Safety stock (also called buffer stock) is a term used by logisticians to describe a level of extra stock that is maintained to mitigate risk of stockouts (shortfall in raw material or packaging) due to uncertainties in supply and demand. Adequate safety stock levels permit business operations to proceed according to their plans. Safety stock is held when there is uncertainty in demand, supply, or manufacturing yield; it serves as an insurance against stockouts.
Safety stock is an additional quantity of an item held in the inventory in order to reduce the risk that the item will be out of stock, safety stock act as a buffer stock in case the sales are greater than planned and or the supplier is unable to deliver the additional units at the expected time.
With a new product, safety stock can be utilized as a strategic tool until the company can judge how accurate their forecast is after the first few years, especially when used with a material requirements planning worksheet. The less accurate the forecast, the more safety stock is required to ensure a given level of service. With a material requirements planning (MRP) worksheet a company can judge how much they will need to produce to meet their forecasted sales demand without relying on safety stock. However, a common strategy is to try and reduce the level of safety stock to help keep inventory costs low once the product demand becomes more predictable. This can be extremely important for companies with a smaller financial cushion or those trying to run on lean manufacturing, which is aimed towards eliminating waste throughout the production process.
The amount of safety stock an organization chooses to keep on hand can dramatically affect their business. Too much safety stock can result in high holding costs of inventory. In addition, products which are stored for too long a time can spoil, expire, or break during the warehousing process. Too little safety stock can result in lost sales and, thus, a higher rate of customer turnover. As a result, finding the right balance between too much and too little safety stock is essential.
Reasons for keeping safety stock
Safety stocks are mainly used in a “Make To Stock” manufacturing strategy. This strategy is employed when the lead time of manufacturing is too long to satisfy the customer demand at the right cost/quality/waiting time.
The main goal of safety stocks is to absorb the variability of the customer demand. Indeed, the Production Planning is based on a forecast, which is (by definition) different from the real demand. By absorbing these variations, safety stock improves the customer service level.
Creating a safety stock will also prevent stock-outs from other variations, like an upward trend in customer demand.
Safety stock is used as a buffer to protect organization from stockouts caused by inaccurate planning or poor schedule adherence by suppliers. As such, its cost (in both material and management) is often seen as a drain on financial resources that results in reduction initiatives. In addition, time sensitive goods such as food, drink, and other perishable items could spoil and go to waste if held as safety stock for too long. Various methods exist to reduce safety stock, these include better use of technology, increased collaboration with suppliers, and more accurate forecasting  In a lean supply environment, lead times are reduced, which can help minimize safety stock levels thus reducing the likelihood and impact of stockouts. Due to the cost of safety stock, many organizations opt for a service level led safety stock calculation; for example, a 95% service level could result in stockouts, but is at a level that is satisfactory to the company. The lower the service level, the lower the requirement for safety stock.
An Enterprise Resource Planning system (ERP system) can also help an organization reduce its level of safety stock. Most ERP systems provide a type of Production Planning module. An ERP module such as this can help a company develop highly accurate and dynamic sales forecasts and sales and operations plans. By creating more accurate and dynamic forecasts, a company reduces their chance of producing insufficient inventory for a given period and, thus, should be able to reduce the amount of safety stock that they require. In addition, ERP systems use established formulas to help calculate appropriate levels of safety stock based on the previously developed production plans. While an ERP system aids an organization in estimating a reasonable amount of safety stock, the ERP module must be set up to plan requirements effectively.
The size of the safety stock depends on the type of inventory policy that is in effect. An inventory node is supplied from a "source" which fulfills orders for the considered product after a certain replenishment lead time. In a periodic inventory policy the inventory level is checked periodically (such as once a month) and an order is placed at that time as to meet the expected demand until next order. In this case, the safety stock is calculated considering the demand and supply variability risks during this period plus the replenishment lead time. If the inventory policy is continuous policy (such as an Order point-Order Quantity policy or an Order Point-Order Up To policy) the inventory level is continuously monitored and orders are placed with freedom of time. In this case, safety stock is calculated considering the risk of only the replenishment lead time. If applied correctly, continuous inventory policies can lead to smaller safety stock whilst ensuring higher service levels, in line with lean processes and more efficient overall business management.
Methods for calculating safety stocks
Reorder Point Method with Demand and Lead Time Uncertainty for Type I Service
- Demand: the amount of items consumed by customers, usually a random variable.
- Lead time: the delay between the time the reorder point (inventory level which initiates an order) is reached and renewed availability.
- Service level: the desired probability of meeting demand during lead time without a stockout. Naturally, when the desired service level is increased, the required safety stock increases as well.
- Forecast error: an estimate of how far actual demand may be from forecast demand.
- is the service level, and is the inverse distribution function of a standard normal distribution with cumulative probability ; for example, =1.65 for 95% service level.
- and are the mean and standard deviation of lead time.
- and are the mean and standard deviation of demand in each unit time period.
The reorder point can then be calculated as:
The first term in the ROP formula is the average demand during the lead time. The second term is the safety stock. If the lead time is deterministic, i.e. , then the ROP formula is simplified as .
Issues with this approach
- The inclusion of service level by means of NORMSINV assumes a normal distribution of lead time demand. First of all, real demand cannot be negative. This skews normality leading to consistent overestimation of safety stock by this formula. Secondly, supply is not a random event but a conscious decision, often triggered by batch sizes, economic order quantities or ROP formulas on the receiving side. With a very large amount of sources (for example, consumers of a central retail warehouse) that may not be an issue but otherwise it is (for example, for a manufacturer that supplies these retail warehouses)
- The use of average and standard demand assumes it is statically constant. For seasonal demand (for example high in summer, low in winter) the formula will consistently produce stock outs in summer and waste in winter. Similar errors apply for demand that grows or declines.
- Lead time is extremely hard to quantify in complex manufacturing and/or purchase environment, which has become the norm in global supply chains that span many independent partners. In practice, lead time is estimated by a rule of thumb that hardly improves on estimating safety stock with a rule of thumb. Even when lead time is correctly quantified, the formula assumes supply (production and purchase) is statistically constant, which is not always the case.
Type II Service
Another popular approach described by Nahmias uses the standardized unit loss integral L(z), given by:
Where is cumulative distribution function for the standard normal. Let β be the proportion of demands met from stock (service level), Q the order quantity and σ the standard deviation of demand, then the following relationship holds:
In this case the safety stock is given by:
and the expected number of units out of stock during and order cycle is given by σL(z).
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