Forecast-based inventory management, or MRP (Material Requirements Planning) logic, is a forward-planning methodology for managing inventory. This method ensures that businesses can meet demand without overstocking, which ties up capital, or understocking, which can lead to lost sales and dissatisfied customers.

By anticipating demand and adjusting inventory levels accordingly, this approach helps maintain the right balance between having enough stock to meet customer needs and minimizing excess inventory costs. Businesses can optimize operations, reduce waste, and improve customer satisfaction by predicting future needs. Let’s break down how this works.

 

Core Concepts of Forecast-Based Inventory Management

Inventory Dynamics Models: Inventory dynamics models are fundamental to understanding and managing inventory levels. The simplest model, known as the “sawtooth” model, demonstrates inventory levels decreasing with demand and replenishing just in time. However, real-world scenarios often require more sophisticated models. By incorporating stochastic elements and variability, such as Monte Carlo simulations, businesses can account for random fluctuations in demand and lead time, providing a more realistic forecast of inventory levels.

IP&O platform enhances inventory dynamics modeling through advanced data analytics and simulation capabilities. By leveraging AI and machine learning algorithms, our IP&O platform can predict demand patterns more accurately, adjusting models in real time based on the latest data. This leads to more precise inventory levels, reducing the risk of stockouts and overstocking.

Determining Order Quantity and Timing: Effective inventory management requires knowing when and how much to order. This involves forecasting future demand and calculating the lead time for replenishing stock. By predicting when inventory will hit safety stock levels, businesses can plan their orders to ensure continuous supply.

Our latest tools excel at optimizing order quantities and timing by utilizing predictive analytics and AI. These systems can analyze vast amounts of data, including historical sales and market trends. By doing so, they provide more accurate demand forecasts and optimize reorder points, ensuring inventory is replenished just in time without excess.

Calculating Lead Time: Lead time is the period from placing an order to receiving the stock. It varies based on the availability of components. For example, if a product is assembled from multiple components, the lead time will be determined by the component with the longest lead time.

Smart AI-driven solutions enhance lead time calculation by integrating with supply chain management systems. These systems track supplier performance, and historical lead times, to provide more accurate lead time estimates. Additionally, smart technologies can alert businesses to potential delays, allowing for proactive adjustments to inventory plans.

Safety Stock Calculation: Safety stock acts as a buffer to protect against variability in demand and supply. Calculating safety stock involves analyzing demand variability and setting a stock level that covers most potential scenarios, thus minimizing the risk of stockouts.

IP&O technology significantly improves safety stock calculation through advanced analytics. By continuously monitoring demand patterns and supply chain variables, smart systems can dynamically adjust safety stock levels. Machine learning algorithms can predict demand spikes or drops and adjust safety stock accordingly, ensuring optimal inventory levels while minimizing holding costs.

The Importance of Accurate Forecasting in Inventory Management

Accurate forecasting is key for minimizing forecast errors, which can lead to excess inventory or stockouts. Techniques such as utilizing historical data, enhancing data inputs, and applying advanced forecasting methods help achieve better accuracy. Forecast errors can have significant financial implications: over-forecasting results in excess inventory while under-forecasting leads to missed sales opportunities. Managing these errors through systematic tracking and adjusting forecasting methods is crucial for maintaining optimal inventory levels.

Safety stock ensures that businesses meet customer needs even if actual demand deviates from the forecast. This cushion protects against unforeseen demand spikes or delays in replenishment. Accurate forecasting, effective error management, and strategic use of safety stock enhance forecast-based inventory management. Companies can understand inventory dynamics, determine the right order quantities and timing, calculate accurate lead times, and set appropriate safety stock levels.

Using state-of-the-art technology like IP&O provides significant advantages by offering real-time data insights, predictive analytics, and adaptive models. This leads to more efficient inventory management, reduced costs, and improved customer satisfaction. Overall, IP&O empowers businesses to plan better and respond swiftly to market changes, ensuring they maintain the right inventory balance to meet customer needs without incurring unnecessary costs.

