Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs.

In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making.

For many industries—especially manufacturing, transportation, utilities, and any sector reliant on complex machinery—spare parts serve as the backbone of maintenance operations. Ineffective management can result in significant downtime when critical parts are unavailable, leading to production halts, service disruptions, and customer dissatisfaction. On the other hand, overstocking items that may not be used promptly ties up working capital, increases storage costs, and can lead to obsolescence.

Given that many spare parts experience intermittent and unpredictable demand, it is essential to have a clear and proactive strategy for managing them. Effective spare parts inventory management ensures operational efficiency, cost savings, and reliability, which can provide a competitive advantage in the marketplace.

Key Strategies for Managing Spare Parts Inventory

1. Forecasting Intermittent Demand. Spare parts often exhibit irregular demand patterns characterized by long periods of zero demand punctuated by sudden spikes when equipment failures occur. Traditional forecasting methods, which rely on consistent historical data trends, may not accurately predict such erratic usage. This can lead to either overstocking or stockouts.

Utilizing specialized forecasting tools like Smart IP&O’s patented intermittent demand forecasting algorithms can provide more accurate predictions. These advanced models analyze historical usage data, equipment failure rates, and maintenance schedules to adjust for demand variability. By incorporating probabilistic forecasting , machine learning, and AI techniques, now we can avoid both shortages that could halt operations and excess inventory that unnecessarily consumes resources.

2. Setting Optimal Safety Stock Levels. Safety stock is essential for mitigating the risk of stockouts, especially for critical spare parts. Safety stock should account for lead time variability, demand fluctuations, and the criticality of the part. Using systems that calculate optimal safety stock levels based on these factors ensures that your parts are available when needed without excessive overstock​. Safety stock settings should be reviewed regularly as part of an ongoing inventory optimization process.

3. Using Min/Max Inventory Policies. A common approach to spare parts inventory is using Min/Max policies, where inventory is replenished up to a maximum level once it drops below a minimum threshold. This system allows for flexibility and ensures that stock levels are maintained without requiring constant monitoring. Adjusting these parameters based on service level goals can ensure you’re not carrying excess inventory while still meeting demand​.

4. Inventory Optimization involves balancing holding costs, stockout costs, and desired service levels to achieve the most cost-effective inventory management strategy. Software solutions like Smart IP&O can simulate various demand and supply scenarios and calculate the optimal inventory policies.

By leveraging advanced AI algorithms and data analytics, Smart IP&O helps organizations determine the right inventory levels for each spare part, considering factors like demand variability, lead times, and cost constraints. This ensures that you maintain the right balance between having sufficient inventory to meet demand and minimizing the costs associated with overstocking. Moreover, optimization tools allow for continuous adjustments based on real-time data and shifting demand patterns, enabling organizations to respond proactively to market or supply chain changes.

5. Regular Review of Supplier Lead Times Supplier performance and lead times can significantly impact your spare parts strategy. Delivery delays can cause stockouts if not accounted for in your planning. Monitoring actual lead times against expected performance helps adjust reorder points and safety stock levels accordingly.  Systems like Smart IP&O provide detailed reporting on supplier performance, including lead time variability, on-time delivery rates, and quality metrics. With access to this information, you can identify potential risks in your supply chain and take proactive measures, such as finding alternative suppliers or adjusting inventory policies, to mitigate the impact of supplier unreliability.

6. Managing Obsolescence. Spare parts often become obsolete when equipment is upgraded or phased out. Holding onto obsolete inventory ties up capital and occupies valuable warehouse space. Regularly reviewing your inventory for items nearing obsolescence can prevent excess stock. Methods such as using cycle stock and safety stock calculations based on demand can help mitigate the risks of holding onto outdated inventory​.

7. Automating Inventory Processes. Automation in inventory management can significantly reduce manual errors, increase efficiency, and ensure timely replenishment of spare parts. Tools like Smart IP&O automate many forecasting, optimization, and replenishment tasks that would otherwise be labor-intensive and prone to human error.

