Effective supply chain and inventory management are essential for achieving operational efficiency and customer satisfaction. This blog provides clear and concise answers to some basic and other common questions from our Smart IP&O customers, offering practical insights to overcome typical challenges and enhance your inventory management practices. Focusing on these key areas, we help you transform complex inventory issues into strategic, manageable actions that reduce costs and improve overall performance with Smart IP&O.

1. What is lead time demand?
The demand is expected to occur over the replenishment lead time. Lead time demand is determined by Smart’s forecasting methods. 

2. What is the Min, and how is it computed?
The Min is displayed in the drivers section of SIO is the reorder point and is the sum of the lead time demand and the safety stock. When on-hand inventory falls below the minimum due to demand, you will need to order more.  Smart also has a “min” in the “ordering rules” field of SIO, which is the minimum order quantity you can place with a supplier. 

3. What is the Max, and how is it computed?
The max is the largest quantity of inventory that will be on the shelf if you adhere to the ordering policy. The Max is the sum of the Min (reorder point) plus the defined OQ. 

4. How do you determine the order quantity (OQ)?
The order quantity is initially imported from your ERP system. It can be changed based on a number of user defined choices, including:

Multiple lead time demand
Multiple monthly or weekly demand
Smart’s recommended OQ

5. What is the Economic Order Quantity?
It is the order quantity that will minimize the total costs, considering the cost of holding and costs of ordering inventory. 

6. What is the “recommended OQ” that Smart computes?
It is the economic order quantity plus an adjustment if necessary to ensure that the size of the order is greater than or equal to the demand over lead time.

7. Why is the system predicting that we’ll have a low service level?
Smart predicts the service level that will result from the specified inventory policy (Min/Max or Reorder Point/Order Quantity), assuming adherence to that policy.  When the predicted service level is low, it can mean that the expected demand over the lead time is greater than the reorder point (Min).  When demand over the replenishment lead time is greater than the reorder point your probability of stocking out is higher resulting in a low service level. It may also be that your lead time for replenishment isn’t entered accurately.  If the lead time entered is longer than reality, the reorder point may not cover the demand over the lead time.  Please check your lead time inputs.

8. Why is the Service level showing as zero when the reorder point (or min) is not zero?
Smart predicts the service level that will result from the specified inventory policy (Min/Max or Reorder Point/Order Quantity), assuming adherence to that policy. When the predicted service level is low, it can mean that the expected demand over the lead time is greater than the reorder point (Min), sometimes many times greater, which would all but guarantee a stock-out.  When demand over the replenishment lead time is greater than the reorder point your probability of stocking out is higher resulting in a low service level. It may also be that your lead time for replenishment isn’t entered accurately.  If the lead time entered is longer than reality, the reorder point may not cover the demand over the lead time.  Please check your lead time inputs.

9. But my actual service levels aren’t as low as Smart is predicting, why?
That may be true because Smart predicts your service level if you adhere to the policy.  It is possible you aren’t adhering to the policy that the service level prediction is based on.  If your on-hand inventory is higher than your Max quantity, you aren’t adhering to the policy.  Check your input assumptions for lead time.   Your actual lead times might be much shorter than entered resulting in a predicted service level that is lower than you expect.

10. Smart seems to be recommending too much inventory, or at least more than I’d expect it would; why?
You should consider evaluating the inputs, such as service level and lead times.  Perhaps your actual lead times aren’t as long as the lead time Smart is using.  We’ve seen situations where suppliers artificially inflate their quoted lead times to ensure they are always on time.  If you use that lead time when computing your safety stocks, you’ll inevitably over-stock.  So, review your actual lead time history (Smart provides the supplier performance report for this) to get a sense of the actual lead times and adjust accordingly.  Or it is possible you are asking for a very high service level that may be further compounded with a very volatile item that has several significant spikes in demand.  When demand significantly fluctuates from the mean, using a high service level target (98%+) will result in stocking policies that are designed to cover even very large spikes.  Try a lower service level target or reducing the lead time (assuming the specified lead time is no longer realistic) and your inventory will decrease, sometimes very substantially.

