{"id":9161,"date":"2022-12-05T10:19:41","date_gmt":"2022-12-05T14:19:41","guid":{"rendered":"https:\/\/smartcorp.com\/blog\/extend-epicor-kinetic-forecasting-min-max-planning-copy\/"},"modified":"2022-12-20T15:42:59","modified_gmt":"2022-12-20T19:42:59","slug":"amplie-epicor-prophet-21-con-el-pronostico-puntos-de-pedido-de-smart","status":"publish","type":"post","link":"https:\/\/smartcorp.com\/es\/blog\/epicor-prophet-21-forecasting-dynamic-reorder-point-planning-p21\/","title":{"rendered":"Ampl\u00ede Epicor Prophet 21 con el pron\u00f3stico de Smart IP&O y la planificaci\u00f3n din\u00e1mica de los puntos de pedido"},"content":{"rendered":"

[et_pb_section fb_built=”1″ _builder_version=”4.18.0″ _module_preset=”default” custom_margin=”0px||||false|false” custom_padding=”0px||||false|false” global_colors_info=”{}”][et_pb_row admin_label=”Row” module_class=”blog_glob” _builder_version=”4.16″ locked=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ saved_specialty_column_type=”2_3″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text content_last_edited=”off|desktop” admin_label=”CONTENT” module_class=”post-main_text” _builder_version=”4.19.1″ header_3_text_color=”#0c71c3″ header_3_font_size=”19px” background_size=”initial” background_position=”top_left” background_repeat=”repeat” z_index_tablet=”500″ text_text_shadow_horizontal_length_tablet=”0px” text_text_shadow_vertical_length_tablet=”0px” text_text_shadow_blur_strength_tablet=”1px” link_text_shadow_horizontal_length_tablet=”0px” link_text_shadow_vertical_length_tablet=”0px” link_text_shadow_blur_strength_tablet=”1px” ul_text_shadow_horizontal_length_tablet=”0px” ul_text_shadow_vertical_length_tablet=”0px” ul_text_shadow_blur_strength_tablet=”1px” ol_text_shadow_horizontal_length_tablet=”0px” ol_text_shadow_vertical_length_tablet=”0px” ol_text_shadow_blur_strength_tablet=”1px” quote_text_shadow_horizontal_length_tablet=”0px” quote_text_shadow_vertical_length_tablet=”0px” quote_text_shadow_blur_strength_tablet=”1px” header_text_shadow_horizontal_length_tablet=”0px” header_text_shadow_vertical_length_tablet=”0px” header_text_shadow_blur_strength_tablet=”1px” header_2_text_shadow_horizontal_length_tablet=”0px” header_2_text_shadow_vertical_length_tablet=”0px” header_2_text_shadow_blur_strength_tablet=”1px” header_3_text_shadow_horizontal_length_tablet=”0px” header_3_text_shadow_vertical_length_tablet=”0px” header_3_text_shadow_blur_strength_tablet=”1px” header_4_text_shadow_horizontal_length_tablet=”0px” header_4_text_shadow_vertical_length_tablet=”0px” header_4_text_shadow_blur_strength_tablet=”1px” header_5_text_shadow_horizontal_length_tablet=”0px” header_5_text_shadow_vertical_length_tablet=”0px” header_5_text_shadow_blur_strength_tablet=”1px” header_6_text_shadow_horizontal_length_tablet=”0px” header_6_text_shadow_vertical_length_tablet=”0px” header_6_text_shadow_blur_strength_tablet=”1px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” use_border_color=”off” global_colors_info=”{}”]<\/p>\n

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\u2019s trust in your ERP by providing robust predictive analytics, consensus-based forecasting, and what-if scenario planning.<\/p>\n

Replenishment Planning Features within Epicor Prophet 21
<\/strong>Epicor P21 can manage replenishment by suggesting what to order and when via reorder point-based or forecast-driven inventory policies. \u00a0Users may compute these policies externally or generate them dynamically within P21.\u00a0 Once the policies and forecasts have been specified, P21\u2019s 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.<\/p>\n

