I can’t imagine being an inventory planner in spare parts, distribution, or manufacturing and having to create safety stock levels, reorder points, and order suggestions without using key performance predictions of service levels, fill rates, and inventory costs:

Smart’s Inventory Optimization solution generates out-of-the-box key performance predictions that dynamically simulate how your current stocking policies will perform against possible future demands.  It reports on how often you’ll stock out, the size of the stockouts, the value of your inventory, holding costs, and more.  It lets you proactively identify problems before they occur so you can take corrective action in the short term. You can create what-if scenarios by setting targeted service levels and modifying lead times so you an see the predicted impact of these changes before committing to it.

For example,

• You can see if a proposed move from the current service level of 90% to a targeted service level of 97% is financially advantageous
• You can automatically identify if a different service level target is even more profitable to your business that the proposed target.
• You can see exactly how much you’ll need to increase your reorder points to accommodate a longer lead time.

If you aren’t equipping planners with the right tools, they’ll be forced to set stocking policies, safety stock levels, and create demand forecasts in Excel or with outdated ERP functionality.   Not knowing how policies are predicted to perform will leave your company ill equipped to properly allocate inventory.  Contact us today to learn how we can help!

What’s different about inventory planning for Maintenance, Repair, and Operations (MRO) compared to inventory planning in manufacturing and distribution environments? In short, it’s the nature of the demand patterns combined with the lack of actionable business knowledge.

Demand Patterns

Manufacturers and distributors tend to focus on the top sellers that generate the majority of their revenue. These items typically have high demand that is relatively easy to forecast with traditional time series models that capitalize on predictable trend and/or seasonality.  In contrast, MRO planners almost always deal with intermittent demand, which is more sparse, more random, and harder to forecast.  Furthermore, the fundamental quantities of interest are different. MRO planners ultimately care most about the “when” question:  When will something break? Whereas the others focus on the “how much” question of units sold.

Business Knowledge

Manufacturing and distribution planners can often count on gathering customer and sales feedback, which can be combined with statistical methods to improve forecast accuracy. On the other hand, bearings, gears, consumable parts, and repairable parts are rarely willing to share their opinions. With MRO, business knowledge about which parts will be needed and when just isn’t reliable (excepting planned maintenance when higher-volume consumable parts are replaced). So, MRO inventory planning success goes only as far as their probability models’ ability to predict future usage takes them. And since demand is so intermittent, they can’t get past Go with traditional approaches.

Methods for MRO

In practice, it is common for MRO and asset-intensive businesses to manage inventories by resorting to static Min/Max levels based on subjective multiples of average usage, supplemented by occasional manual overrides based on gut feel. The process becomes a bad mixture of static and reactive, with the result that a lot of time and money is wasted on expediting.

There are alternative planning methods based more on math and data, though this style of planning is less common in MRO than in the other domains. There are two leading approaches to modeling part and machine breakdown: models based on reliability theory and “condition-based maintenance” models based on real-time monitoring.

Reliability Models

Reliability models are the simpler of the two and require less data. They assume that all items of the same type, say a certain spare part, are statistically equivalent. Their key component is a “hazard function”, which describes the risk of failure in the next little interval of time. The hazard function can be translated into something better suited for decision making: the “survival function”, which is the probability that the item is still working after X amount of use (where X might be expressed in days, months, miles, uses, etc.). Figure 1 shows a constant hazard function and its corresponding survival function.

Figure 1: Constant hazard function and its survival function

A hazard function that doesn’t change implies that only random accidents will cause a failure. In contrast, a hazard function that increases over time implies that the item is wearing out. And a decreasing hazard function implies that an item is settling in. Figure 2 shows an increasing hazard function and its corresponding survival function.

Figure 2: Increasing hazard function and its survival function

Reliability models are often used for inexpensive parts, such as mechanical fasteners, whose replacement may be neither difficult nor expensive (but still might be essential).

Condition-Based Maintenance

Models based on real-time monitoring are used to support condition-based maintenance (CBM) for expensive items like jet engines. These models use data from sensors embedded in the items themselves. Such data are usually complex and proprietary, as are the probability models supported by the data. The payoff from real-time monitoring is that you can see trouble coming, i.e., the deterioration is made visible, and forecasts can predict when the item will hit its red line and therefore need to be taken off the field of play. This allows individualized, pro-active maintenance or replacement of the item.

Figure 3 illustrates the kind of data used in CBM. Each time the system is used, there is a contribution to its cumulative wear and tear. (However, note that sometimes use can improve the condition of the unit, as when rain helps keep a piece of machinery cool). You can see the general trend upward toward a red line after which the unit will require maintenance. You can extrapolate the cumulative wear to estimate when it will hit the red line and plan accordingly.

Figure 3: Illustrating real-time monitoring for condition-based maintenance

To my knowledge, nobody makes such models of their finished goods customers to predict when and how much they will next order, perhaps because the customers would object to wearing brain monitors all the time. But CBM, with its complex monitoring and modeling, is gaining in popularity for can’t-fail systems like jet engines. Meanwhile, classical reliability models still have a lot of value for managing large fleets of cheaper but still essential items.

Smart’s approach
The above condition-based maintenance and reliability approaches require an excessive data collection and cleansing burden that many MRO companies are unable to manage. For those companies, Smart offers an approach that does not require development of reliability models. Instead, it exploits usage data in a different way. It leverages probability-based models of both usage and supplier lead times to simulate thousands of possible scenarios for replenishment lead times and demand.  The result is an accurate distribution of demand and lead times for each consumable part that can be exploited to determine optimal stocking parameters.   Figure 4 shows a simulation that begins with a scenario for spare part demand (upper plot) then produces a scenario of on-hand supply for particular choices of Min/Max values (lower line). Key Performance Indicators (KPIs) can be estimated by averaging the results of many such simulations.

Figure 4: An example of a simulation of spare part demand and on-hand inventory

You can read about Smart’s approach to forecasting spare parts here: https://smartcorp.com/wp-content/uploads/2019/10/Probabilistic-Forecasting-for-Intermittent-Demand.pdf

Inventory Control concerns the management of physical goods, focusing on an accurate and up-to-the-minute count of every item in inventory and where it is located, as well as efficient retrieval of items. Relevant technologies include computer databases, barcoding, Radio Frequency Identification (RFID), and the use of robots for retrieval.

Inventory Management aims to execute the inventory policy defined by the company. Inventory Management is often accomplished using Enterprise Resource Planning (ERP) systems, which generate purchase orders, production orders, and reporting that details current inventory on hand, incoming, and up for order.

Inventory Planning sets operational policy details, such as item-specific reorder points and order quantities, and predicts future demand and supplier lead times. Important components of an inventory planning process include what-if scenarios for netting out on-hand inventory, analyzing how changes to demand, lead times, and stocking policies will impact ordering, as well as managing exceptions and contingencies.

Inventory Optimization utilizes an analytical process that computes values for inventory planning parameters (e.g., reorder points and order quantities) that optimize a numerical goal or “objective function” without violating a numerical constraint. For instance, an objective function might be to achieve the lowest possible inventory operating cost (defined as the sum of inventory holding costs, ordering costs, and shortage costs), and the constraint might be to achieve a fill rate of at least 90%. Using a mathematical model of the inventory system and probability forecasts of item demand, inventory optimization can quickly and automatically suggest how to best manage thousands of inventory items.

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.

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.