The aftermarket sector provides OEMs with a decisive advantage by offering a steady revenue stream and fostering customer loyalty through the reliable and timely delivery of service parts. However, managing inventory and forecasting demand in the aftermarket is fraught with challenges, including unpredictable demand patterns, vast product ranges, and the necessity for quick turnarounds.  Traditional methods often fall short due to the complexity and variability of demand in the aftermarket. The latest technologies can analyze large datasets to predict future demand more accurately and optimize inventory levels, leading to better service and lower costs.

This blog explores how the latest AI-driven technologies can transform the OEM aftermarket by analyzing large datasets to predict future demand more accurately, optimize inventory levels, enhance forecasting accuracy, and improve customer satisfaction, ultimately leading to better service and lower costs.

Enhancing Forecast Accuracy with AI  

Using state-of-the-art technology, organizations can significantly enhance forecast accuracy by analyzing historical data, recognizing patterns, and predicting future demand. Our latest (IP&O) Inventory Planning &Optimization technology uses AI to provide real-time insights and automate decision-making processes. It employs adaptive forecasting techniques to ensure forecasts remain relevant as market conditions change. The system integrates advanced algorithms to manage intermittent data and make real-time modifications while handling complex calculations and considering factors like lead times, forecast errors, seasonality, and market trends. By leveraging better data inputs and advanced analytics, companies can significantly reduce forecast errors and minimize the costs associated with overstocking and stockouts.  Our IP&O platform is designed to handle the complexities and challenges unique to service parts management, such as intermittent demand and large assortments of parts.

Repair and Return Module: The platform accurately simulates the processes of part breakdown and repair. It predicts downtime, service levels, and inventory costs associated with the current rotating spare parts pool. Planners will know how many spares to stock to achieve short- and long-term service level requirements and, in operational settings, whether to wait for repairs to be completed and returned to service or to purchase additional service spares from suppliers, avoiding unnecessary buying and equipment downtime.

 Intermittent Demand Forecasting: IP&O’s patented intermittent demand forecasting technology provides highly accurate forecasts for items with sporadic demand patterns typical in the aftermarket. This capability is crucial for optimizing inventory levels and ensuring that critical parts are available when needed without overstocking.

Real-Time Inventory Optimization: Our technology dynamically adjusts inventory policies to align with changing demand patterns and market conditions. It calculates optimal reorder points and order quantities, balancing service levels with inventory costs. This ensures that OEMs can maintain high service levels while minimizing excess inventory and related carrying costs.

Scenario Planning and What-If Analysis: IP&O allows users to create multiple inventory scenarios to evaluate the impact of different inventory policies on service levels and costs. This capability helps OEMs make informed decisions about stocking strategies and respond proactively to market changes or supply chain disruptions.

Seamless ERP Integration: The platform offers seamless integration with leading ERP systems, such as Epicor and NetSuite, enabling automatic synchronization of forecasts and inventory data. This integration facilitates efficient execution of replenishment orders and ensures that inventory levels are continually aligned with the latest demand forecasts.

Forecast Accuracy and Reporting:  Our Advanced System provides detailed reporting and dashboards that track forecast accuracy, inventory performance, and supplier reliability. By analyzing these metrics, OEMs can continually refine their forecasting models and improve overall supply chain performance.

Real-world examples illustrate the substantial impact of AI-driven Forecasting and Inventory Optimization in the OEM aftermarket.  Prevost Parts, a division of a leading Canadian manufacturer of intercity buses and coach shells, used IP&O to address the intermittent demand of over 25,000 active parts. By integrating accurate sales forecasts and safety stock requirements into their ERP system, supported by AI and real-time machine learning adjustments, they reduced backorders by 65%, lost sales by 59%, and increased fill rates from 93% to 96% in just three months. This transformation significantly improved their inventory allocation, reducing transportation and inventory costs​​.

Incorporating AI and ML into IP&O processes is not just a technological upgrade but a strategic move that can transform the OEM aftermarket. IP&O  technology ensures better service quality and customer satisfaction by improving forecast accuracy, optimizing inventory levels, and reducing costs. As the aftermarket sector continues to grow and evolve, embracing AI will be key to staying competitive and meeting customer expectations efficiently.

MRO organizations exist in a wide range of  industries, including public transit, electrical utilities, wastewater, hydro power, aviation, and mining. To get their work done, MRO professionals use Enterprise Asset Management (EAM) and Enterprise Resource Planning (ERP) systems. These systems are designed to do a lot of jobs. Given their features, cost, and extensive implementation requirements, there is an assumption that EAM and ERP systems can do it all.

For example, at a recent Maximo Utilities Working Group event, several prospects stated that “Our EAM will do that” when asked about requirements for forecasting usage, netting out supply plans, and optimizing inventory policies. They were surprised to learn it did not and wanted to know more.

In this post, we summarize the need for add-on software that addresses specialized analytics for inventory optimization, forecasting, and service parts planning.   

EAM Systems

EAM systems can’t ingest forecasts of future usage – these systems simply aren’t designed to conduct supply planning and many don’t even have a place to hold forecasts. So, when an MRO business needs to net out known requirements for planned production or capital projects, an add-on application like Smart IP&O is needed.

