In today’s competitive business landscape, companies are constantly seeking ways to improve their operational efficiency and drive increased revenue. Optimizing service parts management is an often-overlooked aspect that can have a significant financial impact. Companies can improve overall efficiency and generate significant financial returns by effectively managing spare parts inventory. This article will explore the economic implications of optimized service parts management and how investing in Inventory Optimization and Demand Planning Software can provide a competitive advantage.

The Importance of Optimized Service Parts Planning:

Optimized service parts management plays a vital role in mitigating inventory risks and ensuring critical spare parts availability. While subjective planning may work on a small scale, it becomes insufficient when managing large inventories of intermittently demanded spare parts. Traditional forecasting approaches simply fail to accurately account for the extreme demand variability and frequent periods of zero demand that is so common with spare parts.  This results in large misallocations of stock, higher costs, and poor service levels.

The key to optimized service parts management lies in understanding the trade-off between service and cost. Inventory Optimization and Demand Planning Software powered by probabilistic forecasting and Machine learning Algorithms can help companies better understand the cost vs. benefit of each inventory decision and wield inventory as a competitive asset. By generating accurate demand forecasts and optimal stocking policies such as Min/Max, Safety Stock Levels, and Reorder Points in seconds, companies can know how much is too much and when to add more. By wielding inventory as a competitive asset, companies can drive up service levels and drive down costs.

Improve the Financial Outcome of Spare Parts Planning

1. Accurate forecasting is crucial to optimize inventory planning and meet customer demand effectively. State-of-the-art demand planning software accurately predicts inventory requirements, even for intermittent demand patterns. By automating forecasting, companies can save time, money, and resources while improving accuracy.
2. Meeting customer demand is a critical aspect of service parts management. Companies can enhance customer satisfaction, loyalty, and increase their chances of winning future contracts for the asset-intensive equipment they sell by ensuring the availability of spare parts when needed. Through effective demand planning and inventory optimization, organizations can reduce lead times, minimize stockouts, and maintain service levels, thereby improving the financial impact of all decisions.
3. Financial gains can be achieved through optimized service parts planning, including the reduction of inventory and product costs. Excess storage and obsolete inventory can be significant cost burdens for organizations. By implementing best-of-breed inventory optimization software, companies can identify cost-effective solutions, driving up service levels and reducing costs. This leads to improved inventory turnover, reduced carrying costs, and increased profitability.
4. Procurement planning is another essential aspect of service parts management. Organizations can optimize inventory levels, reduce lead times, and avoid stockouts by aligning procurement and the associated order quantities with accurate demand forecasts. For example, accurate forecasts can be shared with suppliers so that blanket purchase commitments can be made. This provides the supplier revenue certainty and, in exchange, can hold more inventory, thereby reducing lead times.
5. Intermittent demand planning is a particular challenge in spare parts management. Conventional rule-of-thumb approaches fall short in handling demand variability effectively. This is because traditional approaches assume demand is normally distributed when in reality, it is anything but normal. Spare parts demand random bursts of large demand intersperse many period of zero demand.  Smart Software’s solution incorporates advanced statistical models and machine learning algorithms to analyze historical demand patterns, enabling accurate planning for intermittent demand. Companies can significantly reduce stockout costs and improve efficiency by addressing this challenge.

Evidence from Smart Software’s Customers:

Investing in Smart Software’s Inventory Optimization and Demand Planning Software enables companies to unlock cost savings, elevate customer service levels, and enhance operational efficiency. Through accurate demand forecasting, optimized inventory management, and streamlined procurement processes, organizations can achieve financial savings, meet customer demands effectively, and improve overall business performance.

• Metro-North Railroad (MNR) experienced an 8% reduction in parts inventory, reaching a record high customer service level of 98.7%, and reduced inventory growth for new equipment from a projected 10% to only 6%. Smart Software played a crucial role in identifying multi-year service part needs, reducing administrative lead times, formulating stock reduction plans for retiring fleets, and identifying inactive inventory for disposal. MNR saved costs, maximized disposal benefits, improved service levels, and gained accurate insights for informed decision-making, ultimately improving their bottom line and customer satisfaction.
• Seneca Companies, an industry leader in automotive petroleum services, adopted Smart Software to model customer demand, control inventory performance, and drive replenishment. Field service technicians embraced its use, and total inventory investment decreased by more than 25%, from $11 million to $8 million, while maintaining first-time fix rates of 90%+.
• A leading Electric Utility implemented Smart IP&O in just 3 months and then used the software to optimize its reorder points and order quantities for over 250,000 spare parts. During the first phase of the implementation, the platform helped the Utility reduce inventory by $9,000,000 while maintaining service levels. The implementation was part of the company’s strategic supply chain optimization initiative.

