform, you won’t need to manually track metrics like demand variability — it’ll automate these complex analyses.
Example of Dynamic Safety Stock Calculation
Say a fashion retailer needs to calculate DSS for a trending jacket (average daily demand = 50 units, σₚ = 15 units). Supplier lead time averages 7 days (σₗ = 2 days), with a 95% service level (Z=1.65).
Step 1: Calculate demand during lead time (D):
D= 50units/day X 7 days= 350 units
Step 2: Compute DSS using the earlier highlighted formula:
DSS = 1.65 X sqrt((202 X7) + (222 X 350)
= 1.65 X sqrt((400×7)+ (4X350)
= 1.65 X sqrt (3800+1400
= 1.65x sqrt (4200)
=1.65×64.8 = 107 units
Result: Maintain 107 units as dynamic safety stock to cover 95% of demand/lead time fluctuations.
Tools and Technologies for Managing Dynamic Safety Stock
The “how to calculate DSS” section demonstrates that DSS is heavily statistical. While it’s possible to perform these calculations using spreadsheets, this approach is incredibly tedious, error-prone, and counterintuitive to the very purpose of dynamic safety stock.
You need tools and technologies to process multiple data streams, perform complex calculations, and generate actionable real-time recommendations to manage and maximize DSS effectively. At a minimum, deploy the following:
- Enterprise Resource Planning (ERP) Systems: For historical and real-time data integration and centralization
- AI-powered Demand Forecasting & ML Analytics Platforms: To analyze the data and generate probabilistic demand forecasts for DSS calculations
- Real-Time Monitoring and Alerting Systems: To provide the live insights needed to make a DSS strategy truly dynamic
- Statistical Analytics Engines: To handle the complex mathematical modeling DSS requires and analyze scenarios that would overwhelm basic spreadsheet tools
GAINS: Transform Inventory Management Without Overhaul
Adopting the DSS strategy for buffer inventory management is one of the best steps you can take in your organization. The good news is that you don’t need to rip and replace your existing systems.
GAINS offers a composable AI/ML-powered platform that integrates seamlessly with your existing ERP and supply chain systems. This allows your organization to embrace adaptive safety stock strategies incrementally. Request a demo today to explore how GAINS helps you achieve a dynamic safety stock strategy.