Inventory Optimization software with features that support planning known future demand will take project-based data not maintained in the EAM system (including project start dates, duration, and when each part is expected to be needed) and compute a period-by-period forecast over any planning horizon. That “planned” forecast can be projected alongside statistical forecasts of “unplanned” demand arising from normal wear and tear. At that point, parts planning software can net out the supply and identify gaps between supply and demand. This ensures that these gaps won’t go unnoticed and result in shortages that would otherwise delay the completion of the projects. It also minimizes excess stock that would otherwise be ordered too soon and needlessly consumes cash and warehouse space. Again, MRO businesses sometimes mistakenly assume that these capabilities are addressed by their EAM package.

ERP Systems

ERP systems, on the other hand, typically do include an MRP module that is designed to ingest a forecast and net out material requirements. Processing will consider current on hand inventory, open sales orders, scheduled jobs, incoming purchase orders, any bill of materials, and items in transit while transferring between sites. It will compare those current state values to the replenishment policy fields plus any monthly or weekly forecasts to determine when to suggest replenishment (a date) and how much to replenish (a quantity).

So, why not use the ERP system alone to net out the supply plan to prevent shortages and excess? First, while ERP systems have a placeholder for a forecast and some systems can net out supply using their MRP modules, they don’t make it easy to reconcile planned demand requirements associated with capital projects. Most of the time, the data on when planned projects will occur is maintained outside of the ERP, especially the project’s bill of materials detailing what parts will be needed to support the project. Second, many ERP systems don’t offer anything effective when it comes to predictive capabilities, relying instead on simple math that just won’t work for service parts due to the high prevalence of intermittent demand. Finally, ERP systems don’t have flexible user-friendly interfaces that support interacting with the forecasts and supply plan.

Reorder Point Logic

Both ERP and EAM have placeholders for reorder point replenishment methods such as Min/Max levels. You can use inventory optimization software to populate these fields with the risk-adjusted reorder point policies. Then within the ERP or EAM systems, orders are triggered whenever actual (not forecasted) demand drives on-hand stock below the Min. This type of policy doesn’t use a traditional forecast that projects demand week-over-week or month-over-month and is often referred to as “demand driven replenishment” (since orders only occur when actual demand drives stock below a user defined threshold).

But just because it isn’t using a period-over-period forecast doesn’t mean it isn’t being predictive. Reorder point policies should be based on a prediction of demand over a replenishment lead time plus a buffer to protect against demand and supply variability. MRO businesses need to know the stockout risk they are incurring with any given stocking policy. After all, inventory management is risk management – especially in MRO businesses when the cost of stockout is so high. Yet, ERP and EAM do not offer any capabilities to risk-adjust stocking policies. They force users to manually generate these policies externally or to use basic rule of thumb math that doesn’t detail the risks associated with the choice of policy.

Summary

Supply chain planning functionality such as inventory optimization isn’t the core focus of EAM  and ERP. You should leverage add-on planning platforms, like Smart IP&O, that support statistical forecasting, planned project management, and inventory optimization. Smart IP&O will develop forecasts and stocking policies that can be input to an EAM or ERP system to drive daily ordering.

Do MRO companies genuinely prioritize reducing excess spare parts inventory? From an organizational standpoint, our experience suggests not necessarily. Boardroom discussions typically revolve around expanding fleets, acquiring new customers, meeting service level agreements (SLAs), modernizing infrastructure, and maximizing uptime. In industries where assets supported by spare parts cost hundreds of millions or generate significant revenue (e.g., mining or oil & gas), the value of the inventory just doesn’t raise any eyebrows, and organizations tend to overlook massive amounts of excessive inventory.

Consider a public transit agency.  In most major cities, the annual operating budgets will exceed $3 billion.  Capital expenses for trains, subway cars, and infrastructure may reach hundreds of millions annually. Consequently, a spare parts inventory valued at $150 million might not grab the attention of the CFO or general manager, as it represents a small percentage of the balance sheet.  Moreover, in MRO-based industries, many parts need to support equipment fleets for a decade or more, making additional stock a necessary asset. In some sectors like utilities, holding extra stock can even be incentivized to ensure that equipment is kept in a state of good repair.

We have seen concerns about excess stock arise when warehouse space is limited. I recall, early in my career, witnessing a public transit agency’s rail yard filled with rusted axles valued at over $100,000 each.  I was told the axles were forced to be exposed to the elements due to insufficient warehouse space. The opportunity cost associated with the space consumed by extra stock becomes a consideration when warehouse capacity is exhausted. The primary consideration that trumps all other decisions is how the stock ensures high service levels for internal and external customers.  Inventory planners worry far more about blowback from stockouts than they do from overbuying.  When a missing part leads to an SLA breach or downed production line, resulting in millions in penalties and unrecoverable production output, it is understandable.

Asset-intensive companies are missing one giant point. That is, the extra stock doesn’t insulate against stockouts; it contributes to them. The more excess you have, the lower your overall service level because the cash needed to purchase parts is finite, and cash spent on excess stock means there isn’t cash available for the parts that need it.  Even publicly funded MRO businesses, like utilities and transit agencies, acknowledge the need to optimize spending, now more than ever.  As one materials manager shared, “We can no longer fix problems with bags of cash from Washington.”  So, they must do more with less, ensuring optimal allocation across the tens of thousands of parts they manage.

This is where state-of-the-art inventory optimization software comes in, predicting the required inventory for targeted service levels, identifying when stock levels yield negative returns, and recommending reallocations for improved overall service levels.  Smart Software has helped asset intensive MRO based businesses optimize reorder levels across each part for decades. Give us a call to learn more. 

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

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.