Optimizing Service Parts Planning for Competitive Advantage

Optimized service parts management is crucial for companies seeking to improve efficiency, reduce costs, and ensure the availability of necessary spare parts. Organizations can unlock significant value in this field by investing in Smart Software’s Inventory Optimization and Demand Planning Software. Companies can achieve better financial performance and gain a competitive edge in their respective markets through improved data analysis, automation, and inventory planning.

Smart Software is designed for the modern marketplace, which is volatile and always changing. It can handle SKU proliferation, longer supply chains, less predictable lead times, and more intermittent and less forecastable demand patterns. It can also integrate with virtually every ERP solution on the market, by field-proven seamless connections or using a simple import/export process supported by Smart Software’s data model and data processing engine. By using Smart Software, companies can leverage inventory as a competitive asset, enhance customer satisfaction, drive up service levels, push down costs, and save substantial money.

Managing spare parts presents numerous challenges, such as unexpected breakdowns, changing schedules, and inconsistent demand patterns. Traditional forecasting methods and manual approaches are ineffective in dealing with these complexities. To overcome these challenges, this blog outlines key strategies that prioritize service levels, utilize probabilistic methods to calculate reorder points, regularly adjust stocking policies, and implement a dedicated planning process to avoid excessive inventory. Explore these strategies to optimize spare parts inventory and improve operational efficiency.

Bottom Line Upfront

​1.Inventory Management is Risk Management.

2.Can’t manage risk well or at scale with subjective planning – Need to know service vs. cost.

3.It’s not supply & demand variability that are the problem – it’s how you handle it.

4.Spare parts have intermittent demand so traditional methods don’t work.

5.Rule of thumb approaches don’t account for demand variability and misallocate stock.

6.Use Service Level Driven Planning  (service vs. cost tradeoffs) to drive stock decisions.

7.Probabilistic approaches such as bootstrapping yield accurate estimates of reorder points.

8.Classify parts and assign service level targets by class.

9.Recalibrate often – thousands of parts have old, stale reorder points.

10.Repairable parts require special treatment.

Do Focus on the Real Root Causes

Intermittent Demand

• Slow moving, irregular or sporadic with a large percentage of zero values.
• Non-zero values are mixed in randomly – spikes are large and varied.
• Isn’t bell shaped (demand is not Normally distributed around the average.)
• At least 70% of a typical Utility’s parts are intermittently demanded.

Normal Demand

• Very few periods of zero demand (exception is seasonal parts.)
• Often exhibits trend, seasonal, or cyclical patterns.
• Lower levels of demand variability.
• Is bell-shaped (demand is Normally distributed around the average.)

Don’t rely on averages

• OK for determining typical usage over longer periods of time.
• Often forecasts more “accurately” than some advanced methods.
• But…insufficient for determining what to stock.

Don’t Buffer with Multiples of Averages

Example:  Two equally important parts so let’s treat them the same.
We’ll order more  when On Hand Inventory ≤ 2 x Avg Lead Time Demand.

Do use Service Level tradeoff curves to compute safety stock

Standard Normal Probabilities

OK for normal demand. Doesn’t work with intermittent demand!

Don’t use Normal (Bell Shaped) Distributions

• You’ll get the tradeoff curve wrong:

– e.g., You’ll target 95% but achieve 85%.

– e.g., You’ll target 99% but achieve 91%.

• This is a huge miss with costly implications:

– You’ll stock out more often than expected.

– You’ll start to add subjective buffers to compensate and then overstock.

– Lack of trust/second-guessing of outputs paralyzes planning.

Why Traditional Methods Fail on Intermittent Demand: 

Traditional Methods are not designed to address core issues in spare parts management.

Need: Probability distribution (not bell-shaped) of demand over variable lead time.

• Get: Prediction of average demand in each month, not a total over lead time.
• Get: Bolted-on model of variability, usually the Normal model, usually wrong.

Need: Exposure of tradeoffs between item availability and cost of inventory.

• Get: None of this; instead, get a lot of inconsistent, ad-hoc decisions.

Do use Statistical Bootstrapping to Predict the Distribution:

Then exploit the distribution to optimize stocking policies.

How does Bootstrapping Work?

24 Months of Historical Demand Data.

Bootstrap Scenarios for a 3-month Lead Time.

Bootstrapping Hits the Service Level Target with nearly 100% Accuracy!

• National Warehousing Operation.

Task: Forecast inventory stocking levels for 12,000 intermittently demanded SKUs at 95% & 99% service levels

Results:

At 95% service level, 95.23% did not stock out.

At 99% service level, 98.66% did not stock out.