11. Smart is using spikes in demand I don’t want it to consider, and it is inflating inventory, how can I correct this?
If you are sure that the spike won’t happen again, then you can remove it from the historical data via an override using Smart Demand Planner. You’ll need to open the forecast project containing that item, adjust the history, and save the adjusted history.  You can contact tech support to help you set this up. If the spikes are part of the normal randomness that can sometimes occur, it’s best to leave it alone. Instead, consider a lower service level target.  The lower target means the reorder points don’t need to cover the extreme values as often resulting in a lower inventory.

12. When I change the Order Quantity or Max, my cycle service levels don’t change, why?
Smart reports on “cycle service level” and “service level.”  When you change your order quantities and Max quantities this will not impact the “cycle service level” because cycle service levels report on performance during the replenishment period only.  This is because all that protects you from stockout after the order is placed (and you must wait until the order arrives for the replenishment) is the reorder point or Min. Changing the size of the order quantity or Max on hand (up to levels) won’t impact your cycle service levels.  Cycle service level is influenced only by the size of the reorder points and the amount of safety stock being added whereas Smart’s “service level” will change when you modify both reorder points and order quantities.

13. My forecast looks inaccurate.  It’s not showing any of the ups and downs observed in history, why?
A good forecast is the one number that is closest to the actual compared to other numbers that could have been predicted.  When the historical ups and downs aren’t happening in predictable intervals then often, the best forecast is one that averages or smooths through those historical ups and downs.  A forecast predicting future ups and downs that aren’t happening in obvious patterns historically is more likely to be less accurate than one that is forecast a straight or trend line only.

14. What is optimization? How does it work?
Optimization is an option for setting stocking policies where the software picks the stocking policy that yields the total lowest operating cost.  For example, if an item is very expensive to hold, a policy that has more stockouts, but less inventory would yield total lower costs than a policy that had fewer stockouts and more inventory.   On the other hand, if the item has a high stock out cost then a policy that yields fewer stockouts but requires more inventory would yield more financial benefit than a policy that had less inventory but more stockouts.  When using the optimization feature, the user must specify the service level floor (the minimum service level).  The software will then decide whether a higher service level will yield a better return.  If it does, the reordering policies will target the higher service level.  If it doesn’t the reordering policies will default to the user defined service level floor.        This webinar provides details and explanations on the math behind optimization.  https://www.screencast.com/t/3CfKJoMe2Uj

15. What is a what-if scenario?
What-if scenarios enable you to try out different user-defined choices of inventory policy and test the predicted impact on metrics such as service levels, fill rates, and inventory value. To explore these scenarios, click on the Drivers tab, either at the summary level or the “Items” level, and enter the desired adjustments. You can then recalculate to see how these changes would affect your overall inventory performance. This allows you to compare various strategies and select the most cost-effective and efficient approach for your supply chain.

By addressing common questions and challenges, we’ve provided actionable insights to help you improve your inventory management practices. With Smart IP&O, you have the tools you need to make informed inventory decisions, reduce costs, and enhance overall performance.

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.

 

 

In this blog, we are steering the conversation towards the transformative potential of technology in inventory management. The discussion centers around the limitations of simple thinking in managing inventory control processes and the necessity of adopting systematic software solutions. Dr. Tom Willemain highlights the contrast between Smart Software and the basic, albeit comfortable, approaches commonly employed by many businesses. These elementary methods, often favored for their ease of use and zero cost, are scrutinized for their inadequacies in addressing the dynamic challenges of inventory management.

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​The importance of this subject lies in the critical role inventory management plays in a business’s operational efficiency and its direct impact on customer satisfaction and profitability. Dr. Tom Willemain points out the common pitfalls of relying on oversimplified rules of thumb, such as the whimsical nursery rhyme used by one company to determine reorder points, or the gut feel method, which depends on unquantifiable intuition rather than data. These approaches, while appealing in their simplicity, fail to adapt to market fluctuations, supplier reliability, or changes in demand, thus posing significant risks to the business. The video also critiques the practice of setting reorder points based on multiples of average demand, highlighting its disregard for demand volatility, a fundamental consideration in inventory theory.