 

 

In this blog, we will explore how organizations can achieve exceptional efficiency and accuracy with AI-driven inventory optimization. Traditional inventory management methods often fall short due to their reactive nature and reliance on manual processes. Maintaining optimal inventory levels is fundamental for meeting customer demand while minimizing costs. The introduction of AI-driven inventory optimization can significantly reduce the burden of manual processes, providing relief to supply chain managers from tedious tasks. With AI, we can predict demand more accurately, reduce excess stock, avoid stockouts, and ultimately improve our organization’s bottom line. Let’s explore how this approach not only boosts sales and operational efficiency but also elevates customer satisfaction by ensuring products are always available when needed.

 

Insights for Improved Decision-Making in Inventory Management

1. Enhanced Forecast Accuracy Advanced Machine Learning algorithms analyze historical data to identify patterns that humans might miss. Techniques like clustering, regime change detection, anomaly detection, and regression analysis provide deep insights into data. Measuring forecast error is essential for refining forecast models; for example, techniques like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) help quantify the accuracy of forecasts. Businesses can improve accuracy by continuously monitoring and adjusting forecasts based on these error metrics. As the Demand Planner at a Hardware Retailer stated, “With the improvements to our forecasts and inventory planning that Smart Software enabled, we have been able to reduce safety stock by 20% while also reducing stock-outs by 35%.”

2. Real-Time Data Analysis State-of-the-art systems can process vast amounts of data in real time, allowing businesses to adjust their inventory levels dynamically based on current demand trends and market conditions. Anomaly detection algorithms can automatically identify and correct sudden spikes or drops in demand, ensuring that the forecasts remain accurate. A notable success story comes from Smart IP&O, which enabled one company to reduce inventory by 20% while maintaining service levels by continuously analyzing real-time data and adjusting forecasts accordingly. FedEx Tech’s Manager of Materials highlighted, “Whatever the request, we need to meet our next-day service commitment – Smart enables us to risk adjust our inventory to be sure we have the products and parts on hand to achieve the service levels our customers require.”

3. Improved Supply Chain Efficiency Intelligent technology platforms can optimize the entire supply chain, from procurement to distribution, by predicting lead times and optimizing order quantities. This reduces the risk of overstocking and understocking. For instance, using forecast-based inventory management, Smart Software helped a manufacturer streamline its supply chain, reducing lead times by 15% and enhancing overall efficiency. The VP of Operations at Procon Pump stated, “One of the things I like about this new tool… is that I can evaluate the consequences of inventory stocking decisions before I implement them.”

4. Enhanced Decision-Making AI provides actionable insights and recommendations, enabling managers to make informed decisions. This includes identifying slow-moving items, forecasting future demand, and optimizing stock levels. Regression analysis, for example, can relate sales to external variables like seasonality or economic indicators, providing a deeper understanding of demand drivers. One of Smart Software’s clients reported a significant improvement in decision-making processes, resulting in a 30% increase in service levels while reducing excess inventory by 15%. “Smart IP&O enabled us to model demand at each stocking location and, using service level-driven planning, determine how much to stock to achieve the service level we require,” noted the Purchasing Manager at Seneca Companies.

5. Cost Reduction By optimizing inventory levels, businesses can reduce holding costs and minimize losses from obsolete or expired products. AI-driven systems also reduce the need for manual inventory checks, saving time and labor costs. A recent case study shows how implementing Inventory Planning & Optimization (IP&O) was accomplished within 90 days of project start. Over the ensuing six months, IP&O enabled the adjustment of stocking parameters for several thousand items, resulting in inventory reductions of $9.0 million while sustaining target service levels.

 

By leveraging advanced algorithms and real-time data analysis, businesses can maintain optimal inventory levels and enhance their overall supply chain performance. Inventory Planning & Optimization (IP&O) is a powerful tool that can help your organization achieve these goals. Incorporating state-of-the-art inventory optimization into your organization can lead to significant improvements in efficiency, cost reduction, and customer satisfaction.

 

 

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.

 

 

 

Companies that depend on spreadsheets for demand planning, forecasting, and inventory management are often constrained by the spreadsheet’s inherent limitations. This post examines the drawbacks of traditional inventory management approaches caused by spreadsheets and their associated costs, contrasting these with the significant benefits gained from embracing state-of-the-art planning technologies.