This means you can rely on output to set expectations and confidently make targeted stock adjustments that lower inventory and increase service.

Set Target Service Levels According to Order Frequency & Size

Recalibrate Reorder Points Frequently

• Static ROPs cause excess and shortages.
• As lead time increases, so should the ROP and vice versa.
• As usage decreases, so should the ROP and vice versa.
• Longer you wait to recalibrate, the greater the imbalance.
• Mountains of parts ordered too soon or too late.
• Wastes buyers’ time placing the wrong orders.
• Breeds distrust in systems and forces data silos.

Do Plan Rotables (Repair Parts) Differently

Summary

1.Inventory Management is Risk Management.

2.Can’t manage risk well or at scale with subjective planning – Need to know service vs. cost.

3.It’s not supply & demand variability that are the problem – it’s how you handle it.

4.Spare parts have intermittent demand so traditional methods don’t work.

5.Rule of thumb approaches don’t account demand variability and misallocate stock.

6.Use Service Level Driven Planning  (service vs. cost tradeoffs) to drive stock decisions.

7.Probabilistic approaches such as bootstrapping yield accurate estimates of reorder points.

8.Classify parts and assign service level targets by class.

9.Recalibrate often – thousands of parts have old, stale reorder points.

10.Repairable parts require special treatment.

Managing spare parts inventories can feel impossible. You don’t know what will break and when. Feedback from mechanical departments and maintenance teams is often inaccurate. Planned maintenance schedules are often shifted around, making them anything but “planned.”   Usage (i.e., demand) patterns are most often extremely intermittent, i.e., demand jumps randomly between zero and something else, often a surprisingly big number. Intermittency, combined with the lack of significant trend or seasonal patterns, render traditional time-series forecasting methods inaccurate. The large number of part-by-locations combinations makes it impossible to manually create or even review forecasts for individual parts.   Given all these challenges, we thought it would be helpful to outline a number of do’s (and their associated don’ts).

1. Do use probabilistic methods to compute a reorder points and Min/Max levels
   Basing stocking decisions on average daily usage isn’t the right answer. Nor is reliance on traditional forecasting methods like exponential smoothing models. Neither approach works when demand is intermittent because they don’t take proper account of demand volatility. Probabilistic methods that simulate thousands of possible demand scenarios work best. They provide a realistic estimate of the demand distribution and can handle all the zeros and random non-zeros. This will ensure the inventory level is right-sized to hit whatever service level target you choose.
    
2. Do use service levels instead of rule-of-thumb methods to determine stocking levels
   Many parts planning organizations rely on multiples of daily demand and other rules of thumb to determine stocking policies. For example, reorder points are often based on doubling average demand over the lead time or applying some other multiple depending on the importance of the item. However, averages don’t account for how volatile (or noisy) a part is and will lead to overstocking less noisy parts and understocking more noisy parts.
    
3. Do frequently recompute stocking policies
   Just because demand is intermittent doesn’t mean nothing changes over time. Yet after interviewing hundreds of companies managing spare parts inventory, we find that fewer than 10% recompute stocking policies monthly. Many never recompute stocking policies until there is a “problem.” Across thousands of parts, usage is guaranteed to drift up or down on at least some of the parts. Supplier lead times can also change. Using an outdated reorder point will cause orders to trigger too soon or too late, creating lots of problems. Recomputing policies every planning cycle ensures inventory will be right-sized. Don’t be reactive and wait for a problem to occur before considering whether the Min or Max should be modified. By then it’s too late – it’s like waiting for your brakes to fail before making a repair. Don’t worry about the effort of recomputing Min/Max values for large numbers of SKU’s: modern software does it automatically. Remember: Recalibration of your stocking policies is preventive maintenance against stockout!
    
4. Do get buy-in on targeted service levels
   Inventory is expensive and should be right-sized based on striking a balance between the organization’s willingness to stock out and its willingness to budget for spares. Too often, planners make decisions in isolation based on pain avoidance or maintenance technicians’ requests without consideration of how spending on one part impacts the organization’s ability to spend on another part. Excess inventory on one part hurts service levels on other parts by disproportionally consuming the inventory budget. Make sure that service level goals and associated inventory costs of achieving the service levels are understood and agreed to.
    
5. Do run a separate planning process for repairable parts
   Some parts are very expensive to replace, so it is preferable to send them to repair facilities or back to the OEM for repair. Accounting for the supply side randomness of when repairable parts will be returned, and knowing whether to wait for a repair or to purchase an additional spare, are critical to ensuring item availability without inventory bloat. This requires specialized reporting and the use of probabilistic models.  Don’t treat repairable parts like consumable parts when planning.
    
