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.

Did you know that it was Benjamin Franklin who invented the lightning rod to protect buildings from lightning strikes? Now, it’s not every day that we must worry about lightning strikes, but in today’s unpredictable business climate, we do have to worry about supply chain disruptions, long lead times, rising interest rates, and volatile demand. With all these challenges, it’s never been more vital for organizations to accurately forecast parts usage, stocking levels, and to optimize replenishment policies such as reorder points, safety stocks, and order quantities.  In this blog, we’ll explore how companies can leverage innovative solutions like inventory optimization and parts forecasting software that utilize machine learning algorithms, probabilistic forecasting, and analytics to stay ahead of the curve and protect their supply chains from unexpected shocks.

Spare Parts Planning Solutions
Spare parts optimization is a key aspect of supply chain management for many industries. It involves managing the inventory of spare parts to ensure they are available when needed without having excess inventory that can tie up capital and space. Optimizing spare parts inventory is a complex process that requires a deep understanding of usage patterns, supplier lead times, and the criticality of each part for the business.

In this blog, our primary emphasis will be on the crucial aspect of inventory optimization and demand forecasting. However, other approaches highlighted below for spare parts optimization, such as predictive maintenance and 3D printing, Master Data Management, and collaborative planning should be investigated and deployed as appropriate.

1. Predictive Maintenance: Using predictive analytics to anticipate when a part is likely to fail and proactively replace it, rather than waiting for it to break down. This approach can help companies reduce downtime and maintenance costs, as well as improve overall equipment effectiveness.
2. 3D printing: Advancements in 3D printing technology are enabling companies to produce spare parts on demand, reducing the need for excess inventory. This not only saves space and reduces costs but also ensures that parts are available when needed.
3. Master Data Management: Data Management platforms ensure that part data is properly identified, cataloged, cleansed, and organized. All too often, MRO organizations hold the same part number under different SKUs. These duplicate parts serve the same purpose but require different SKU numbers to ensure regulatory compliance or security.  For example, a part used to support a government contract may be required be sourced from a US manufacturer to stay in compliance with “Buy America” regulations.  It’s critical that these part numbers be identified and consolidated into one SKU, when possible, to keep inventory investments in check.
4. Collaborative Planning: Collaborating with suppliers and customers to share data, forecasts, and plan demand can help companies reduce lead times, improve accuracy, and reduce inventory levels. Forecasting plays an essential role in collaboration as sharing insights on purchases, demand, and buying behavior ensures suppliers have the information they need to ensure stock availability for customers.

Inventory Optimization
Abraham Lincoln was once quoted as saying, “Give me six hours to chop down a tree, and I will spend the first four sharpening the axe”? Lincoln knew that preparation and optimization were key to success, just like organizations need to have the right tools, such as inventory optimization software, to optimize their supply chain and stay ahead in the market. With inventory optimization software, organizations can improve their forecasting accuracy, lower inventory costs, improve service levels, and reduce lead times. Lincoln knew that sharpening the axe was necessary to accomplish the job effectively without overexerting.  Inventory Optimization ensures that inventory dollars are allocated effectively across thousands of parts helping ensure service levels while minimizing excess stock.

Spare parts play a decisive role in maintaining operational efficiency, and the lack of critical parts can lead to downtime and reduced productivity. The sporadic nature of spare parts demand makes it difficult to predict when a specific part will be required, resulting in the risk of overstocking or understocking, both of which can incur costs for the organization.  Additionally, managing lead times for spare parts poses its own set of challenges. Some parts may have lengthy delivery times, necessitating the maintenance of adequate inventory levels to avoid shortages. However, carrying excess inventory can be costly, tying up capital and storage space.

Given the myriad of challenges facing materials management departments and spare parts planners, planning demand, stocking levels, and replenishment of spare parts without an effective inventory optimization solution is akin to attempting to chop down a tree with a very blunt axe! The sharper the axe, the better your organization will be able to contend with these challenges.

