Just as the renowned astronomer Copernicus transformed our understanding of astronomy by placing the sun at the center of our universe, today, we invite you to re-center your approach to inventory management. And while not quite as enlightening, this advice will help your company avoid being caught in the gravitational pull of inventory woes—constantly orbiting between stockouts, surplus gravity, and the unexpected cosmic expenses of expediting?

In this article, we’ll walk you through the process of crafting a spare parts inventory plan that prioritizes availability metrics such as service levels and fill rates while ensuring cost efficiency. We’ll focus on an approach to inventory planning called Service Level-Driven Inventory Optimization. Next, we’ll discuss how to determine what parts you should include in your inventory and those that might not be necessary. Lastly, we’ll explore ways to enhance your service-level-driven inventory plan consistently.

In service-oriented businesses, the consequences of stockouts are often very significant.  Achieving high service levels depends on having the right parts at the right time. However, having the right parts isn’t the only factor. Your Supply Chain Team must develop a consensus inventory plan for every part, then continuously update it to reflect real-time changes in demand, supply, and financial priorities.

Managing inventory with Service-level-driven planning combines the ability to plan thousands of items with high-level strategic modeling. This requires addressing core issues facing inventory executives:

• Lack of control over supply and associated lead times.
• Unpredictable intermittent demand.
• Conflicting priorities between maintenance/mechanical teams and Materials Management.
• Reactive “wait and see” approach to planning.
• Misallocated inventory, causing stockouts and excess.
• Lack of trust in systems and processes.

The key to optimal service parts management is to grasp the balance between providing excellent service and controlling costs. To do this, we must compare the costs of stockout with the cost of carrying additional spare parts inventory. The costs of a stockout will be higher for critical or emergency spares, when there is a service level agreement with external customers, for parts used in multiple assets, for parts with longer supplier lead times, and for parts with a single supplier. The cost of inventory may be assessed by considering the unit costs, interest rates, warehouse space that will be consumed, and potential for obsolescence (parts used on a soon-to-be-retired fleet have a higher obsolescence risk, for example).

To arbitrate how much stock should be put on the shelf for each part, it is critical to establish consensus on the desired key metrics that expose the tradeoffs the business must make to achieve the desired KPIs. These KPIs will include Service Levels that tell you how often you meet usage needs without falling short on stock, Fill Rates that tell you what percentage of demand is filled, and Ordering costs detail the expenses incurred when you place and receive replenishment orders. You also have Holding costs, which encompass expenses like obsolescence, taxes, and warehousing, and Shortage costs that pertain to expenses incurred when stockouts happen.

An MRO business or Aftermarket Parts Planning team might desire a 99% service level across all parts – i.e., the minimum stockout risk that they are willing to accept is 1%. But what if the amount of inventory needed to support that service level is too expensive? To make an informed decision on whether there is going to be a return on that additional inventory investment, you’ll need to know the stockout costs and compare that to the inventory costs. To get stockout costs, multiply two key elements: the cost per stockout and the projected number of stockouts. To get inventory value, multiply the units required by the unit cost of each part. Then determine the annual holding costs (typically 25-35% of the unit cost). Choose the option that yields a total lower cost. In other words, if the benefit associated with adding more stock (reduced shortage costs) outweighs the cost (higher inventory holding costs), then go for it. A thorough understanding of these metrics and the associated tradeoffs serves as the compass for decision-making.

Modern software aids in this process by allowing you to simulate a multitude of future scenarios. By doing so, you can assess how well your current inventory stocking strategies are likely to perform in the face of different demand and supply patterns. If anything falls short or goes awry, it’s time to recalibrate your approach, factoring in current data on usage history, supplier lead times, and costs to prevent both stockouts and overstock situations.

Enhance your service-level-driven inventory plan consistently.

In conclusion, it’s crucial to assess your service-level-driven plan continuously. By systematically constructing and refining performance scenarios, you can define key metrics and goals, benchmark expected performance, and automate the calculation of stocking policies for all items. This iterative process involves monitoring, revising, and repeating each planning cycle.

The depth of your analysis within these stocking policies relies on the data at your disposal and the configuration capabilities of your planning system. To achieve optimal outcomes, it’s imperative to maintain ongoing data analysis. This implies that a manual approach to data examination is typically insufficient for the needs of most organizations.

For information on how Smart Software can help you meet your service supply chain goals with service-driven planning and more, visit the following blogs.

–   “Explaining What  Service-Level Means in Your Inventory Optimization Software”  Stocking recommendations can be puzzling, especially when they clash with real-world needs.  In this post, we’ll break down what that 99% service level means and why it’s crucial for managing inventory effectively and keeping customers satisfied in today’s competitive landscape.