Spreadsheets, while flexible for their infinite customizability, are fundamentally manual in nature requiring significant data management, human input, and oversight. This increases the risk of errors, from simple data entry mistakes to complex formula errors, that cause cascading effects that adversely impact forecasts.  Additionally, despite advances in collaborative features that enable multiple users to interact with a common sheet, spreadsheet-based processes are often siloed. The holder of the spreadsheet holds the data.  When this happens, many sources of data truth begin to emerge.  Without the trust of an agreed-upon, pristine, and automatically updated source of data, organizations don’t have the necessary foundation from which predictive modeling, forecasting, and analytics can be built.

In contrast, advanced planning systems like Smart IP&O are designed to overcome these limitations. Such systems are built to automatically ingest data via API or files from ERP and EAM systems, transform that data using built in ETL tools, and can process large volumes of data efficiently.  This enables businesses to manage complex inventory and forecasting tasks with greater accuracy and less manual effort because the data collection, aggregation, and transformation is already done. Transitioning to advanced planning systems is key for optimizing resources for several reasons.

Spreadsheets also have a scaling problem. The bigger the business grows, the greater the number of spreadsheets, workbooks, and formulas becomes.  The result is a tightly wound and rigid set of interdependencies that become unwieldy and inefficient.  Users will struggle to handle the increased load and complexity with slow processing times and an inability to manage large datasets and face challenges collaborating across teams and departments.

On the other hand, advanced planning systems for inventory optimization, demand planning, and inventory management are scalable, designed to grow with the business and adapt to its changing needs. This scalability ensures that companies can continue to manage their inventory and forecasting effectively, regardless of the size or complexity of their operations. By transitioning to systems like Smart IP&O, companies can not only improve the accuracy of their inventory management and forecasting but also gain a competitive edge in the market by being more responsive to changes in demand and more efficient in their operations.

Benefits of Jumping in: An electric utility company struggled to maintain service parts availability without overstocking for over 250,000-part numbers across a diverse network of power generation and distribution facilities. It replaced their twenty-year-old legacy planning process that made heavy use of spreadsheets with Smart IP&O and a real-time integration to their EAM system.  Before Smart, they were only able to modify Min/Max and Safety Stock levels infrequently.  When they did, it was nearly always because a problem occurred that triggered the review.  The methods used to change the stocking parameters relied heavily on gut feel and averages of the historical usage.   The Utility leveraged Smart’s what-if scenarios to create digital twins of alternate stocking policies and simulated how each scenario would perform across key performance indicators such as inventory value, service levels, fill rates, and shortage costs.  The software pinpointed targeted Min/Max increases and decreases that were deployed to their EAM system, driving optimal replenishments of their spare parts.  The result:  A significant inventory reduction of $9 million that freed up cash and valuable warehouse space while sustaining 99%+ target service levels.

Managing Forecast Accuracy: Forecast error is an inevitable part of inventory management, but most businesses don’t track it.  As Peter Drucker said, “You can’t improve what you don’t measure.”  A global high-tech manufacturing company utilizing a spreadsheet-based forecast process had to manually create its baseline forecasts and forecast accuracy reporting.  Given the planners’ workload and siloed processes, they just didn’t update their reports very often, and when they did, the results had to be manually distributed.  The business didn’t have a way of knowing just how accurate a given forecast was and couldn’t cite their actual errors by group of part with any confidence.  They also didn’t know whether their forecasts were outperforming a control method.  After Smart IP&O went live, the Demand Planning module automated this for them. Smart Demand Planner now automatically reforecasts their demand each planning cycle utilizing ML methods and saves accuracy reports for every part x location.  Any overrides that are applied to the forecasts can now be auto-compared to the baseline to measure forecast value add – i.e., whether the additional effort to make those changes improved the accuracy.  Now that the ability to automate the baseline statistical forecasting and produce accuracy reports is in place, this business has solid footing from which to improve their forecast process and resulting forecast accuracy.