By integrating these tools with existing  ERP systems, organizations can achieve seamless updates and adjustments based on the latest demand and supply data. Automation enables real-time visibility into inventory levels, demand trends, and supply chain disruptions, allowing for quicker decision-making and enhanced responsiveness to changes. Moreover, automation frees up personnel to focus on strategic tasks rather than routine data entry and calculations.

Managing spare parts inventory effectively ensures operational continuity and avoids unnecessary costs. By leveraging advanced forecasting tools, setting optimal safety stock levels, and using smart inventory optimization strategies, companies can minimize stockouts, reduce holding costs, and enhance overall service levels. Continuous improvement and the integration of technology into the inventory management process provide significant long-term benefits for any organization reliant on spare parts. Embracing these best practices not only contributes to operational efficiency but also supports strategic objectives such as cost reduction, customer satisfaction, and competitive advantage. 

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.

Utilities have unique supply chain optimization requirements, primarily ensuring high uptime by keeping all critical machines running continuously. Achieving this involves maintaining a high availability of spare parts to guarantee a consistent, reliable, and safe supply. Additionally, as regulated entities, utilities must also carefully manage and control costs.

Managing supply chains efficiently

To maintain a reliable electricity supply at 99.99%⁺ service levels, for example, utilities must be able to respond quickly to changes in demand in the near term and accurately anticipate future demand. To do so, they must have a well-organized supply chain that allows them to purchase the necessary equipment, materials, and services from the right suppliers at the right time, in the right quantities, and at the right price.

Doing so has become increasingly more challenging in the last 3 years.

• Requirements for safety, reliability, and service delivery are more stringent.
• Supply chain disruptions, unpredictable supplier lead times, intermittent spikes in parts usage have always been problematic, but now they are more the rule than the exception.
• Deregulation in the early 2000’s removed spare parts from the list of directly reimbursed items, forcing utilities to pay for spares directly from revenues[1]
• The constant need for capital combined with aggressively climbing interest rates mean costs are scrutinized more than ever.

As a result, Supply Chain Optimization (SCO) has become an increasingly mission-critical business practice for utilities.  To contend with these challenges, utilities can no longer simply manage their supply chain — they must optimize it.  And to do that, investments in new processes and systems will be required.

[1] Scala et al. “Risk and Spare Parts Inventory in Electric Utilities”. Proceedings of the Industrial Engineering Research Conference.

Inventory Planning and Optimization   

Targeted investments in inventory optimization technology offer a path forward for every utility.  Inventory Optimization solutions should be prioritized because they:

1. Can be implemented in a fraction of the time required for initiatives in other areas, such as warehouse management, supply chain design,  and procurement consolidations. It is not uncommon to start generating benefit after 90 days and to have a full software deployment in less than 180 days.
2. Can generate massive ROI, yielding 20x returns and seven figure financial benefits annually. By better forecasting parts usage, utilities will reduce costs by purchasing only the necessary inventory while controlling the risk of stockouts that lead to downtime and poor service levels.
3. Provide foundational support for other initiatives. A strong supply chain rests on the foundation of solid usage forecasts and inventory purchasing plans.

Using predictive analytics and advanced algorithms, inventory optimization helps utilities maximize service levels and reduce operational costs by optimizing inventory levels for spare parts. For example, an electric utility might use statistical forecasting to predict future parts usage, conduct inventory audits to identify excess inventory, and leverage analytical results to identify where inventory optimization efforts should focus first. By doing this, the utility can ensure that machines are running at optimal levels and reduce the risk of costly delays due to a lack of spares.

By using analytics and data, you can identify which spare parts and equipment are most likely to be needed and order only the necessary items. This helps to ensure that equipment has high up-time. It rewards regular monitoring and adjusting of inventory levels so that when operating conditions change, you can detect the change and adjust accordingly. This implies that planning cycles must operate at a tempo high enough to keep up with changing conditions. Leveraging probabilistic forecasting to recalibrate spares stocking policies for each planning cycle ensures that stocking policies (such as min/max levels) are always up-to-date and reflect the latest parts usage and supplier lead times.

