Stochastic forecasting and cloud-based planning help AIOs and MRO providers reduce spare parts inventory while keeping the machinery running smoothly.
For asset-intensive organizations (AIOs) and maintenance, repair, and overhaul (MRO) service providers, having the right parts at the right time is a crucial part of a profitable business. Stock-outs and machine downtime impact customer service levels, but overstocking expensive parts is wasteful.
To cope with the unpredictability and complexity of spare parts demand, MRO planners must be able to predict the required materials for each job, facts that are not always certain until repairs are underway. Advanced analytics can be used to detect trends, patterns, and seasonality within historical MRO data and can provide a great advantage to optimizing inventories when looking to future resource planning.
One way to look at future spare parts needs is the desired level of certainty that the part will be in stock when you need it. In other words, what is your risk appetite for stocking out? The answer might depend on the part’s cost, its lead time, or the cost of the downtime a stock-out would cause. Probabilistic (stochastic) forecasting, a type of advanced analytics, provides stockout probability levels for each amount of inventory on hand considering the patterns and trends around needing that particular part in the past.
|Inventory on Hand||Probability of Stockout||Suggested Order Quantity|
In the example above, the Suggested Order Quantity assumes a stockout risk tolerance of >1%. However, it could be true that because the cost of downtime for the machines this part repairs is not great, an organization might tolerate a 10% risk of stockout, and only ensure they have 90 parts on hand. When considering purchase requirements for MRO and spare parts planning, the probability of stockout, lead time to receive a spare part, cost of machine downtime, and risk appetite all need be considered at the SKU level.
Planners managing the inventory needs in spreadsheets or a homegrown system might find their tools are not scalable or as flexible as needed. Between SKU proliferation, part obsolescence, bloated inventories, and expensive downtime, materials planners might find themselves worried at night about maintaining service levels and remaining profitable. To learn how a cloud-based planning platform powered by probabilistic forecasting can help optimize inventory and automate the forecasting and planning process, request a demo now.