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Manage orders at scale
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Simplify accounting and grow your business
Is your inventory protected against fluctuating demands and vendor lead times? When was the last time you lost business due to a stockout? Stockouts can occur due to a surge in demand or unpredictable procurement lead times. Knowing which products to stock and how much to reorder is critical for optimizing your inventory and maximizing profits. It is also vital to safeguard your business from variability in consumer demand and lead times.
As the name suggests, safety stock is the additional inventory held by a business to meet an unforeseen surge in demand or unpredictable procurement lead time. The main reason why businesses implement a safety stock strategy is to maintain service levels and prevent stockouts.
So, how do you plan your inventory levels when there are too many variables? This where a data-driven safety stock strategy can help.
Primarily, having a safety stock strategy can prepare your business against two factors:
Unless you deal in products that sell consistently all year round, it would be impossible to predict how much stock you need to hold. For example, FMCG products are typically in demand throughout the year, making it easier to determine target inventory levels. The demand and supply are more or less consistent for such products and require fewer amounts of safety stock. On the other hand, seasonal products like air conditioners are much more challenging to forecast. To cover an unexpected increase in demand, you would typically need to maintain a more significant amount of safety stock.
Another reason to use a safety stock strategy is to overcome the impact that vendor lead time delays can have on your business. An unexpected delay at your vendor’s end, in turn, causes your lead times to be affected. Having sufficient safety stock helps you maintain service levels while waiting for products or raw materials from your vendors.
Lead time is the time measured in days between raising a purchase requisition and restocking the product in your warehouse. It includes the time for purchase order approvals, vendor delivery time, and quality inspections. To calculate the standard deviation of lead time, you will need the following:
Let’s take a look at JJ Sportswear’s lead time data for the last five purchases of their best-selling product.
| Expected Time | Actual Time | Variance |
|---|---|---|
| 10 | 8 | 2 |
| 10 | 7 | 3 |
| 10 | 8 | 2 |
| 10 | 12 | -2 |
| 10 | 11 | -1 |
First, let’s calculate the average variance.
Next, let’s add the average variance to the average expected time to determine the standard deviation of lead time.
Before we calculate the average demand, it is important to establish the time frame for the demand data. A good rule of thumb is to use the day sales inventory (DSI) – the time it takes between two reorders.
| Week | Sales Volume |
|---|---|
| W1 | 500 Units |
| W2 | 520 Units |
| W3 | 510 Units |
| W5 | 490 Units |
JJ Sportswear typically reorder their best-selling product every month. In the table above, we use JJ Sportswear’s sales volume for one month broken down into weekly increments. To calculate the average daily demand, we simply take the sum of sales and divide it by the number of days in the month.
The service level of a particular product determines how much safety stock is required to cover uncertainties in demand and lead time. The higher the service level, the more safety stock is needed.
To calculate the safety stock, we”ll need to convert the service level to a value that we can plug into our safety stock formula. To determine the service factor based on the desired service level, we will use a normal distribution chart.
| Service Level Required (%) | Safety Stock Coverage Factor Z-Value | Service Level Required (%) | Safety Stock Coverage Factor Z-Value |
|---|---|---|---|
| 70% | 0.524 | 90% | 1.282 |
| 75% | 0.674 | 91% | 1.341 |
| 80% | 0.842 | 92% | 1.405 |
| 81% | 0.878 | 93% | 1.476 |
| 82% | 0.915 | 94% | 1.555 |
| 83% | 0.954 | 95% | 1.645 |
| 84% | 0.994 | 96% | 1.751 |
| 85% | 1.036 | 97% | 1.881 |
| 86% | 1.080 | 98% | 2.054 |
| 87% | 1.126 | 99% | 2.326 |
| 88% | 1.175 | 99.5% | 2.576 |
| 89% | 1.227 | 99.90% | 3.090 |
For exmple, if you want to achieve a 90% service, the normal distribution will give you a multiplier coefficient of 1.28 to meet 90% of the demand.
To calculate the safety stock, we simply multiply the three figures we have calculated above.
This means that JJ Sportswear would need 864 units of safety stock on hand to meet 90% of the sales demand over an average lead time of 10.8 days.
Now that we know how to calculate the safety stock let’s look at some of the common pitfalls you’d want to avoid.
One common mistake that businesses make during inventory planning is setting a zero safety stock to reduce inventory costs. However, this approach usually results in a decrease in service level and costs the business more in lost sales.
The goal of safety stock, as we have seen above, is to prevent stockouts. However, it is common for businesses to be over-optimistic and target a service level of 100%. Having a service level of 100% theoretically means that you’d be able to meet sales demand at all times. But, it also means that you’d have a large amount of safety stock and, therefore, a large amount of money tied up in inventory.
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