Smart Software’s Axe is the Sharpest
Smart Inventory Optimization and Demand Planning Software uses a unique empirical probabilistic forecasting approach that results in accurate forecasts of inventory requirements, even where demand is intermittent. Since nearly 90% of spare and service parts are intermittent, an accurate solution to handle this type of demand is required.   Smart’s solution was patented in 2001 and additional innovations were recently patented in May of 2023 (announcements coming soon!).  The solution was awarded as a finalist in the APICS Technological Innovation Category for its role in helping transform the resource management industry.

The Role of Intermittent Demand
Intermittent demand does not conform to a simple normal or bell-shaped distribution that makes it impossible to forecast accurately with traditional, smoothing-based forecasting methods.  Parts and items with intermittent demand – also known as lumpy, volatile, variable or unpredictable demand – have many zero or low-volume values interspersed with random spikes of demand that are often many times larger than the average. This problem is especially prevalent in companies that manage large inventories of service and spare parts in industries such as aviation, aerospace, power and water supply and utilities, automotive, heavy asset management, high tech, as well as in MRO (Maintenance, Repair, and Overhaul).

Scenario Analysis
Smart’s patented and award-winning technology rapidly generates tens of thousands of possible scenarios of future demand sequences and cumulative demand values over an item’s lead time. These scenarios are statistically similar to the item’s observed data, and they capture the relevant details of intermittent demand without relying on the assumptions commonly made about the nature of demand distributions by traditional forecasting methods. The result is a highly accurate forecast of the entire distribution of cumulative demand over an item’s lead time. The bottom line is that with the information these demand distributions provide, companies can easily plan safety stock and service level inventory requirements for thousands of intermittently demanded items with nearly 100% accuracy.

Benefits
Implementing innovative solutions from Smart Software such as SmartForecasts for statistical forecasting, Demand Planner for consensus parts planning, and Inventory Optimization for developing accurate replenishment drivers such as min/max and safety stock levels will provide forward-thinking executives and planners with better control over their organization’s operations.  It will result in the following benefits:

1. Improved Forecasting Accuracy: Accurate demand forecasting is fundamental for any organization that deals with spare parts inventory management. Inventory optimization software uses sophisticated algorithms to analyze historical usage patterns, identify trends and forecast future demand with a high degree of accuracy. With this level of precision in forecasting, organizations can avoid the risk of overstocking or understocking their spare parts inventory.
2. Lower Inventory Costs: One major challenge that supply chain leaders face when dealing with spare parts inventory management is the cost associated with maintaining an optimal stock of spares at all times. By optimizing inventory levels using modern technology systems like artificial intelligence (AI), machine learning (ML), and predictive analytics, organizations can reduce carrying costs while ensuring they have adequate stocks available when needed.
3. Improved Service Levels: When it comes to repair and maintenance services, time is money! Downtime due to the unavailability of critical spare parts can result in lost productivity and revenue for businesses across industries such as manufacturing plants, power generation facilities, or data centers managing IT infrastructure equipment. Optimizing your spare parts inventory ensures that you always have the right amount on hand, reducing downtime caused by waiting for deliveries from suppliers.
4. Reduced Lead Times: Another benefit that accrues from accurate demand forecasting through modern warehouse technologies is reduced lead time in delivery which leads to better customer satisfaction since customers will receive their orders faster than before thus improving brand loyalty. Therefore, the adoption of new strategies driven by AI/ML tools creates value within supply chain operations leading to increased efficiency gains not only limited reductionism cost but also streamlining processes related to production scheduling, logistics transportation planning among others

Conclusion
Through the utilization of inventory optimization and demand planning software, organizations can overcome various challenges such as supply chain disruptions, rising interest rates, and volatile demand. This enables them to reduce costs associated with excess storage space and obsolete inventory items. By leveraging sophisticated algorithms, inventory optimization software enhances forecasting accuracy, ensuring organizations can avoid overstocking or under-stocking their spare parts inventory. Additionally, it helps lower inventory costs by optimizing levels and leveraging technologies like artificial intelligence (AI), machine learning (ML), and predictive analytics. Improved service levels are achieved as organizations have the right quantity of spare parts readily available, reducing downtime caused by waiting for deliveries. Furthermore, accurate demand forecasting leads to reduced lead times, enhancing customer satisfaction and fostering brand loyalty. Adopting such strategies driven by AI/ML tools not only reduces costs but also streamlines processes, including production scheduling and logistics transportation planning, ultimately increasing efficiency gains within the supply chain.