Get it Right and Keep it Right:  Another customer in the aftermarket parts business has used Smart’s forecasting solutions since 2005 – nearly 20 years!  They were faced with challenges forecasting intermittently demanded parts sold to support their auto aftermarket business. By replacing their spreadsheet-based approach and manual uploads to SAP with statistical forecasts of demand and safety stock from SmartForecasts, they were able to significantly reduce backorders and lost sales, with fill rates improving from 93% to 96% within just three months.  The key to their success was leveraging Smart’s patented method for forecasting intermittent demand – The “Smart-Willemain” bootstrap method generated accurate estimates of the cumulative demand over the lead time that helped ensure better visibility of the possible demands.

Connecting Forecasts to the Inventory Plan: Advanced planning systems support forecast-based inventory management, which is a proactive approach that relies on demand forecasts and simulations to predict possible outcomes and their associated probabilities.  This data is used to determine optimal inventory levels.  Scenario-based or probabilistic forecasting contrasts with the more reactive nature of spreadsheet-based methods. A longtime customer in the fabric business, previously dealt with overstocks and stockouts due to intermittent demand for thousands of SKUs. They had no way of knowing what their stock-out risks were and so couldn’t proactively modify policies to mitigate risk other than making very rough-cut assumptions that tended to overstock grossly.  They adopted Smart Software’s demand and inventory planning software to generate simulations of demand that identified optimal Minimum On-Hand values and order quantities, maintaining product availability for immediate shipping, highlighting the advantages of a forecast-based inventory management approach.

Better Collaboration:  Sharing forecasts with key suppliers helps to ensure supply.  Kratos Space, part of Kratos Defense & Security Solutions, Inc., leveraged Smart forecasts to provide their Contract Manufacturers with better insights on future demand.  They used the forecasts to make commitments on future buys that enabled the CM to reduce material costs and lead times for engineered-to-order systems. This collaboration demonstrates how advanced forecasting techniques can lead to significant supply chain collaboration that yields efficiencies and cost savings for both parties.

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​​In a highly configurable manufacturing environment, forecasting finished goods can become a complex and daunting task. The number of possible finished products skyrockets when many components are interchangeable. A traditional MRP would force us to forecast every single finished product, which can be unrealistic or even impossible. Several leading solutions introduce the concept of the “Planning BOM,” which allows the use of forecasts at a higher level in the manufacturing process. In this article, we will discuss this functionality in Epicor Kinetic and how you can take advantage of it with Epicor Smart Inventory Planning and Optimization (Smart IP&O) to get ahead of your demand in the face of this complexity.

Why Would I Need a Planning BOM?

Traditionally, each finished product or SKU would have a rigidly defined bill of materials. If we stock that product and want to plan around forecasted demand, we will forecast demand for those products and then feed MRP to blow this forecasted demand from the finished good level down to its components via the BOM.

Many companies, however, offer highly configurable products where customers can select options on the product they buy. As an example, recall the last time you bought a cellphone. You chose a brand and model, but from there, you were likely presented with options: what screen size do you want? How much storage do you want? What color do you prefer? If that business wants to have these cellphones ready and available to ship to you in a reasonable time, suddenly, they are no longer just anticipating demand for that model—they must forecast that model for every type of screen size, for all storage capacities, for all colors, and all possible combinations of those as well! For some manufacturers, these configurations can compound to hundreds or thousands of possible finished good permutations.

There may be so many possible customizations that the demand at the finished product level is completely unforecastable in a traditional sense. Thousands of those cellphones may sell every year, but for each possible configuration, the demand may be extremely low and sporadic—perhaps certain combinations sell once and never again.

This often forces these companies to plan reorder points and safety stock levels mostly at the component level, while largely reacting to firm demand at the finished good level via MRP. While this is a valid approach, it lacks a systematic way to leverage forecasts that may account for anticipated future activity such as promotions, upcoming projects, or sales opportunities. Forecasting at the “configured” level is effectively impossible, and trying to weave in these forecast assumptions at the component level isn’t feasible either.

Planning BOM Explained This is where Planning BOMs come in. Perhaps the sales team is working on a big B2B opportunity for that model, or there’s a planned promotion for Cyber Monday. While trying to work in those assumptions for every possible configuration isn’t realistic, doing it at the model level is totally doable—and tremendously valuable.