Epicor P21 has 4 Replenishment Methods<\/strong>
In the item maintenance screen of Epicor P21, users can choose from one of four replenishment methods for each stock item.<\/p>\n

    \n
  1. Min\/Max<\/li>\n
  2. Order Point\/Order Quantity<\/li>\n
  3. EOQ<\/li>\n
  4. Up To<\/li>\n<\/ol>\n

    There are additional settings and configurations for determining lead times and accounting for order modifiers such as supplier-imposed minimum and maximum order quantities.\u00a0 Min\/Max and Order Point\/Order Quantity are considered \u201cstatic\u201d policies.\u00a0 EOQ and Up To are considered \u201cdynamic\u201d policies and computed within P21.<\/p>\n

    Min\/Max<\/u>
    <\/strong>The reorder point is equal to the Min. \u00a0Whenever 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). \u00a0Min\/Max is considered a static policy and once entered into P21 will remain unchanged unless overridden by the user.\u00a0 Users often run spreadsheets to compute the Min\/Max values and update them from time to time.<\/p>\n

    Order Point\/Order Quantity<\/u>
    <\/strong>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.\u00a0 Users often run spreadsheets to compute OP\/OQ values and update them from time to time.<\/p>\n

    EOQ<\/u>
    <\/strong>The EOQ policy is a reorder point-based method.\u00a0 The reorder point is dynamically generated based on P21\u2019s forecast of demand over lead time + demand over the review period + safety stock.\u00a0 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. \u00a0When on hand inventory breaches the reorder point, the PORG report will kick out an order equal to the computed EOQ.<\/p>\n

    Up To
    <\/u>The Up To method is another dynamic policy that relies on a reorder point.\u00a0 It is computed the same way as the EOQ method using P21\u2019s forecasted demand over the lead time + demand over review period + safety stock. \u00a0\u00a0The order quantity suggestion is based on whatever is needed to replenish stock back \u201cup to\u201d the reorder point.\u00a0 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 \u201cup to\u201d the reorder point.<\/p>\n

    \"Epicor<\/a>

    P21\u2019s Item Maintenance Screen where users can specify the desired inventory policy and configure other settings such as safety stock and order modifiers.<\/p><\/div>\n

    Limitations<\/strong><\/p>\n

    Forecast Methods
    <\/u>There are two forecast modes in P21:\u00a0 Basic and Advanced.\u00a0 Each use a series of averaging methods and require manual configurations and user determined classification rules to generate a demand forecast.\u00a0 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. \u00a0Lots 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.\u00a0 There isn\u2019t a way to easily compare the forecast accuracy of different configurations.\u00a0 For example, is it better to use 24 months of history or 18 months?\u00a0 Is it more accurate to assume a trend should be applied when an item grows by 2% per month or should it be 10%?\u00a0 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\u00a0 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.\u00a0 \u00a0To learn more about how this works, check out this blog post (coming soon)<\/p>\n

    Forecast Management & Consensus Planning
    <\/u>P21 lacks forecast management features that enable organizations to plan at multiple hierarchy levels such as product family, region, or by customer.\u00a0 Forecasts must be created at the lowest level of granularity (product by location) where demand is often too intermittent to get a good forecast.\u00a0 There isn\u2019t 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.\u00a0\u00a0<\/p>\n

    Intermittent Demand
    <\/u>Many P21 customers rely on static methods (Min\/Max and OP\/OQ) because of the prevalence of intermittent demand.\u00a0 Otherwise known as \u201clumpy\u201d, 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\u2019t work.\u00a0 Since distributors don\u2019t 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\u2019s parts will have intermittent demand. \u00a0The 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.\u00a0 As illustrated in the graph below, it isn\u2019t possible to consistently forecast the spikes.\u00a0 You are stuck with a forecast that is effectively an average of the prior periods.<\/p>\n

    \"Epicor<\/a>

    Forecasts of intermittent demand can\u2019t predict the spikes and require safety stock buffers to protect against stockouts.<\/p><\/div>\n