In this article, we will review the inventory ordering functionality in Epicor P21, explain its limitations, and summarize how Smart Inventory Planning & Optimization (Smart IP&O) can help reduce inventory, minimize stock-outs and restore your organization’s trust in your ERP by providing robust predictive analytics, consensus-based forecasting, and what-if scenario planning.

Replenishment Planning Features within Epicor Prophet 21
Epicor P21 can manage replenishment by suggesting what to order and when via reorder point-based or forecast-driven inventory policies.  Users may compute these policies externally or generate them dynamically within P21.  Once the policies and forecasts have been specified, P21’s Purchase Order Requirements Generator (PORG) will create automated order suggestions of what to replenish and when by reconciling incoming supply, current on hand, outgoing demand, stocking policies, and demand forecasts.

Epicor P21 has 4 Replenishment Methods
In the item maintenance screen of Epicor P21, users can choose from one of four replenishment methods for each stock item.

1. Min/Max
2. Order Point/Order Quantity
3. EOQ
4. Up To

There are additional settings and configurations for determining lead times and accounting for order modifiers such as supplier-imposed minimum and maximum order quantities.  Min/Max and Order Point/Order Quantity are considered “static” policies.  EOQ and Up To are considered “dynamic” policies and computed within P21.

Min/Max
The reorder point is equal to the Min.  Whenever on hand inventory drops below the Min (reorder point) the PORG report will create an order suggestion up to the Max (for example, if on hand after the breach is 20 units and the Max is 100 then the order quantity will be 80).  Min/Max is considered a static policy and once entered into P21 will remain unchanged unless overridden by the user.  Users often run spreadsheets to compute the Min/Max values and update them from time to time.

Order Point/Order Quantity
This is the same as the Min/Max policy except instead of ordering up to the Max, an order will be suggested for a fixed quantity defined by the user (for example, always order 100 units when the order point is breached). OP/OQ is considered a static policy and will remain unchanged unless overridden by the user.  Users often run spreadsheets to compute OP/OQ values and update them from time to time.

EOQ
The EOQ policy is a reorder point-based method.  The reorder point is dynamically generated based on P21’s forecast of demand over lead time + demand over the review period + safety stock.  The order quantity is based on an Economic Order Quantity calculation that considers holding costs and ordering costs and attempts to recommend an order size that minimizes total cost.  When on hand inventory breaches the reorder point, the PORG report will kick out an order equal to the computed EOQ.

Up To
The Up To method is another dynamic policy that relies on a reorder point.  It is computed the same way as the EOQ method using P21’s forecasted demand over the lead time + demand over review period + safety stock.   The order quantity suggestion is based on whatever is needed to replenish stock back “up to” the reorder point.  This tends to equate to an order quantity that is consistent with the lead time demand because as demand drives stock below the reorder point, orders will be suggested “up to” the reorder point.

P21’s Item Maintenance Screen where users can specify the desired inventory policy and configure other settings such as safety stock and order modifiers.

Limitations

Forecast Methods
There are two forecast modes in P21:  Basic and Advanced.  Each use a series of averaging methods and require manual configurations and user determined classification rules to generate a demand forecast.  Neither mode is designed with an out-of-the-box expert system that automatically generates forecasts that account for underlying patterns such as trend or seasonality.  Lots of configuration is required that tends to inhibit user adoption and modification of the assumed forecasting rules defined in the initial implementation that may no longer be relevant.  There isn’t a way to easily compare the forecast accuracy of different configurations.  For example, is it better to use 24 months of history or 18 months?  Is it more accurate to assume a trend should be applied when an item grows by 2% per month or should it be 10%?  Is it better to assume the item is seasonal if 80% or more of it’s demand occurs in 6 months of the year or  4 months of the year? As a result, it is common for classification rules to be too broad or specific resulting in problems such as application of an incorrect forecasting model, using too much or too little history, or over/understating the trend and seasonality.   To learn more about how this works, check out this blog post (coming soon)

Forecast Management & Consensus Planning
P21 lacks forecast management features that enable organizations to plan at multiple hierarchy levels such as product family, region, or by customer.  Forecasts must be created at the lowest level of granularity (product by location) where demand is often too intermittent to get a good forecast.  There isn’t a way to share forecasts, collaborate, review, or create forecasts at aggregate levels, and agree on the consensus plan. It is difficult to incorporate business knowledge, assess forecasts at higher levels of aggregation, and track whether overrides are improving or hurting forecast accuracy. This makes forecasting too one-dimensional and dependent on the initial math configurations.  