Concluding, the presenter advocates for a more sophisticated, data-driven approach to inventory management. By leveraging advanced software solutions like those offered by Smart Software, businesses can accurately model complex demand patterns and stress-test inventory rules against numerous future scenarios. This scientific method allows for the setting of reorder points that account for real-world variability, thereby minimizing the risk of stockouts and the associated costs. The video emphasizes that while simple heuristics may be tempting for their ease of use, they are inadequate for today’s dynamic market conditions. The presenter encourages viewers to embrace technological solutions that offer professional-grade accuracy and adaptability, ensuring sustainable business success.

 

 

​​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.

 

 

We believe the leading edge of supply chain analytics to be the development of digital twins of inventory systems. These twins take the form of discrete event models that use Monte Carlo simulation to generate and optimize over the full range of operational risks. We also assert that we and our colleagues at Smart Software have played an outsized role in forging that leading edge. But we are not alone: there are a small number of other software firms around the globe who are catching up.

So, what’s next for supply chain analytics? Where is the next frontier? It might involve some sort of neural network model of a distribution system. But we’d give better odds on an extension of our leading-edge models of “single echelon” inventory systems to “multi-echelon” inventory systems.

Figures 1 and 2 illustrate the distinction between single and multiple echelon systems. Figure 1 depicts a manufacturer that relies on a Source to replenish its stock of spare parts or components. When stockouts loom, the manufacturer orders replenishment stock from the Source.

Figure 1: A single-echelon inventory system

 

Single echelon models do not explicitly include details of the Source. It remains mysterious, an invisible ghost whose only relevant feature is the random time it takes to respond to a replenishment request. Importantly, the Source is implicitly assumed to never itself stock out. That assumption may be “good enough” for many purposes, but it cannot be literally true. It gets handled by stuffing supplier stockout events into the replenishment lead time distribution. Pushing back on that assumption is the rationale for multiechelon modeling.

Figure 2 depicts a simple two-echelon inventory system. It shifts domains from manufacturing to distribution. There are multiple warehouses (WH’s) dependent on a distribution center (DC) for resupply. Now the DC is an explicit part of the model. It has a finite capacity to process orders and requires its own reordering protocols. The DC gets its replenishment from higher up the chain from a Source. The Source might be the manufacturer of the inventory item or perhaps a “regional DC” or something similar, but – guess what? – it is another ghost. As in the single-echelon model, this ghost has one visible characteristic: the probability distribution of its replenishment lead time. (The punch line of a famous joke in physics is “But madame, it’s turtles all the way down.” In our case, “It’s ghosts all the way up.”)

Figure 2: A two-echelon inventory system

 

The problem of process design and optimization is much harder with two levels. The difficulty is not just the addition of two more control parameters for every WH (e.g., a Min and a Max for each) plus the same two parameters for the DC. Rather, the tougher part is modeling the interaction among the WH’s. In the single-level model, each WH operates in its own little world and never hears “Sorry, we’re stocked out” from the ghostly Source. But in a two-level system, there are multiple WH’s all competing for resupply from their shared DC. This competition creates the main analytical difficulty: the WH’s cannot be modeled in isolation but must be analyzed simultaneously. For instance, if one DC services ten WH’s, there are 2+10×2 = 22 inventory control parameters whose values need to be calculated. In nerd-speak: It is not trivial to solve a 22-variable constrained discrete optimization problem having a stochastic objective function.

If we choose the wrong system design, we discover a new phenomenon inherent in multi-echelon systems, which we informally call “meltdown” or “catastrophe.” In this phenomenon, the DC cannot keep up with the replenishment demands of the WH’s, so it eventually creates stockouts at the warehouse level. Then the WH’s increasingly frantic replenishment requests exhaust the inventory at the DC, which starts its own panicked requests for replenishment from the regional DC. If the regional DC takes too long to refill the DC, then the whole system dissolves into a stockout tragedy.

One solution to the meltdown problem is to overdesign the DC so it almost never runs out, but that can be very expensive, which is why there is a regional DC in the first place. So any affordable system design has a DC that is just good enough to last a long time between meltdowns. This perspective implies a new type of key performance indicator (KPI), such as “Probability of Meltdown within X years is less than Y percent.”

The next frontier will require new methods and new metrics but will offer a new way to design and optimize distribution systems. Our skunk works is already generating prototypes. Watch this space.