The Planning BOM can use a forecast at a higher level and then blow demand down based on predefined proportions for its possible components. For example, the cellphone manufacturer may know that most people opt for 128GB of storage, and far fewer opt for upgrades to 256GB or 512GB. The planning BOM allows the organization to (for example) blow 60% of the demand down to the 128GB option, 30% to the 256GB option, and 10% to the 512GB option. They could do the same for screen sizes, colors, or other available customizations.

The business can now focus its forecast at this model level, leaving the Planning BOM to determine the component mix. Clearly, defining these proportions requires some thought, but Planning BOMs effectively allows businesses to forecast what would otherwise be unforecastable.

The Importance of a Good Forecast

Of course, we still need a good forecast to load into Epicor Kinetic. As explained in this article, while Epicor Kinetic can import a forecast, it often cannot generate one, and when it does it tends to require a great deal of hard-to-use configurations that don’t often get revisited, resulting in inaccurate forecasts. It is, therefore, up to the business to come up with its own sets of forecasts, often manually produced in Excel. Forecasting manually generally presents a number of challenges, including but not limited to:

• The inability to identify demand patterns like seasonality or trend.
• Overreliance on customer or sales forecasts.
• Lack of accuracy or performance tracking.

No matter how well configured the MRP is with your carefully considered Planning BOMs, a poor forecast means poor MRP output and mistrust in the system—garbage in, garbage out. Continuing along with the “cellphone company” example, without a systematic way of capturing key demand patterns and/or domain knowledge in the forecast, MRP can never see it.

 

Smart IP&O: A Comprehensive Solution

Smart IP&O supports planning at all levels of your BOM, though the “blowing out” is handled via MRP inside Epicor Kinetic. Here is the method we use for our Epicor Kinetic customers, which is straightforward and effective:

• Smart Demand Planner: The platform contains a purpose-built forecasting application called Smart Demand Planner that you will use to forecast demand for your manufactured products (usually finished goods). It generates statistical forecasts, enables planners to make adjustments and/or weave in other forecasts (such as sales or customer forecasts), and tracks accuracy. The output of this is a forecast that goes into forecast entry inside Epicor Kinetic, where MRP will pick it up. MRP will subsequently use demand at the finished good level, and also blow out material requirements through the BOM, so that demand is recognized at lower levels as well.
• Smart Inventory Optimization: You simultaneously use Smart Inventory Optimization to set min/max/safety levels both for any finished goods you make to stock (if applicable; some of our customers operate purely make-to-order off of firm demand), as well as for raw materials. The key here is that at the raw material level, Smart will leverage job usage demand, supplier lead times, etc., to optimize these parameters while at the same time using sales orders/shipments as demand at the finished good level. Smart handles these multiple inputs of demand elegantly via the bidirectional integration with Epicor Kinetic.

When MRP runs, it nets out supply & demand (which, once again, includes raw material demand blown out from the finished good forecast) against the min/max/safety levels you have established to suggest PO and job suggestions.

 

Extend Epicor Kinetic with Smart IP&O

Smart IP&O is designed to extend your Epicor Kinetic system with many integrated demand planning and inventory optimization solutions. For example, it can generate statistical forecasts automatically for large numbers of items, allows for intuitive forecast adjustments, tracks forecast accuracy, and ultimately allows you to generate true consensus-based forecasts to better anticipate the needs of your customers.

Thanks to highly flexible product hierarchies, Smart IP&O is perfectly suited to forecasting at the Planning BOM level, so you can capture key patterns and incorporate business knowledge at the levels that matter most. Furthermore, you can analyze and deploy optimal safety stock levels at any level of your BOM.

Leveraging Epicor Kinetic’s Planning BOM capabilities alongside Smart IP&O’s advanced forecasting and inventory optimization features ensures that you can meet demand efficiently and accurately, regardless of the complexity of your product configurations. This synergy not only enhances forecast accuracy but also strengthens overall operational efficiency, helping you stay ahead in a competitive market.