Calculating reorder points & safety stock

Reorder point formula, safety stock & lead times – what you need to know

6 min read

A common way to calculate inventory reorder points is to use a formula that focuses on ordering a specific number stock days. These calculations are done manually, often in excel, and the data is then input into an ERP system, so it has the relevant parameters to create reorder alerts.

In this post we take a closer look at the most common reorder point formula used in supply chain management and then uncover why this method often results in poor order fulfilment.

Reorder point formula definition:

Reorder point problems and solutionsThe reorder point formula works out when stock hits a level at which it’s the right time to reorder. If you order too soon, you’ll have excess items that will cost money to store – order too late and you’ll risk going out of stock and losing sales.

Common reorder point formula

The most basic formula for reorder point planning is based on how many items you sell per day. You then multiply this by the number of days of stock you want to carry. This is usually related to lead time, to ensure your ordering point is set at a level where there is enough stock to last until your next delivery:

reorder point formula

This reorder point formula requires you to calculate your estimated demand during your lead time e.g how much stock you think you will sell (or consume) on average, each day, until new stock arrives. This is usually based on previous consumption data over an appropriate time period e.g the previous four weeks, six months etc. The length of time used is often driven by industry, for example a retailer buying on shorter lead times and needing to react more to recent market trends would look over a shorter time period than a wholesaler, typically buying on a longer lead time with a more consistent customer base.

Reorder point formula example

As an example, let’s imagine a business, ABC Autoparts, selling car parts from the Far East into the UK market. Imagine they sell on average 250 brake pads a week (approx. 36 a day) and their lead time is 28 days.

ABC Autoparts historic four-week consumption:
Reorder point example one
ABC Autoparts needs at least 28 days’ worth of stock to prevent availability issues.

Reorder point = 36 x 28 = 1008 units

For an extra level of precaution, they also need to add safety stock. The easiest way to do this is to add a specific number of safety stock days to the reorder point, however there are more advanced ways to calculate safety stock.

Adding an extra week’s safety stock increases the reorder point value to 1258 units e.g 1008 + 250 = 1258 units.

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Reorder point problems and solutions

Whilst simple to calculate and manage, there are fundamental flaws with this basic reorder point formula:

1. Using historic average consumption often leads to inaccurate lead time demand forecasts

Using historic average consumption to calculate lead time forecasts works well if you sell the same amount of each item in every period. But if sales fluctuate over a couple of periods, then you’re going to suffer with either stockouts, or excess inventory.

Any lead time demand forecast needs to consider the following factors:

Each of these factors is easy to miss when forecasting is based on rolling average consumption data. But if they remain under the radar, they can cause big demand forecasting issues that lead to sold out lines or, in contrast, obsolete stock.

Unfortunately, it’s extremely challenging and time-consuming to consider these factors when forecasting demand is a manual task. Plus, manual forecasts can go out-out-date within days of calculation. Ideally forecasts need to be reviewed regularly and reorder point calculations updated accordingly, to account for market changes.

The most efficient way to do this is to use demand forecasting software. A tool, such as EazyStock, will automatically analyse historic demand data for each inventory item. It then assigns a demand type, based on its position in the product life cycle and a relevant forecasting algorithm. These are reviewed daily, along with indicators of seasonality and demand trends and forecasts are dynamically adjusted as required.

2. Reorder point calculations are disconnected from customer satisfaction

Reorder point challengesBasing reorder points on a set number of stock days e.g average daily usage, has no connection back to customer service or customer satisfaction. Stock days is merely an accounting formula to help measure and track stock turnover. When used in purchasing, it will not help optimise stock levels or achieve optimum levels of stock availability.

The main objective of all inventory management practices should be to fulfil as many orders as possible, so customers are happy with their service. To ensure this happens, replenishment activity needs to be linked back to a metric that focuses on stock availability.

That’s why more and more inventory management teams are beginning to use service level targets. For example, if ABC Autoparts sets their service level target for brake pads at 99%, this means they will be able to fulfil 99% of orders and only have to say ‘no’ 1% of the time. This allows their order fulfilment operations to be directly linked to their customers’ service experience. With target service levels set, based on stock availability, they can then work out how much stock they need to carry to achieve them and set corresponding reordering parameters.

Whilst service level targets are easy to set, tracking and measuring whether they have been achieved is much more difficult to do manually. An inventory optimisation tool, such as EazyStock, is designed to do this work for you.

3. The reorder point formula presumes static lead times

The reorder point formula presumes that lead times are consistent, but this is not always the case, for example ABC Autoparts’ goods have a long distance to travel and shipping could get delayed. Bad weather, customs issues, supplier shutdowns and more recently pandemics are all events that can delay lead times and result in stock shortages and lost sales. Presuming average lead times are a constant is therefore unrealistic and will result in stock availability problems.

It’s key, therefore, to regularly review supplier delivery times and update reorder points to reflect the current situation. However, this is a difficult task when calculations are carried out manually. A good inventory optimisation tool will be able to track lead times automatically. If lead times then increase, the system will automatically adjust reordering parameters, ensuring you have the correct inventory levels to cover demand until the delivery arrives.

4. Inadequate safety stock formula cannot account for demand and supply variability

You may be thinking that safety stock will act as the buffer against any fluctuations in demand or lead times. And this is true for items where demand and lead times are consistent and predictable, but not when they are more erratic.

Let’s revisit ABC Autoparts. In the table below we can see that actual sales of brake pads only fluctuated between 250 and 300 units per week. This meant that one week of safety stock e.g 250 units, was enough to cover the increase in sales.

ABC Autoparts actual four-week consumption:

However, if we look at air conditioning units, the story is much different. ABC Autoparts predicted that possible demand would be much more erratic in their forecast, as sales often rise and fall with the British weather. But the average weekly predicted consumption still worked out to 250 units, so they kept the safety stock level at 250 units.  As a consequence, ABC Autoparts ran out of stock in week two:

Reorder point example three

For products whose demand is more erratic, higher levels of safety stock are required, but how do you calculate the optimal level?

The answer is to use a formula with probabilistic distribution. Whilst a probabilistic safety stock formula would not help ABC Autoparts predict the weather, it would add a greater allowance for the fact that there will be a higher variance in demand for air conditioning units in the summer.

An inventory optimisation tool links safety stock quantities to target service levels, whilst also accounting for demand volatility. For example, using EazyStock, service levels for brake pads that have regular demand are likely to be higher e.g 99%, than air conditioning units at, say, 95%, where demand is more erratic.

The safety stock calculation in each instance will also be different. EazyStock uses different probability distributions to calculate safety stock depending on the demand characteristics of the product. In our example the air conditioning units with highly varying sales volumes will use a different safety stock calculation than the brake pads, which have more consistent sales volumes. Using different calculations, tailored to the demand characteristics of each product, is the only way to achieve optimum stock levels and meet 99% and 95% service levels respectively.

Automating reorder point and safety stock calculations

In reality, most stock items in your warehouse will have different demand characteristics, some being affected by seasonality, others by market trends. In addition, many lead times are not as reliable or regular as they used to be. Using a ‘one size fits all’ reorder point and safety stock formula to calculate replenishment needs will, therefore, lead to stock availability challenges.

Manually calculating safety stock and reorder points to input into your ERP is a time-consuming task that quickly leads to outdated data being used to produce replenishment alerts. Instead, the answer is to connect an inventory optimisation tool. This will automatically do these calculations for you, based on the latest market dynamics, keeping your replenishment parameters as relevant as possible.

If you’d like to know more about EazyStock, please get in touch with our team for a no obligation chat.

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