Introduction
There is always a motivation to hold a low level of spares especially if the spare is expensive and holding cost is significant. However, it may run a risk of spare unavailability which in turn causes production loss. We can quantify this loss as penalty cost. A cost optimized restock policy is achieved when the holding cost is equal to (or slightly higher than) the penalty cost.
This presentation uses AssetStudio’s AeROS reliability analytical tool to optimize the restock policy for a critical spare.
Simulation approach for cost optimization restock policy
Cost optimization restock policy
For Cost Optimization analysis, the follow inputs are required:
- Penalty cost per day: The cost running out of spare per day for a unit.
- Holding cost per day: The cost of keeping a unit in warehouse per day.
- Demand model: The time-between-request of spare distribution.
- Lead-time model: The lead-time distribution of the spare.
The output results (the suggested policy and associated costs) are:
- Max. Level: The suggested maximum holding quantity (total quantity in warehouse and in shipments).
- ROP (Re-Ordering Point): The quantity level that is set to trigger an order.
- Min. Level: The expected minimum quantity level.
- Ave. Level: The average quantity level.
- Achieved Serviceability: The probability that spares are available in the warehouse.
- Qty Consumed (Year): The average quantity of spares consumed per year.
- No. Restock (Year): The average number of restock per year.
- Penalty Cost (Year): The average penalty cost per year due to spare unavailability.
- Holding Cost (Year): The average cost of holding spares per year.
The Scenario
Assuming the holding cost of spart part A is $5/day. If the part is not available, the operation will incur a cost of $10,000/day due to loss of production. From the past record, the time-to-dispense (demand model) follow an Exponential distribution with a mean of 22 days. When an order for this part is placed, it takes 20 days to arrive. What is the Re-ordering policy such that the cost of maintaining the stock is minimum?
The input information is entered into the Spare Analysis tool in AeROS.
Based on the input, the simulation algorithm walks through different values of ROP (Re-Ordering Point) and compute the corresponding “Penalty cost/Year” and “Holding cost/Year”. The desired ROP is when the Penalty cost/Year is equal or lower than the Holding cost/Year.
The output results are as shown below.
The dialog allows a batch processing of up to 500 items. In this example, we are analyzing only one item.
The total cost for this spare restock strategy is the sum of penalty cost and holding cost (about $11.3 k). If the ROP (Reordering Point) is more than 3 units, penalty cost will reduce, but the holding cost will increase such that the total cost is greater than $11.3 k. If the ROP less than 3 units, the holding cost will decrease, but the penalty cost will increase such that the total cost is greater than $11.3 k.
The following plot shows the spare availability profile in the warehouse.
The blue curve is the average availability in the warehouse over 5000 simulations. The green curve is the profile of spare quantity of the last simulation.
Comments
Cost optimization strategy requires penalty cost information, which may not be straight forward to obtain. The penalty cost is derived from the production downtime due to spare unavailability. However, in some cases, operators can compensate for the lost productions by working extra time or boosting the production rate for a period of time.
Hence, some asset management professionals derive the spare restock strategy based on serviceability. A 95% serviceability means that they are willing to take the 5% risk that spare is not available when it is needed. AeROS’s Spare Analysis tool also provides this option to solve for serviceability based restock strategy.
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