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Finding a balance between risk and capital spending to determine the optimum number of spares for each part

How many times have you needed a critical spare part, only to find that it wasn’t available?  Or noticed that expensive spare part that’s just been sitting on the shelves for the last three years – and there are two of them?

If the spare item is very inexpensive and the consequence of a stock-out is very large you might think the answer is obvious; just stock more than you would ever possibly needasdasdasdasd,000+ loss depending on how long you’re down. In the second scenario, your immediate reaction to the suggestion of throwing one away might be to keep it, just in case. Who is qualified to make that judgment call?

It would be nice if there were a quantitative way to determine the probability of an event happening that required that spare part to help make that decision.

The key to better spare parts decision-making 

One of the best ways to economically minimize or even eliminate the impact of unexpected shutdowns is through keeping the optimum number of spares in your warehouse. It seems like a painfully obvious answer, but the disastrous results of not doing so is even more painful. Once you dig into it, it’s not always as straightforward an answer as you might think.

What if you don’t have the budget for all the spare parts you need? Just make a quick calculation of the hourly cost of downtime multiplied by the time it will take to get that spare part that’s not in stock. The risk of downtime should help you convince anyone in the decision-making process to stock the optimum number of spares.

In an ideal world, it would be nice to be able to keep a spare part of every single item in the plant but that’s expensive, and also doesn’t necessarily make sense. The bigger challenge is quantifying the risk of having some spare parts on hand but not others. How do you decide? The key is in better decision-making.

Taking out the challenge in spare parts decision-making

Going back to the expensive spare part that’s been sitting on your shelf – a system reliability model will help you clearly see what kind of risk you are taking by eliminating one spare part. If the risk is high, the probability of an event occurring that will need that spare part will be high so you’ll likely want to keep that second spare. If you have plenty of warehouse shelf space, maybe this isn’t a big concern.

But what if you do have limited shelf space? And what if you have limited capital to spend on spare parts?

Regardless of your situation, having a system reliability modeling tool is going to help you make a more informed decision to determine which spares to keep and the optimum number of spares you should have on hand. This tool does this by giving you the knowledge of how each spare part impacts plant availability and reliability.

You’ll want a model that takes into account:

• cost of spare and new order lead time
• current equipment failure and repair rates and distributions
• quantity currently kept in stock
• redundancy in service (online spares)
• redundancy in unit operations (multiple trains)
• buffer storage of product that mitigates downtime
• other factors unique to the system under analysis

This level of detail is just a part of what will go into your comprehensive model. All LCSIM models also take into account alternate flowpaths, sprint capacity, dynamic feed, supply chain issues, intermediate storage capacities and other variables that affect plant operation. The model can identify critical failures in the process and the probability of the frequency they will occur – so you can adequately stock parts for events with high probability. All of these systems are highly interrelated so it’s important to consider these in making spare parts decisions.

How to determine the optimum number of spares

Here are four steps to help you determine the number of optimum spares for your plant or site:

Step 1: Define your objective or target

Identify spare parts to optimize.  Look for the items that could potentially bring production down and are dependent on a warehouse spare(s) to effect repair.

Step 2: Identify what KPI’s you will use to measure those targets

Key performance indicators that are typically used include:

• Total Production
• Availability
• Shutdowns and Slowdowns – # of occurrences, magnitude and duration
• Buffer tank empty events
• Spare stock-outs

Step 3: Collect and assess the current spare parts information

At this step you will gather as much data as possible, including any factors that may impact desired objective. This may include cost of spare, new order lead time, current spare part consumption rate and repair time for typical problems seen in the past. You can then construct a model yourself or use outside expertise to help fill in any gaps or for the creation of your model.

Step 4: Evaluate data to determine the optimum number of spares

With your newly constructed model, you can optimize for the previous identified KPI’s. You can predict plant availability with current design and how it might increase if you, for example, kept two of those critical spares in stock. You could look to see which spares are causing the biggest areas of revenue loss in your plant – it is quantified in a way that will help you justify that change.

Let the data speak for itself through the simulation model.

Find your sweet spot between risk and capital spend

For plants with lot of revenue at stake, it’s critical to have the right spares at the right time to support equipment reliability and productivity. The spare parts do not necessarily need to be in a warehouse nearby, you could have a supplier partnership where they keep one in stock for you. It just needs to be available when you need it.

The real cost of not having a quantitative, data-informed spare parts inventory lies within the risk of revenue lost when those spare parts are not available. The key is in balancing the level of risk you’re willing to take versus spending extra capital to keep those critical and less critical spare parts in stock.

Your company needs you find this sweet spot and bring them to a higher standard of reliability, maintainability and performance of assets. If not you, then who will take the lead?