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.

Inventory optimization software that supports what-if analysis will expose the tradeoff of stockouts vs. excess costs of varying service level targets. But first it is important to identify how “service levels” is interpreted, measured, and reported. This will avoid miscommunication and the false sense of security that can develop when less stringent definitions are used.  Clearly defining how service level is calculated puts all stakeholders on the same page. This facilitates better decision-making.

There are many differences in what companies mean when they cite their “service levels.”  This can vary from company to company and even from department to department within a company.  Here are two examples:

1. Service level measured “from the shelf” vs. a customer-quoted lead time.
   Service level measured “from the shelf” means the percentage of units ordered that are immediately available from stock. However, when a customer places an order, it is often not shipped immediately. Customer service or sales will quote when the order will be shipped. If the customer is OK with the promised ship date and the order is shipped by that date, then service level is considered to have been met.  Service levels will clearly be higher when calculated over a customer quoted lead time vs. “from the shelf.”

2. Service level measured over fixed vs. variable customer quoted lead time.
   High service levels are often skewed because customer-quoted lead times are later adjusted to allow nearly every order to be filled “on time and in full.” This happens when the initial lead time can’t be met, but the customer agrees to take the order later, and the customer quoted lead time field that is used to track service level is adjusted by sales or customer service.

Clarifying how “service levels” are defined, measured, and reported is essential for aligning organizations and enhancing decision-making, resulting in more effective inventory management practices.

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 this article, we will review the “suggested orders” functionality in Epicor BisTrack, explain its limitations, and summarize how Smart Inventory Planning & Optimization (Smart IP&O) can help reduce inventory & minimize stock-outs by accurately assessing the tradeoffs between stockout risks and inventory costs.

Automating Replenishment in Epicor BisTrack
Epicor BisTrack’s “Suggested Ordering” can manage replenishment by suggesting what to order and when via reorder point-based policies such as min-max and/or manually specified weeks of supply. BisTrack contains some basic functionality to compute these parameters based on average usage or sales, supplier lead time, and/or user-defined seasonal adjustments. Alternatively, reorder points can be specified completely manually. BisTrack will then present the user with a list of suggested orders by reconciling incoming supply, current on hand, outgoing demand, and stocking policies.

How Epicor BisTrack “Suggested Ordering” Works
To get a list of suggested orders, users specify the methods behind the suggestions, including locations for which to place orders and how to determine the inventory policies that govern when a suggestion is made and in what quantity.

First, the “method” field is specified from the following options to determine what kind of suggestion is generated and for which location(s):

Purchase – Generate purchase order recommendations.

1. Centralized for all branches – Generates suggestions for a single location that buys for all other locations.
2. By individual branch – Generates suggestions for multiple locations (vendors would ship directly to each branch).
3. By source branch – Generates suggestions for a source branch that will transfer material to branches that it services (“hub and spoke”).
4. Individual branches with transfers – Generates suggestions for an individual branch that will transfer material to branches that it services (“hub and spoke”, where the “hub” does not need to be a source branch).

Manufacture – Generate work order suggestions for manufactured goods.

1. By manufacture branch.
2. By individual branch.

Transfer from source branch – Generate transfer suggestions from a given branch to other branches.

Next, the “suggest order to” is specified from the following options:

1. Minimum – Suggests orders “up to” the minimum on hand quantity (“min”). For any item where supply is less than the min, BisTrack will suggest an order suggestion to replenish up to this quantity.
2. Maximum when less than min – Suggests orders “up to” a maximum on-hand quantity when the minimum on-hand quantity is breached (e.g. a min-max inventory policy).

3. Based on cover (usage) – Suggests orders based on coverage for a user-defined number of weeks of supply with respect to a specified lead time. Given internal usage as demand, BisTrack will recommend orders where supply is less than the desired coverage to cover the difference.

4. Based on over (sales) – Suggests orders based on coverage for a user-defined number of weeks of supply with respect to a specified lead time. Given sales orders as demand, BisTrack will recommend orders where supply is less than the desired coverage to cover the difference.

5. Maximum only – Suggests orders “up to” a maximum on-hand quantity where supply is less than this max.

Finally, if allowing BisTrack to determine the reorder thresholds, users can specify additional inventory coverage as buffer stock, lead times, how many months of historical demand to consider, and can also manually define period-by-period weighting schemes to approximate seasonality. The user will be handed a list of suggested orders based on the defined criteria. A buyer can then generate POs for suppliers with the click of a button.

Limitations

Rule-of-thumb Methods

While BisTrack enables organizations to generate reorder points automatically, these methods rely on simple averages that do not capture seasonality, trends, or the volatility in an item’s demand. Averages will always lag behind these patterns and are unable to pick up on trends. Consider a highly seasonal product like a snow shovel—if we take an average of Summer/Fall demand as we approach the Winter season instead of looking ahead, then the recommendations will be based on the slower periods instead of anticipating upcoming demand. Even if we consider an entire years’ worth of history or more, the recommendations will overcompensate during the slower months and underestimate the busy season without manual intervention.

