Is safety stock regarded as emergency spares or as a day-to-day buffer against spikes in demand? Knowing the difference and configuring your ERP properly will make a big difference to your bottom line.

The Safety Stock field in your ERP system can mean very different things depending on the configuration. Not understanding these differences and how they impact your bottom line is a common issue we’ve seen arise in implementations of our software.

Implementing inventory optimization software starts with new customers completing the technical implementation to get data flowing.  They then receive user training and spend weeks carefully configuring their initial safety stocks, reorder levels, and consensus demand forecasts with Smart IP&O.  The team becomes comfortable with Smart’s key performance predictions (KPPs) for service levels, ordering costs, and inventory on hand, all of which are forecasted using the new stocking policies.

But when they save the policies and forecasts to their ERP test system, sometimes the orders being suggested are far larger and more frequent than they expected, driving up projected inventory costs.

When this happens, the primary culprit is how the ERP is configured to treat safety stock.  Being aware of these configuration settings will help planning teams better set expectations and achieve the expected outcomes with less effort (and cause for alarm!).

Here are the three common examples of ERP safety stock configurations:

Configuration 1. Safety Stock is treated as emergency stock that can’t be consumed. If a breach of safety stock is predicted, the ERP system will force an expedite no matter the cost so the inventory on hand never falls below safety stock, even if a scheduled receipt is already on order and scheduled to arrive soon.

Configuration 2. Safety Stock is treated as Buffer stock that is designed to be consumed. The ERP system will place an order when a breach of safety stock is predicted but on hand inventory will be allowed to fall below the safety stock. The buffer stock protects against stockout during the resupply period (i.e., the lead time).

Configuration 3. Safety Stock is ignored by the system and treated as a visual planning aid or rule of thumb. It is ignored by supply planning calculations but used by the planner to help make manual assessments of when to order.

Note: We never recommend using the safety stock field as described in Configuration 3. In most cases, these configurations were not intended but result from years of improvisation that have led to using the ERP in a non-standard way.  Generally, these fields were designed to programmatically influence the replenishment calculations.  So, the focus of our conversation will be on Configurations 1 and 2. 

Forecasting and inventory optimization systems are designed to compute forecasts that will anticipate inventory draw down and then calculate safety stocks sufficient to protect against variability in demand and supply. This means that the safety stock is intended to be used as a protective buffer (Configuration 2) and not as emergency sparse (Configuration 3).  It is also important to understand that, by design, the safety stock will be consumed approximately 50% of the time.

Why 50%? Because actual orders will exceed an unbiased forecast half of the time. See the graphic below illustrating this.  A “good” forecast should yield the value that will come closest to the actual most often so actual demand will either be higher or lower without bias in either direction.

If you configured your ERP system to properly allow consumption of safety stock, then the on hand inventory might look like the graph below.  Note that some safety stock is consumed but avoided a stockout.  The service level you target when computing safety stock will dictate how often you stockout before the replenishment order arrives.  Average inventory is roughly 60 units over the time horizon in this scenario.

If your ERP system is configured to not allow consumption of safety stock and treats the quantity entered in the safety stock field more like emergency spares, then you will have a massive overstock!  Your inventory on hand would look like the graph below with orders being expedited as soon as a breach of safety stock is expected. Average inventory is roughly 90 units, a 50% increase compared to when you allowed safety stock to be consumed.

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.

We often are asked, “Why is the software driving up the inventory?” The answer is that Smart isn’t driving it in either direction – the inputs are driving it, and those inputs are controlled by the users (or admins). Here are four things you can do to get the results you expect.

1. Confirm that your service level targets are commensurate with what you want for that item or group of items. Setting very high targets (95% or more) will likely drive inventory up if you have been coasting along at a lower level and are OK with being there. It’s possible you’ve never achieved the new higher service level but customers have not complained.  Figure out what service level has worked by evaluating historical reports on performance and set your targets accordingly. But keep in mind that competitors may beat you on item availability if you keep using your father’s service level targets.

2. Make sure your understanding of “service level” aligns with the software system’s definition. You may be measuring performance based on how often you ship within one week from receipt of the customer order, whereas the software is targeting reorder points based on your ability to ship right away, not within a week. Clearly the latter will require more inventory to hit the same “service level.” For instance, a 75% same-day service level may correspond to a 90% same-week service level. In this case, you are really comparing apples to oranges. If this is the reason for the excess stock, then determine what “same day” service level is needed to get you to your desired “same week” service level and enter that into the software. Using the less-stringent same-day target will drop the inventory, sometimes very significantly.

3. Evaluate the lead time inputs. We’ve seen instances in which lead times had been inflated to trick old software into producing desired results. Modern software tracks suppliers’ performance by recording their actual lead times over multiple orders, then it takes account of lead time variability in its simulations of daily operations. Watch out if your lead times are fixed at one value that was decided on in the distant past and isn’t current.

4. Check your demand signal. You have lots of historical transactions in your ERP system that can be used in many ways to determine the demand history. If you are using signals such as transfers, or you are not excluding returns, then you may be overstating demand. Spend a little time on defining “demand” in the way that makes most sense for your situation.

Reveal how forecasts are used with these 4 questions.

Often companies will insist that they “don’t use forecasts” to plan inventory.  They often use reorder point methods and are struggling to improve on-time delivery, inventory turns, and other KPIs. While they don’t think of what they are doing as explicitly forecasting, they certainly use estimates of future demand to develop reorder points such as min/max.

Regardless of what it is called, everyone tries to estimate future demand in some way and uses this estimate to set stocking policies and drive orders. To improve inventory planning and make sure you aren’t over/under ordering and creating large stockouts and inventory bloat, it is important to understand exactly how your organization uses forecasts. Once this is understood, you can assess whether the quality of the forecasts can be improved.

Try getting answers to the following questions. It will reveal how forecasts are being used in your business – even if you don’t think you use forecasts.

1.  Is your forecast a period-by-period estimate over time that is used to predict what on-hand inventory will be in the future and triggers order suggestions in your ERP system?

2. Or is your forecast used to derive a reorder point but not explicitly used as a per-period driver to trigger orders? Here, I may predict we’ll sell 10 per week based on the history, but we are not loading 10, 10, 10, 10, etc., into the ERP. Instead, I derive a reorder point or Min that covers the two-period lead time + some amount of buffer to help protect against stock out. In this case, I’ll order more when on hand gets to 25.

3. Is your forecast used as a guide for the planner to help subjectively determine when they should order more?  Here, I predict 10 per week, and I assess the on-hand inventory periodically, review the expected lead time, and I decide, given the 40 units I have on hand today, that I have “enough.” So, I do nothing now but will check back again in a week.

4. Is it used to set up blanket orders with suppliers? Here, I predict 10 per week and agree to a blanket purchase order with the supplier of 520 per year. The orders are then placed in advance to arrive in quantities of 10 once per week until the blanket order is consumed.

Once you get the answers, you can then ask how the estimates of demand are created.  Is it an average? Is it deriving demand over lead time from a sales forecast?  Is there a statistical forecast generated somewhere?  What methods are considered? It will also be important to assess how safety stocks are used to protect against demand and supply variability.  More on all of this in a future article.

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.