Inventory is one of the working capital components that can be influenced by poor demand information and can be a big drain in the cash-to-cash cycle.
Although not measured and monitored from a supply chain perspective, receivables and payables are as important to monitor in the business model. Optimal inventory is defined as the inventory that is just sufficient to meet your customer orders.
Since this is a balancing act, demand forecast accuracy summarily determines both your inventory levels and service levels. You can make a trade-off between the two for a given demand forecast error.
A small effort on the demand side will result in tremendous benefits and improved demand information to help the supply chain. Even if the demand is highly uncertain, you can install a demand management process that will link itself to the retail or the final customer demand to help determine the order forecast for the lead time.
Yes, it is true you can achieve a 99% or more customer service level by keeping extremely high levels of inventory, especially if you are in the dark about your demand forecasts. Or, if the organization has no idea how customer demand is generated.
You may hear a number of supply chain experts say that demand forecast is tough and hence a waste of time. They may have fancy solutions that advocate an expensive re-engineering of the supply chain to accommodate any type of demand. Although that is technically possible, it is not a useful way to spend organizational resources.
Historical demand forecast metrics affect the amount of inventory cushion you need to hold to cover future forecast volatility. This is done through the safety stock measure that in statistical terms, is an estimation of confidence intervals using a one-tailed test.
Safety Stock = Customer Service Level * Standard Error of the Demand during the Lead time
Safety Stock = Customer Service Level * RMSE * Square-root of (Lead time)
Safety stock is the margin of error required based on the customer service level and the deviation of the demand during the lead time. The customer service level is the z-value in standard statistics for calculating confidence intervals.
In practice, people use the daily average of this historical data as an approximation for the mean and in essence, they ignore the forecast. The correction to this method is to use the daily average of the forecast. This also requires breaking out the monthly forecast into a weekly or daily forecast.
This will result in two components to the deviation namely,
Safety stock calculations need to be adjusted depending on how wide the Lead Time is compared to how accurate the daily distribution is. For example, if the monthly forecast is very accurate but the daily distribution is incorrect but the Lead Time is three weeks then, you may end up carrying incorrect safety stock during the Lead time. Generally, if the Lead Time is the same as the forecast bucket, most problems are solved.
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