In a world dominated by giants like Amazon, it’s increasingly critical for brick-and-mortar retailers to meet customer demands as quickly as possible. One way to achieve this is by ensuring that stock is readily available; otherwise, customers may turn to competitors. Deciding how much stock to keep on the floor is crucial and likely at the heart of a retailer’s operations. In this article, we’ll focus on the importance of considering safety stock determination as a risk mitigation analysis measure.
A General Risk Analysis
The first step in risk management analysis is simply identifying the risk. For retailers, the risk corresponds to running out of stock.
Suppose you’re a company that manages operations very well, always orders on time, and has perfect inventory tracking. Let’s go even further: your minimum stock always matches your sales forecasts over your replenishment lead time. Well, guess what? That’s not enough to prevent you from occasionally running out of stock! It’s normal; inventory shortages are subject to random variables beyond your control. See where I’m going?
Why do we run out of stock, you ask? There are two main causes, each requiring specific management:
- Cause 1: Actual sales during the replenishment lead time exceeded expectations. This is a good problem to have but still carries a significant opportunity cost.
- Cause 2: The replenishment delivery lead time was longer than promised.
These are the only two causes I’ve observed. We’ll discuss possible mitigation measures in the following sections.
Cause 1: Sales Were Higher Than Expected
Why were sales higher than forecasted? The answer is simple: you’ll never achieve perfect forecasting. Of course, it’s always better to make higher-quality forecasts, but regardless of the reason, if there’s always a discrepancy between actual and forecasted sales, it indicates an inherent difficulty in making perfect predictions, and this difficulty is measurable. To protect against this, you should establish a safety stock.
In this case, your reorder point should equal your sales forecast over the lead time plus what we call the safety stock (yes, using the well-known formula that accounts for the desired service level and your forecast error).
Cause 2: Replenishment Lead Time Was Longer Than Expected
Similar to Cause 1, this is again a variable dependent on the predictability of delivery lead time. Even with forecasts based on a specific lead time, if the duration of that lead time changes, the next replenishment might not arrive on time.
You can probably see where this is going: we need to measure this unpredictability and convert it into the number of additional units to keep in stock. The conversion is straightforward: we transition from coverage in days to quantity. At this point, we take the sales forecast we plan to make over the coverage period to convert.
You can follow the same approach as in the previous section and apply it to delivery lead times once all coverage components have been converted into quantities.
Discussion
From a practical standpoint, I’ve often been told that considering safety stock as a risk analysis approach is more burdensome than simply determining a desired coverage between replenishments, or just setting a fixed quantity as safety stock.
The reasoning behind those who prefer these methods is simple: they claim they don’t really have the time (according to them) to manage the system’s deviations. To that, I respond that the system might be poorly tuned if they’re making such statements. It’s easier to input a coverage duration that’s a bit longer than the lead time. There’s only one figure to enter per product, which makes it easy for them to feel in control. Additionally, if sales are higher, the safety stock will also be higher, which is easy to explain, because sales are often easier to consult than sales forecast deviations. To those who bring up this argument, I reply that in a well-tuned system, they also have only one parameter per product (in our case, the desired level of confidence).
However, the compelling argument I would give them isn’t related to ease of management. In fact, I would argue that a client using methods based on arbitrary coverage lacks information, namely whether the coverage entered is sufficient for the most problematic and hardest-to-forecast situations. I’ve often been given the example of products that are only sold on special order (typically large ones). These are the types of products that are difficult to forecast because the forecasted sales volume is low compared to the volume of confirmed sales. Let’s say it’s product A in the ABC class, because you can’t do without a sale. It’s highly likely that this product is hard to forecast. We don’t know when the exceptional sale will occur, but we know we are always at risk. So, what do we do in this case? We simply increase our reserves, which comes down to measuring our level of uncertainty regarding the sales forecasts and linking that information to our desired service level for that product. This approach is somewhat of a catch-all method, which helps identify products that need the most attention by applying the same rule to everyone.
Conclusion
This article has attempted to convince you that determining safety stock should be part of a risk management approach, and the risk in question is the risk of running out of stock when the customer is ready to buy your product. Two tools that help measure and address this risk have been presented: one to mitigate the risk of selling more stock than expected, and the other to protect against uncertainties in delivery time.
