Model Persediaan Probabilistik

Summary

This video explains probabilistic inventory models, focusing on calculating safety stock to prevent stockouts. It contrasts probabilistic models with deterministic ones, where demand and lead time are uncertain. Various scenarios demonstrate how inventory can either run out early, arrive late, or match the expected timeline. Using normal distribution, the lecturer illustrates how to calculate the required safety stock by determining the Z-score and standard deviation. The video provides an example calculation, guiding viewers through determining the proper inventory buffer to maintain optimal stock levels and avoid supply shortages.

Takeaways

• 😀 Probabilistic inventory models deal with demand and lead time that cannot be predicted with certainty, requiring the use of probability distributions.
• 😀 Unlike deterministic models where demand and lead time are known in advance, probabilistic models account for uncertainty in both demand and lead time.
• 😀 There are three possible scenarios in probabilistic inventory management: stockout before the order arrives, stockout exactly when the order arrives, or no stockout when the order arrives.
• 😀 Safety stock is required to mitigate the risks of stockouts due to uncertainty in demand or lead time in probabilistic inventory models.
• 😀 The concept of 'safety stock' can be modeled using a normal distribution to estimate the level of inventory needed to cover demand fluctuations.
• 😀 The distribution of demand during lead time follows a normal curve, and the deviation from the average demand is used to calculate safety stock.
• 😀 Safety stock is determined by the standard deviation (σ) of demand, and it is adjusted using a safety factor (Z), representing the desired service level.
• 😀 In the normal distribution curve, the shaded area represents the safety stock, and the unshaded area represents the risk of stockouts.
• 😀 The formula to calculate safety stock is: Safety Stock = Z * σ, where Z is the safety factor based on the desired probability of stockout and σ is the standard deviation.
• 😀 Example calculations involve using the Z-score corresponding to the desired service level (e.g., 95%) and multiplying it by the standard deviation of demand to find the appropriate safety stock value.

Q & A

What distinguishes probabilistic inventory models from deterministic inventory models?

Probabilistic inventory models differ from deterministic models in that demand and lead time are not known with certainty. Instead, they are modeled using probability distributions, whereas deterministic models assume that both demand and lead time are known exactly.

What are the three possible outcomes when inventory is not known with certainty?

The three possible outcomes are: 1) Inventory runs out before the order arrives, 2) Inventory runs out exactly when the order arrives, and 3) Inventory is still available when the order arrives.

Why is safety stock important in inventory management?

Safety stock is important because it helps protect against stockouts due to uncertainty in demand or lead time. It acts as a buffer to ensure that the business can continue to meet customer demand even when these factors are unpredictable.

How is safety stock typically calculated in probabilistic inventory models?

Safety stock is calculated using the formula: Safety Stock = Z × σ, where Z is the Z-value corresponding to the desired service level, and σ is the standard deviation of demand during the lead time.

What is the role of the normal distribution in inventory control?

The normal distribution is used to model the variability in demand and lead time. By approximating these factors with a normal distribution, inventory managers can estimate the probability of stockouts and calculate the required safety stock.

What does the Z-value represent in the calculation of safety stock?

The Z-value represents the number of standard deviations away from the mean (average demand) that corresponds to the desired service level. For example, a Z-value of 1.645 corresponds to a 95% service level.

What is the significance of the area under the normal curve in inventory models?

The area under the normal curve represents the probability of certain events occurring. For instance, the area under the curve to the right of the Z-value represents the probability of stockouts, while the area to the left corresponds to the probability of having enough inventory.

How do you calculate the standard deviation of demand in the example provided?

To calculate the standard deviation, you first subtract the mean from each data point (representing monthly demand), square the differences, sum them, divide by the number of data points, and then take the square root of the result.

How do you interpret the Z-value of 1.645 in the context of safety stock calculation?

A Z-value of 1.645 corresponds to a 95% service level, meaning there is a 95% probability that inventory will be available when needed. This Z-value is used to calculate the safety stock required to achieve this level of service.

What happens when you want to reduce the risk of stockouts to a lower probability, like 5%?

If you aim for a lower risk of stockouts (e.g., 5% chance of running out of stock), the Z-value will increase, which means that the required safety stock will also increase to ensure that you can meet the demand with higher confidence.