     <\/p>\n

    Second, P21\u2019s safety stock methods allow you to set a target service level but the underlying logic mistakenly assumes that the demand is normally distributed<\/a>.\u00a0 With intermittent demand, the demand isn\u2019t \u201cnormal\u201d and therefore the estimate of safety stock will be wrong.\u00a0\u00a0 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.\u00a0 Using a normal distribution to compute safety stocks will result in large deviations between the targeted service level and actual service level achieved.\u00a0 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%).<\/p>\n

     <\/p>\n

    \"Epicor<\/a>

    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 \u201cnormal distribution: logic employed by P21. The second is a simulated distribution based on Smart Software\u2019s probabilistic forecasting. The \u201cnormal\u201d 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.<\/p><\/div>\n

    This blog<\/a> explains how you can test your system\u2019s service level accuracy.<\/p>\n

    Reliance on Spreadsheets & Reactive Planning<\/u>
    P21 customers tell us that they rely heavily on the use of spreadsheets to manage stocking policies and forecasting.\u00a0 Spreadsheets aren\u2019t purpose-built for forecasting and inventory optimization. Users will often bake in user-defined\u00a0    rule of thumb<\/a>\u00a0methods that often do more harm than good. \u00a0Once calculated, users must input the information back into P21\u00a0via manual file imports or even manual entry. \u00a0The time consuming nature of the process leads companies to\u00a0infrequently compute their inventory policies \u2013\u00a0<\/a>Many months and on occasion years go by in between mass updates leading to a \u201cset it and forget it\u201d reactive approach, where the only time a buyer\/planner reviews inventory policy is at the time of order.\u00a0 When policies are reviewed after the order point is already breached, it is too late.\u00a0 When the order point is deemed too high, manual interrogation is required to review history, calculate forecasts, assess buffer positions, and to recalibrate.\u00a0 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.\u00a0 If the reorder point is too low, it\u2019s already too late.\u00a0 An expedite is now required driving up costs and even then, you\u2019ll still lose sales if the customer goes elsewhere.<\/p>\n

    Limited What If Planning<\/u>
    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.\u00a0 This forces users to adopt a \u201cwait and see\u201d process when it comes to modifying parameters. Planners will make a change and then monitor actuals until they are confident the change improved things. \u00a0Managing this at scale\u2014many planners are dealing with tens of thousands of items\u2014is extremely time consuming and the end result is infrequent recalibration of inventory policy. This also contributes to     reactive planning<\/a> whereby planners will only review settings after a problem has occurred.<\/p>\n

    Epicor is Smarter
    <\/strong>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.\u00a0 This enables Epicor customers to leverage built-for-purpose best of breed forecasting and inventory optimization applications.\u00a0 With Epicor Smart IP&O you can generate forecasts that capture trend and seasonality without having to first apply manual configurations.\u00a0 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<\/a>. \u00a0 Safety stocks will accurately account for demand and supply variability, business conditions, and priorities.\u00a0 You can leverage\u00a0service level driven planning<\/a>\u00a0so you have just enough stock or turn on\u00a0optimization methods<\/a>\u00a0that 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.<\/p>\n

    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\u2019s Demand Planning and Inventory Optimization platform, please register here:\u00a0https:\/\/smartcorp.com\/epicor-smart-inventory-planning-optimization\/<\/a><\/p>\n

     <\/p>\n

    \"\"<\/a><\/p>\n

     <\/p>\n

     <\/p>\n

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    Smart Inventory Planning & Optimization (Smart IP&O) puede ayudar con la funcionalidad de pedidos de inventario en Epicor P21, reducir el inventario, minimizar los desabastecimientos y restaurar la confianza de su organizaci\u00f3n al proporcionar an\u00e1lisis predictivos s\u00f3lidos y pron\u00f3sticos basados en consenso ... <\/p>","protected":false},"author":164,"featured_media":9166,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"