Intermittent Demand
Many P21 customers rely on static methods (Min/Max and OP/OQ) because of the prevalence of intermittent demand.  Otherwise known as “lumpy”, intermittent demand is characterized by sporadic sales, large spikes in demand, and many periods with no demand at all. When demand is intermittent, traditional forecasting and safety stock methods just don’t work.  Since distributors don’t have the luxury of stocking only high movers with consistent demand, they need specialized solutions that are engineered to effectively plan intermittently demanded items. 80% or more of a distributor’s parts will have intermittent demand.  The stocking policies that are generated using traditional methods such as those available in P21 and other planning applications will result in incorrect estimates of what to stock to achieve the targeted service level.  As illustrated in the graph below, it isn’t possible to consistently forecast the spikes.  You are stuck with a forecast that is effectively an average of the prior periods.

Forecasts of intermittent demand can’t predict the spikes and require safety stock buffers to protect against stockouts.

Second, P21’s safety stock methods allow you to set a target service level but the underlying logic mistakenly assumes that the demand is normally distributed.  With intermittent demand, the demand isn’t “normal” and therefore the estimate of safety stock will be wrong.   Here is what wrong means: when setting a service level of, for example 98%, the expectation is that 98% of the time the stock on hand will fill 100% of what the customer needs from the shelf.  Using a normal distribution to compute safety stocks will result in large deviations between the targeted service level and actual service level achieved.  It is not uncommon to see situations where the actual service level misses the target by 10% or more (i.e., targeted 95% but only achieved 85%).

In this figure you can see the demand history of an intermittently demanded part and two distributions based on this demand history. The first distribution was generated using the same “normal distribution: logic employed by P21. The second is a simulated distribution based on Smart Software’s probabilistic forecasting. The “normal” P21 distribution recommends that 46 units is needed to achieve the 99% service level but when compared to actuals far more inventory was needed. Smart accurately predicted that 63 units was required to achieve the service level.

This blog explains how you can test your system’s service level accuracy.

Reliance on Spreadsheets & Reactive Planning
P21 customers tell us that they rely heavily on the use of spreadsheets to manage stocking policies and forecasting.  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 P21 via manual file imports or even manual entry.  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 is now required driving up costs and even then, you’ll still lose sales if the customer goes elsewhere.

Limited What If Planning
Since features for modifying reorder points and order quantities are baked into P21 it is not possible to make wholesale changes across groups of items and assess predicted outcomes before deciding to commit.  This forces users to adopt a “wait and see” process when it comes to modifying parameters. Planners will make a change and then monitor actuals until they are confident the change improved things.  Managing this at scale—many planners are dealing with tens of thousands of items—is extremely time consuming and the end result is infrequent recalibration of inventory policy. This also contributes to reactive planning whereby planners will only review settings after a problem has occurred.

Epicor is Smarter
Epicor has partnered with Smart Software and offers Smart IP&O as a cross platform add-on to Prophet 21 complete with a bidirectional API-based integration.  This enables Epicor customers to leverage built-for-purpose best of breed forecasting and inventory optimization applications.  With Epicor Smart IP&O you can generate forecasts that capture trend and seasonality without having to first apply manual configurations.  You will be able to automatically recalibrate policies every planning cycle 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 build consensus demand forecasts that blend business knowledge with statistics, better assess customer and sales forecasts, and confidently upload forecasts and stocking policies to Epicor with a few mouse-clicks.