Service Levels and the Tradeoff Curve

The Service Level Tradeoff Curve relates inventory investment to item availability as measured by service level. Service level is the probability that no shortages occur between when you order more stock and when it arrives on the shelf. Surprisingly few companies have data on this important metric across their whole fleet of spare parts.

The Service Level Tradeoff Curve exposes the link between the costs associated with different levels of service and the inventory requirements needed to achieve them.  Knowing which components are important to maintaining high service levels is key to the optimization process and is determined by several factors, including inventory item standardization, criticality, historical usage, and known future repair orders. By understanding this relationship, utilities can better allocate resources, as when using the curves to identify areas where costs can be reduced without hurting system reliability.

With inventory optimization software, setting stocking policies is pure guesswork: It is possible to know how any given increase or decrease will impact service levels other than rough cut estimates.  How the changes will play out in terms of inventory investment, operating costs, and shortage costs, is something no one really knows.  Most utilities rely on rule of thumb methods and arbitrarily adjust stocking policies in a reactive manner after something has gone wrong such as a large stockout or inventory write off.  When adjustments are made this way, there is no fact-based analysis detailing how this change is expected to impact the metrics that matter:  service levels and inventory values.

Inventory Optimization software can compute the detailed, quantitative tradeoff curves required to make informed inventory policy choices or even recommend the target service level that results in the lowest overall operating cost (the sum of holding, ordering, and stock-out costs).  Using this analysis, large increases in stock levels may be mathematically justified when the predicted reduction in shortage costs exceeds the increase in inventory investment and associated holding costs.  By setting appropriate service levels and recalibrating policies across all active parts once every planning cycle (at least once monthly), utilities can minimize the risk of outages while controlling expenditures.

Perhaps the most critical aspects of the response to equipment breakdown are those relating to achieving a first-time fix as rapidly as possible. Having the proper spares available can be the difference between completing a single trip and increasing the mean time to repair, bearing the costs associated with several visits, and causing customer relationships to degrade.

Using modern software, you can benchmark past performance and leverage probabilistic forecasting methods to simulate future performance. By stress-testing your current inventory stocking policies against all plausible scenarios of future parts usage, you will know ahead of time how current and proposed stocking policies are likely to perform. Check out our blog post on how to measure the accuracy of your service level forecast to help you assess the accuracy of inventory recommendations that software providers will purport to provide benefit.

Leveraging Advanced Analytics and AI

When introducing automation, each utility company has its own goals to pursue, but you should begin with assessing present operations to identify areas that may be made more effective. Some companies may prioritize financial issues, but others may prioritize regulatory demands such as clean energy spending or industry-wide changes such as smart grids. Each company’s difficulties are unique, but modern software can point the way to a more effective inventory management system that minimizes excess inventory and places the correct components in the right places at the right times.

Overall, Supply Chain Optimization initiatives are essential for utilities looking to maximize their efficiency and reduce their costs. Technology allows us to make the integration process seamless, and you don’t need to replace your current ERP or EAM system by doing it.  You just need to make better use of the data you already have.

For example, one large utility launched a strategic Supply Chain Optimization (SCO) initiative and added best-in-class capabilities through the selection and integration of commercial off-the-shelf applications.  Chief among these was the Smart Inventory Planning and Optimization system (Smart IP&O), comprising Parts Forecasting / Demand Planning and Inventory Optimization functionality. Within just 90 days the software system was up and running, soon reducing inventory by $9,000,000 while maintaining spares availability at a high level. You can read the case study here Electric Utility Goes with Smart IP&O.

Utilities can ensure that they are able to manage their spare parts supplies in an efficient and cost-effective manner better preparing them for the future.  Over time, this balance between supply and demand translates to a significant edge. Understanding the Service Level Tradeoff Curve helps to understand the costs associated with different levels of service and the inventory requirements needed to achieve them. This leads to reduced operational costs, optimized inventory, and assurance that you can meet your customers’ needs.

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.

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.