Rule of thumb methods also fail when used to buffer against supply and demand variability.  For example, the average demand over the lead time might be 20 units.  However, a planner would often want to stock more than 20 units to avoid stocking out if lead times are longer than expected or demand is higher than the average.  BisTrack allows users to specify the reorder points based on multiples of the averages.  However, because the multiples don’t account for the level of predictability and variability in the demand, you’ll always overstock predictable items and understock unpredictable ones.   Read this article to learn more about why multiples of the average fail when it comes to developing the right reorder point.

Manual Entry
Speaking of seasonality referenced earlier, BisTrack does allow the user to approximate it through the use of manually entered “weights” for each period. This forces the user to have to decide what that seasonal pattern looks like—for every item. Even beyond that, the user must dictate how many extra weeks of supply to carry to buffer against stockouts, and must specify what lead time to plan around. Is 2 weeks extra supply enough? Is 3 enough? Or is that too much? There is no way to know without guessing, and what makes sense for one item might not be the right approach for all items.

Intermittent Demand
Many BisTrack customers may consider certain items “unforecastable” because of the intermittent or “lumpy” nature of their demand. In other words, items that are characterized by sporadic demand, large spikes in demand, and periods of little or no demand at all. Traditional methods—and rule-of-thumb approaches especially—won’t work for these kinds of items. For example, 2 extra weeks of supply for a highly predictable, stable item might be way too much; for an item with highly volatile demand, this same rule might not be enough. Without a reliable way to objectively assess this volatility for each item, buyers are left guessing when to buy and how much.

Reverting to Spreadsheets
The reality is most BisTrack users tend to do the bulk of their planning off-line, in Excel. Spreadsheets aren’t purpose-built for forecasting and inventory optimization. Users will often bake in user-defined rule of thumb methods that often do more harm than good.  Once calculated, users must input the information back into BisTrack manually. The time consuming nature of the process leads companies to infrequently compute their inventory policies – Many months and on occasion years go by in between mass updates leading to a “set it and forget it” reactive approach, where the only time a buyer/planner reviews inventory policy is at the time of order.  When policies are reviewed after the order point is already breached, it is too late.  When the order point is deemed too high, manual interrogation is required to review history, calculate forecasts, assess buffer positions, and to recalibrate.  The sheer volume of orders means that buyers will just release orders rather than take the painstaking time to review everything, leading to significant excess stock.  If the reorder point is too low, it’s already too late.  An expedite may now be required, driving up costs, assuming the customer doesn’t simply go elsewhere.

Epicor is Smarter
Epicor has partnered with Smart Software and offers Smart IP&O as a cross platform add-on to its ERP solutions including BisTrack, a speciality ERP for the Lumber, hardware, and building material industry.  The Smart IP&O solution comes complete with a bidirectional integration to BisTrack.  This enables Epicor customers to leverage built-for-purpose best of breed inventory optimization applications.  With Epicor Smart IP&O you can generate forecasts that capture trend and seasonality without manual configurations.  You will be able to automatically recalibrate inventory policies using field proven, cutting-edge statistical and probabilistic models that were engineered to accurately plan for intermittent demand.   Safety stocks will accurately account for demand and supply variability, business conditions, and priorities.  You can leverage service level driven planning so you have just enough stock or turn on optimization methods that prescribe the most profitable stocking policies and service levels that consider the real cost of carrying inventory. You can support commodity buys with accurate demand forecasting over longer horizons, and run “what-if” scenarios to assess alternative strategies before execution of the plan.

Smart IP&O customers routinely realize 7 figure annual returns from reduced expedites, increased sales, and less excess stock, all the while gaining a competitive edge by differentiating themselves on improved customer service. To see a recorded webinar hosted by the Epicor Users Group that profiles Smart’s Demand Planning and Inventory Optimization platform, please register here.

For too many companies, a critical piece of data fact-finding ― the measurement of demand uncertainty ― is handled by simple but inaccurate rules of thumb.  For example, demand planners will often compute safety stock by a user-defined multiple of the forecast or historical average.  Or they may configure their ERP to order more when on hand inventory gets to 2 x the average demand over the lead time for important items and 1.5 x for less important ones. This is a huge mistake with costly consequences.

The choice of multiple ends up being a guessing game.  This is because no human being can compute exactly how much inventory to stock considering all the uncertainties.  Multiples of the average lead time demand are simple to use but you can never know whether the multiple used is too large or too small until it is too late.  And once you know, all the information has changed, so you must guess again and then wait and see how the latest guess turns out.  With each new day, you have new demand, new details on lead times, and the costs may have changed.  Yesterday’s guess, no more matter how educated is no longer relevant today.  Proper inventory planning should be void of inventory and forecast guesswork.  Decisions must be made with incomplete information but guessing is not the way to go.

Knowing how much to buffer requires a fact-based statistical analysis that can accurately answer questions such as:

• How much extra stock is needed to improve service levels by 5%
• What the hit to on-time delivery will be if inventory is reduced by 5%
• What service level target is most profitable.
• How will the stockout risk be impacted by the random lead times we face.

Intuition can’t answer these questions, doesn’t scale across thousands of parts, and is often wrong.  Data, probability math and modern software are much more effective. Winging it is not the path to sustained excellence.