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    Recently, I had an interesting conversation with an inventory manager and the VP Finance. We were discussing the benefits of being able to automatically optimize both reorder points and order quantities. The VP Finance was concerned that given their large supplier required minimum order quantities, they would not be able to benefit.\u00a0 He said his suppliers held all the power, forcing him to accept massive minimum order quantities and tying his hands. While he felt bad about this, he saw a silver lining: He didn\u2019t have to do any planning. He would accept a large inventory investment, but his customer service levels would the exceptional.\u00a0 Perhaps the large inventory investment was assumed to be the cost of doing business.<\/p>

    I pushed back and pointed out that he was not as powerless as he felt. He still had control of the other half of the procurement process: while he couldn\u2019t control how much to order, he could control when to order by adjusting the reorder point. In other words, there is always room for careful quantitative analysis in inventory management, even when you have one hand tied behind your back.<\/p>

    An Example<\/strong><\/p>

    To put some numbers behind my argument, I created a scenario then analyzed it using our methodology to show how consequential it can be to use inventory optimization software even in constrained situations. In this scenario, item demand averages 2.2 units per day but varies significantly by day of week. Let\u2019s say the imaginary supplier insists on a minimum order quantity of 500 units (way out of proportion to demand) and fills replenishment orders in either three days or ten days in equal proportions (quite inconsistent). To spread the blame around, let\u2019s also suppose that the imaginary supplier\u2019s imaginary customer uses a foolish rule that the reorder point should be 10% of the minimum order quantity. (Why this rule? Too many companies use simple\/simplistic rules of thumb in lieu of proper analysis.)<\/p>

    So, we have a base case in which the order quantity is 500 units, and the reorder point is 50 units. In this case, the fill rate is 100%, but the average number of units on hand is a whopping 330. If the customer would simply lower the reorder point from 50 to 15, the fill rate would still be 99.5%, but the average stock on hand would drop by 11% to 295 units. Using the one hand not tied behind his back, the inventory manager could cut his inventory investment by more than 10%, which would be a noticeable win.<\/p>

    Incidentally, if the minimum order quantity were abolished, the customer would be free to arrive at a new and much better solution. Setting the order quantity to 45 and the reorder point to 25 would achieve a 99% fill rate at the cost of a daily on-hand level of only 35 units: nearly a 90% reduction in inventory investment: a major improvement over the status quo.<\/p>

    Postscript<\/strong><\/p>

    These calculations are possible using our software, which can make visible the otherwise unknown relationships between inventory system design choices (e.g., order quantity and reorder point) and key performance indicators (e.g., average units on hand and fill rate).\u00a0 Armed with this ability to conduct these calculations, alternative arrangements with the supplier may now be considered. For example, what if, in exchange for paying a higher price per unit, the supplier agreed to a lower MOQ. Using the software to conduct an analysis of the key performance indicators using the \u201cwhat if\u201d costs and MOQs would reveal the cost per unit and MOQ that would be needed to develop a more profitable deal.\u00a0\u00a0 Once identified, all parties stand to benefit.\u00a0 The supplier now generates a better margin on sales of its products, and the buyer holds considerably less inventory yielding a holding cost reduction that dwarfs the added cost per unit.\u00a0 Everyone wins.<\/p>

    \u00a0<\/p>

    \u00a0<\/p>

    \"\"<\/a><\/p>

    [\/et_pb_text][et_pb_code module_class=\"comment_sect\" _builder_version=\"4.10.7\" global_colors_info=\"{}\"]jQuery(document).ready(function($) { \/\/ Stuff to do as soon as the DOM is ready. Use $() w\/o colliding with other libs$(window).on('load', function(){ if ($(window).width() < 2000 ) { $(\".comment_sect\").append($(\"#comment-wrap\")) } if ($(window).width() < 2000) { $(\"#et-main-area\").prepend($(\".blog_bg \")) } if ($(window).width() < 2000 ) { $(\".post-main_text\").prepend($(\".et_post_meta_wrapper\")) } }); });[\/et_pb_code][et_pb_code admin_label=\"Comment \" module_class=\"com-title\" global_colors_info=\"{}\"]<\/p>

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