–  “Service-Level-Driven Planning for Service Parts Businesses” Service-Level-Driven Service Parts Planning is a four-step process that extends beyond simplified forecasting and rule-of-thumb safety stocks. It provides service parts planners with data-driven, risk-adjusted decision support.

–   “How to Choose a Target Service Level.” This is a strategic decision about inventory risk management, considering current service levels and fill rates, replenishment lead times, and trade-offs between capital, stocking and opportunity costs.  Learn approaches that can help.

–   “The Right Forecast Accuracy Metric for Inventory Planning.”  Just because you set a service level target doesn’t mean you’ll actually achieve it. If you are interested in optimizing stock levels, focus on the accuracy of the service level projection. Learn how.

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.

Did you know that it was Benjamin Franklin who invented the lightning rod to protect buildings from lightning strikes? Now, it’s not every day that we must worry about lightning strikes, but in today’s unpredictable business climate, we do have to worry about supply chain disruptions, long lead times, rising interest rates, and volatile demand. With all these challenges, it’s never been more vital for organizations to accurately forecast parts usage, stocking levels, and to optimize replenishment policies such as reorder points, safety stocks, and order quantities.  In this blog, we’ll explore how companies can leverage innovative solutions like inventory optimization and parts forecasting software that utilize machine learning algorithms, probabilistic forecasting, and analytics to stay ahead of the curve and protect their supply chains from unexpected shocks.

Spare Parts Planning Solutions
Spare parts optimization is a key aspect of supply chain management for many industries. It involves managing the inventory of spare parts to ensure they are available when needed without having excess inventory that can tie up capital and space. Optimizing spare parts inventory is a complex process that requires a deep understanding of usage patterns, supplier lead times, and the criticality of each part for the business.

In this blog, our primary emphasis will be on the crucial aspect of inventory optimization and demand forecasting. However, other approaches highlighted below for spare parts optimization, such as predictive maintenance and 3D printing, Master Data Management, and collaborative planning should be investigated and deployed as appropriate.

1. Predictive Maintenance: Using predictive analytics to anticipate when a part is likely to fail and proactively replace it, rather than waiting for it to break down. This approach can help companies reduce downtime and maintenance costs, as well as improve overall equipment effectiveness.
2. 3D printing: Advancements in 3D printing technology are enabling companies to produce spare parts on demand, reducing the need for excess inventory. This not only saves space and reduces costs but also ensures that parts are available when needed.
3. Master Data Management: Data Management platforms ensure that part data is properly identified, cataloged, cleansed, and organized. All too often, MRO organizations hold the same part number under different SKUs. These duplicate parts serve the same purpose but require different SKU numbers to ensure regulatory compliance or security.  For example, a part used to support a government contract may be required be sourced from a US manufacturer to stay in compliance with “Buy America” regulations.  It’s critical that these part numbers be identified and consolidated into one SKU, when possible, to keep inventory investments in check.
4. Collaborative Planning: Collaborating with suppliers and customers to share data, forecasts, and plan demand can help companies reduce lead times, improve accuracy, and reduce inventory levels. Forecasting plays an essential role in collaboration as sharing insights on purchases, demand, and buying behavior ensures suppliers have the information they need to ensure stock availability for customers.

Inventory Optimization
Abraham Lincoln was once quoted as saying, “Give me six hours to chop down a tree, and I will spend the first four sharpening the axe”? Lincoln knew that preparation and optimization were key to success, just like organizations need to have the right tools, such as inventory optimization software, to optimize their supply chain and stay ahead in the market. With inventory optimization software, organizations can improve their forecasting accuracy, lower inventory costs, improve service levels, and reduce lead times. Lincoln knew that sharpening the axe was necessary to accomplish the job effectively without overexerting.  Inventory Optimization ensures that inventory dollars are allocated effectively across thousands of parts helping ensure service levels while minimizing excess stock.

Spare parts play a decisive role in maintaining operational efficiency, and the lack of critical parts can lead to downtime and reduced productivity. The sporadic nature of spare parts demand makes it difficult to predict when a specific part will be required, resulting in the risk of overstocking or understocking, both of which can incur costs for the organization.  Additionally, managing lead times for spare parts poses its own set of challenges. Some parts may have lengthy delivery times, necessitating the maintenance of adequate inventory levels to avoid shortages. However, carrying excess inventory can be costly, tying up capital and storage space.

Given the myriad of challenges facing materials management departments and spare parts planners, planning demand, stocking levels, and replenishment of spare parts without an effective inventory optimization solution is akin to attempting to chop down a tree with a very blunt axe! The sharper the axe, the better your organization will be able to contend with these challenges.

