You know disruptive technologies have reached the tipping point when non-IT pros build business plans around them. This is exactly what’s happening with IoT. Because of its ability to drive wide-ranging, game-changing improvements, IoT is starting to be used across all aspects of business operations. One of the newest, and most impactful, areas is spare parts inventory management, a key aspect of the post-sale supply chain.
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Maintaining the right level of spare parts is critical. As you can probably guess, carrying excessive inventory can be prohibitively expensive. But if you have too little, you’ll slow product repairs, hurt customer experience and end up spending more money purchasing new parts for stock replenishment. The problem is, traditional best practices for managing spare parts — using time-series algorithms combined with sales forecasting, seasonality, gut instincts and simple rules of thumb to determine how many parts to stock — are woefully inaccurate because:
- They’re static, “review-and-stock” endeavors based largely on historical demand data
- The algorithms don’t account for variables resulting from failed parts in the field
Knowing this, many companies hedge their bets by purposefully overstocking. Others think they’re maintaining the right levels, but unknowingly overstock. In either case, they’re wasting a lot of money.
New IoT-driven inventory planning
The key to accurately stocking parts is knowing which ones are likely to fail and when they’ll need to be replaced. Some businesses have attempted to use IoT data to understand product failure impacts on inventory planning. However, most of the IoT monitoring programs are designed to respond to signal failures. Plus, IoT data collection is often haphazard and emphasizes the few pieces of equipment that are starting to fail, rather than the whole. This makes it impossible to generate a sound baseline for analyzing product performance and predicting failures — which, in turn, makes it impossible to accurately forecast spare parts needs.
The good news is there’s a new inventory planning algorithm that builds IoT-based failure data directly into the equation. Developed at Massachusetts Institute of Technology Center for Transportation and Logistics, it enables businesses to accurately forecast needs. By using this methodology and analyzing historical failure data on the entire installed base, businesses can predict the exact spare parts they’re likely to need, when and in what quantity.
The better news is that it doesn’t take a huge team to capture IoT data because not much data is needed. As few as 1,000 signals can drive substantial stock reductions.
The best news is that using the IoT-driven parts inventory methodology can help reduce stock by 6-10%. Imagine what that could do to your bottom line.
Getting the better of your instincts
When I tell inventory management execs they can and should dramatically reduce spare parts inventory, they, understandably, have a visceral “no way” reaction. The instinct is to increase, not decrease, stock. Nobody wants to be caught with a shortage. The consequences of missed service-level agreements and angry customers are too fierce.
But when they see the data, run the numbers and learn that they will also be able to maintain — or even enhance — service levels and fill rates by building IoT parts failures into their planning, interest is piqued.
- Reduce inventory up to 10%
In addition to tightening spare parts forecasting, businesses can be more proactive and prescriptive when taking actions to avoid and quickly fix machine failures. This, too, contributes to equipment uptime and reduces the need for spare parts.
- Save substantial money
Carrying costs, for example, physical space, parts handling, and deterioration and obsolescence, average about 25% of inventory value. Thus, even just 6% stock reduction can save hundreds of thousands to millions of dollars, depending on the value of the parts.
- Maintain or improve service levels and fill rates
By aligning service operations with detailed maps of parts demand patterns, businesses can increase responsiveness. Not only are they holding less stock, they’re better able to fulfill parts requests and deliver a timely, improved customer experience.
Advanced analytics drive even more savings
Knowing which parts in the field are likely to fail and, therefore, which spares you’ll need at any given moment, has implications beyond inventory management. You can use that same IoT data to drive a closed-loop system with asset recovery, focusing in a very surgical way on bringing the most in-demand parts back into inventory.
By applying operational segmentation, you can identify tiers of parts to prioritize for returns, and then create automated business rules that target those high-value/high-demand parts so they can be recovered and refurbished quickly. This reduces new buys for stock replenishment, saving even more time and money.
So why not check your gut instinct at the door, and use IoT to reduce spare parts stock, keep customers happy and slash costs? It’s safer and easier than you think.
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