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: https://smartcorp.com/epicor-smart-inventory-planning-optimization/

Extend Epicor Kinetic’s Forecasting & Min/Max Planning with Smart IP&O  
Epicor Kinetic can manage replenishment by suggesting what to order and when via reorder point-based inventory policies. Users can either manually specify these reorder points or use a daily average of demand to dynamically compute the policies.  If the policies aren’t correct then the automatic order suggestions will be inaccurate, and in turn the organization will end up with excess inventory, unnecessary shortages, and a general mistrust of their software systems.  In this article, we will review the inventory ordering functionality in Epicor Kinetic, explain its limitations, and summarize how Smart Inventory Planning & Optimization (Smart IP&O) can help reduce inventory, minimize stockouts and restore your organization’s trust in your ERP by providing the robust predictive functionality that is missing from ERP systems.

Epicor Kinetic (and Epicor ERP 10) Replenishment Policies
In the item maintenance screen of Epicor Kinetic, users can enter planning parameters for every stock item. These include Min On-Hand, Max On-Hand, Safety Stock lead times, and order modifiers such as supplier imposed minimum and maximum order quantities and order multiples.  Kinetic will reconcile incoming supply, current on hand, outgoing demand, stocking policies, and demand forecasts (that must be imported) to net out the supply plan.   Epicor’s time-phased replenishment inquiry details what is up for order and when while the Buyers Workbench enables users to assemble purchase orders.

Epicor’s Min/Max/Safety logic and forecasts that are entered into the “forecast entry” screen drives replenishment.  Here is how it works:

• The reorder point is equal to Min + Safety. This means whenever on hand inventory drops below the reorder point an order suggestion will be created. If demand forecasts are imported via Epicor’s “forecast entry” screen the reorder point will account for the forecasted demand over the lead time and is equal to Min + Safety + Lead time forecast
• If “reorder to Max” is selected, Epicor will generate an order quantity up to the Max. If not selected, Epicor will order the “Min Order Qty” if MOQ is less than the forecasted quantity over the time fence. Otherwise, it will order the forecasted demand over the time-period specified.  In the buyer’s workbench, the buyer can modify the actual order quantity if desired.

Limitations
Epicor’s Min/Max/Safety relies on an average of daily demand. It is easy to set up and understand.  It can also be effective when you don’t have lots of demand history. However, you’ll have to create forecasts and adjust for seasonality, trend, and other patterns externally.  Finally, multiples of averages also ignore the important role of demand or supply variability and this can result in misallocated stock as illustrated in the graphic below: 

In this example, two equally important items have the same average demand (2,000 per month) and safety stock is determined by doubling the lead time demand resulting in a reorder point of 4,000. Because the multiple ignores the role of demand variability, Item A results in a significant overstock and Item B results in significant stockouts.

As designed, Min should hold expected demand over lead time and Safety should hold a buffer. However, these fields are often used very differently across items without a uniform policy; sometimes users even enter a Min and Safety Stock even though the item is being forecasted, effectively over estimating demand! This will generate order suggestions before it is needed, resulting in overstocks.  

Spreadsheet Planning
Many companies turn to spreadsheets when they face challenges setting policies in their ERP system.  These spreadsheets often rely on user defined rule of thumb methods that often do more harm than good.  Once calculated, they must input the information back into Epicor,  via manual file imports or even manual entry.  The time consuming nature of the process leads companies to infrequently compute their inventory policies – Many months of even 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 is now required driving up costs and even then you’ll still lose sales if the customer goes elsewhere.

Epicor is Smarter
Epicor has partnered with Smart Software and offers Smart IP&O as a cross platform add-on to Epicor Kinetic and Prophet 21 with API based integrations.  This enables Epicor customers to leverage built for purpose best of breed forecasting and inventory optimization applications.  With Epicor Smart IP&O you can automatically recalibrate policies every planning cycle using field proven, cutting-edge statistical and probabilistic models.  You can calculate demand forecasts that account for seasonality, trend, and cyclical patterns.  Safety stocks will 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 build consensus demand forecasts that blend business knowledge with statistics, better assess customer and sales forecasts, and confidently upload forecasts and stocking policies to Epicor within a few mouse-clicks.

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: https://smartcorp.com/epicor-smart-inventory-planning-optimization/