Smart Software’s Axe is the Sharpest
Smart Inventory Optimization and Demand Planning Software uses a unique empirical probabilistic forecasting approach that results in accurate forecasts of inventory requirements, even where demand is intermittent. Since nearly 90% of spare and service parts are intermittent, an accurate solution to handle this type of demand is required.   Smart’s solution was patented in 2001 and additional innovations were recently patented in May of 2023 (announcements coming soon!).  The solution was awarded as a finalist in the APICS Technological Innovation Category for its role in helping transform the resource management industry.

The Role of Intermittent Demand
Intermittent demand does not conform to a simple normal or bell-shaped distribution that makes it impossible to forecast accurately with traditional, smoothing-based forecasting methods.  Parts and items with intermittent demand – also known as lumpy, volatile, variable or unpredictable demand – have many zero or low-volume values interspersed with random spikes of demand that are often many times larger than the average. This problem is especially prevalent in companies that manage large inventories of service and spare parts in industries such as aviation, aerospace, power and water supply and utilities, automotive, heavy asset management, high tech, as well as in MRO (Maintenance, Repair, and Overhaul).

Scenario Analysis
Smart’s patented and award-winning technology rapidly generates tens of thousands of possible scenarios of future demand sequences and cumulative demand values over an item’s lead time. These scenarios are statistically similar to the item’s observed data, and they capture the relevant details of intermittent demand without relying on the assumptions commonly made about the nature of demand distributions by traditional forecasting methods. The result is a highly accurate forecast of the entire distribution of cumulative demand over an item’s lead time. The bottom line is that with the information these demand distributions provide, companies can easily plan safety stock and service level inventory requirements for thousands of intermittently demanded items with nearly 100% accuracy.

Benefits
Implementing innovative solutions from Smart Software such as SmartForecasts for statistical forecasting, Demand Planner for consensus parts planning, and Inventory Optimization for developing accurate replenishment drivers such as min/max and safety stock levels will provide forward-thinking executives and planners with better control over their organization’s operations.  It will result in the following benefits:

1. Improved Forecasting Accuracy: Accurate demand forecasting is fundamental for any organization that deals with spare parts inventory management. Inventory optimization software uses sophisticated algorithms to analyze historical usage patterns, identify trends and forecast future demand with a high degree of accuracy. With this level of precision in forecasting, organizations can avoid the risk of overstocking or understocking their spare parts inventory.
2. Lower Inventory Costs: One major challenge that supply chain leaders face when dealing with spare parts inventory management is the cost associated with maintaining an optimal stock of spares at all times. By optimizing inventory levels using modern technology systems like artificial intelligence (AI), machine learning (ML), and predictive analytics, organizations can reduce carrying costs while ensuring they have adequate stocks available when needed.
3. Improved Service Levels: When it comes to repair and maintenance services, time is money! Downtime due to the unavailability of critical spare parts can result in lost productivity and revenue for businesses across industries such as manufacturing plants, power generation facilities, or data centers managing IT infrastructure equipment. Optimizing your spare parts inventory ensures that you always have the right amount on hand, reducing downtime caused by waiting for deliveries from suppliers.
4. Reduced Lead Times: Another benefit that accrues from accurate demand forecasting through modern warehouse technologies is reduced lead time in delivery which leads to better customer satisfaction since customers will receive their orders faster than before thus improving brand loyalty. Therefore, the adoption of new strategies driven by AI/ML tools creates value within supply chain operations leading to increased efficiency gains not only limited reductionism cost but also streamlining processes related to production scheduling, logistics transportation planning among others

Conclusion
Through the utilization of inventory optimization and demand planning software, organizations can overcome various challenges such as supply chain disruptions, rising interest rates, and volatile demand. This enables them to reduce costs associated with excess storage space and obsolete inventory items. By leveraging sophisticated algorithms, inventory optimization software enhances forecasting accuracy, ensuring organizations can avoid overstocking or under-stocking their spare parts inventory. Additionally, it helps lower inventory costs by optimizing levels and leveraging technologies like artificial intelligence (AI), machine learning (ML), and predictive analytics. Improved service levels are achieved as organizations have the right quantity of spare parts readily available, reducing downtime caused by waiting for deliveries. Furthermore, accurate demand forecasting leads to reduced lead times, enhancing customer satisfaction and fostering brand loyalty. Adopting such strategies driven by AI/ML tools not only reduces costs but also streamlines processes, including production scheduling and logistics transportation planning, ultimately increasing efficiency gains within the supply chain.