You’re looking into warehouse automation, and you’ve already decided that an ASRS system is the way to go. But with ASRS costs reaching well into the millions of dollars, you’ve got to get the size right. Overestimate, and you waste valuable capital. Underestimate, and you won’t meet future demand. So we suggest a more scientific approach to ASRS planning. This is how a consumer products manufacturer cut the projected installation costs of their ASRS system by $1.5 million.
We took a different approach for estimating the necessary rack space to help this consumer products manufacturer optimize their ASRS system. We wanted to make sure our client would not be wasting capital on capacity that might not be used, but we also had to ensure that the company would not sacrifice potential revenue by installing a system that wasn’t large enough to fulfill their customers’ orders. Traditionally, engineers have tackled this dilemma in one of three ways:
1. Estimate the average inventory level requirement three years from now – While this may seem like a low cost option, it’s based on the hope that the warehouse Industrial Output India could expand in time if volume increased. This kind of assumption puts your organization at risk and can end up costing millions in lost sales revenue.
2. Estimate peak inventory levels three years from now – This method is more conservative, as it takes volume spikes into account. But it’s still shortsighted and doesn’t account for the long-term investment that’s about to be made.
3. Estimate peak inventory levels 10 years from now – This is very conservative, and it is a widely accepted approach for conventional warehouse planning. However, it doesn’t provide the level of accuracy needed when planning an automated warehouse – where excess capacity is extremely costly.
Most engineers rely on the third method for their ASRS planning, and that’s exactly what our client asked us to do. However, we chose a different method – one that would deliver much more accurate estimates and create significant savings for the company.
To define our client’s actual ASRS requirements, we estimated their safety stock inventory requirement (minimum levels) and predicted production quantities 10 years into the future. Taking advantage of proven industrial engineering theory, we calculated the Economic Order Quantity (EOQ) to account for changes in demand.
EOQ= (square root of) [2k(mu)/HP1]
K=Setup Cost H=Holding Cost
P=Cost of Pallet (mu);=Average Weekly Demand
This allowed us to estimate costs based on different service levels. This consumer products manufacturer set their service level requirement at 99.99% – with the goal of reaching 100% customer satisfaction. This near-perfect service level determined the required safety stock level, and that’s how we arrived at the EOQ.
We began the process by collecting data from the consumer products manufacturer. Once we received the data, we ran our comprehensive analysis. With little-to-no room for error, we Manufacturing Engineer Skills Resume determined the safety stock levels required to meet our client’s 99.99% service level, carefully balancing the likelihood of a stock-out against the cost of a larger ASRS system.
By determining the optimal amount of required rack space, the company was able to halve the estimated costs of their ASRS installation – effectively saving $1.5 million in capital expenditure. The client was also given the confidence that future demand would safely be met.

By master