6. Do count what is purchased against the budget – not just what is consumed
   Many organizations will allocate total part purchases to a separate corporate budget and ding the mechanical or maintenance team’s budget for parts that are used. In most MRO organizations, especially in public transit and utilities, the repair teams dictate what is purchased. If what is purchased doesn’t count against their budget, they will over-buy to ensure there is never any chance of stockout. They have literally zero incentive to get it right, so tens of millions in excess inventory will be purchased. If what is purchased is reflected in the budget, far more attention will be paid to purchasing only what is truly needed. Recognizing that excess inventory hurts service by robbing the organization of cash that could otherwise be used on understocked parts is an important step to ensuring responsible inventory purchasing.

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Why Days of Supply Targets Don’t Work when Computing Safety Stocks

CFOs tell us they need to spend less on inventory without impacting sales.  One way to do that is to move away from using targeted day of supply to determine reorder points and safety stock buffers.   Here is how a days of supply model works:

1. Compute average demand per day and multiply the demand per day by supplier lead time in days to get lead time demand
2. Pick a days of supply buffer (i.e., 15, 30, 45 days, etc.). Use larger buffers being used for more important items and smaller buffers for less important items.
3. Add the desired days of supply buffer to demand over the lead time to get the reorder point. Order more when on hand inventory falls below the reorder point

Here is what is wrong with this approach:

1. The average doesn’t account for seasonality and trend – you’ll miss obvious patterns unless you spend lots of time manually adjusting for it.
2. The average doesn’t consider how predictable an item is – you’ll overstock predictable items and understock less predictable ones. This is because the same days of supply for different items yields a very different stock out risk.
3. The average doesn’t tell a planner how stock out risk is impacted by the level of inventory – you’ll have no idea whether you are understocked, overstocked, or have just enough. You are essentially planning with blinders on.

There are many other “rule of thumb” approaches that are equally problematic.  You can learn more about them in this post. 

A better way to plan the right amount of safety stock is to leverage probability models that identify exactly how much stock is needed given the risk of stock-out you are willing to accept.   Below is a screenshot of Smart Inventory Optimization that does exactly that.  First, it details the predicted service levels (probability of not stocking out) associated with the current days of supply logic.  The planner can now see the parts where predicted service level is too low or too costly.  They can then make immediate corrections by targeting the desired service levels and level of inventory investment. Without this information, a planner isn’t going to know whether the targeted days of safety stock is too much, too little, or just right resulting in overstocks and shortages that cost market share and revenue. 

When deciding on the right stocking parameters for spare parts and service parts, it is important to distinguish between consumable and repairable service parts.  These differences are often overlooked by service parts planning software and can result in incorrect estimates of what to stock.  Different approaches are required when planning for consumables vs. repairable spare parts.

First, let’s define these two types of spare parts.

• Consumable parts are spares contained within the equipment which are replaced rather than repaired when they fail. Examples of consumable parts include batteries, oil filters, screws, and brake pads.  Consumable spare parts tend to be lower-cost parts for which replacement is cheaper than repair or repair may not be possible.
• Repairable parts are parts that are capable of being repaired and returned to service after failing due to causes like wear and tear, damage, or corrosion. Repairable service parts tend to be more expensive than consumable parts, so repair is usually preferable to replacement. Examples of repairable parts include traction motors in rail cars, jet engines, and copy machines.

Traditional spare parts planning software fail to do the job

Traditional parts planning software is not well-adapted to deal with the randomness in both the demand side and the supply side of MRO operations.

Demand-Side Randomness
Planning for consumable spare parts requires calculation of inventory control parameters (such as reorder points and order quantities, min and max levels, and safety stocks). Planning to manage repairable service parts requires calculation of the right number of spares. In both cases, the analysis must be based on probability models of the random usage of consumables or the random breakdown of repairable parts.  For over 90% of these parts, this random demand is “intermittent” (sometimes called “lumpy” or “anything but normally distributed”). Traditional spare parts forecasting methods were not developed to deal with intermittent demand. Relying on traditional methods leads to costly planning mistakes. For consumables, this means avoidable stockouts, excess carrying costs, and increased inventory obsolescence. For repairable parts, this means excessive equipment downtime and the attendant costs from unreliable performance and disruption of operations.

Supply-Side Randomness
Planning for consumable spare parts must take account of randomness in replenishment lead times from suppliers. Planning for repairable parts must account for randomness in repair and return processes, whether provided internally or contracted out. Planners managing these items often ignore exploitable company data. Instead, they may cross their fingers and hope everything works out, or they may call on gut instinct to “call audibles” and then hope everything works out.  Hoping and guessing cannot beat proper probability modeling. It wastes millions annually in unneeded capital investments and avoidable equipment downtime.