{"id":7046,"date":"2026-07-15T22:27:09","date_gmt":"2026-07-15T20:27:09","guid":{"rendered":"https:\/\/one-hive.com\/en\/resources\/articles\/architecture-of-a-demand-planning-model-in-anaplan-features-and-granularity\/"},"modified":"2026-10-15T23:10:40","modified_gmt":"2026-10-15T21:10:40","slug":"architecture-of-a-demand-planning-model-in-anaplan-features-and-granularity","status":"publish","type":"post","link":"https:\/\/one-hive.com\/en\/resources\/articles\/architecture-of-a-demand-planning-model-in-anaplan-features-and-granularity\/","title":{"rendered":"Architecture of a demand planning model in Anaplan: features and granularity"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"7046\" class=\"elementor elementor-7046 elementor-7010\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e061518 e-con-full oh-bg--oxford-blue oh-radius--bottom-30 e-flex e-con e-parent\" data-id=\"e061518\" data-element_type=\"container\" data-e-type=\"container\" data-theme=\"dark\">\n\t\t\t\t<div class=\"elementor-element elementor-element-23b8694a elementor-widget__width-initial elementor-widget elementor-widget-oh-hero\" data-id=\"23b8694a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"oh-hero.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<section class=\"oh-hero oh-hero--leger\"><picture class=\"oh-hero__bg\" ><source media=\"(max-width:767px)\" srcset=\"https:\/\/one-hive.com\/wp-content\/uploads\/2026\/10\/Articles-experts_HEADER-mobile_architecture_demand_planning-1.png\"><img src=\"https:\/\/one-hive.com\/wp-content\/uploads\/2026\/10\/Articles-experts_HEADER-DESKTOP_architecture_demand_planning-1.png\" alt=\"Architecture of a demand planning model in Anaplan: features and granularity\" decoding=\"async\" fetchpriority=\"high\"><\/picture><div class=\"oh-hero__inner\"><h1 class=\"oh-hero__title\">Architecture of a demand planning model in Anaplan: features and granularity<\/h1><h3 class=\"oh-hero__intro\">Explore the design and configuration choices behind an effective demand planning model in Anaplan.<\/h3><div class=\"oh-hero__ctas\"><\/div><\/div><\/section>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-238815ce oh-breadcrumbs elementor-widget elementor-widget-ucaddon_breadcrumbs_resources\" data-id=\"238815ce\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"ucaddon_breadcrumbs_resources.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\n<!-- start Breadcrumbs Resources -->\n\t\t<link id='font-awesome-css' href='https:\/\/one-hive.com\/wp-content\/plugins\/unlimited-elements-for-elementor-premium\/assets_libraries\/font-awesome6\/fontawesome-all.min.css' type='text\/css' rel='stylesheet' >\n\t\t<link id='font-awesome-4-shim-css' 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class=\"elementor-icon-list-item elementor-repeater-item-533dec7 elementor-inline-item\" itemprop=\"datePublished\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text elementor-post-info__item elementor-post-info__item--type-date\">\n\t\t\t\t\t\t\t\t\t\t<time>July 15, 2026<\/time>\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t<li class=\"elementor-icon-list-item elementor-repeater-item-6c3357c elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text elementor-post-info__item elementor-post-info__item--type-custom\">\n\t\t\t\t\t\t\t\t\t\tREADING TIME: 12 MIN \t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-42ee779a e-con-full oh-expertise-x oh-padding e-flex e-con e-parent\" data-id=\"42ee779a\" data-element_type=\"container\" data-e-type=\"container\" data-theme=\"light\">\n\t\t<div class=\"elementor-element 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class=\"oh-expert-card__type oh-categories\">Expert<\/div><div class=\"oh-expert-card__name oh-subtitle\">Etienne Pethelaz<\/div><div class=\"oh-expert-card__role oh-subtitle\">Senior Consultant<\/div><div class=\"oh-expert-card__ctas\"><a class=\"oh-expert-card__cta oh-expert-card__cta--linkedin\" href=\"https:\/\/www.linkedin.com\/in\/etienne-pethelaz-434300145\/\" aria-label=\"LinkedIn\" target=\"_blank\" rel=\"noopener\"><svg aria-hidden=\"true\" width=\"18\" height=\"18\" viewBox=\"0 0 18 18\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><g clip-path=\"url(#clip0_3889_4253)\"><path d=\"M16.6676 0H1.32891C0.594141 0 0 0.580078 0 1.29727V16.6992C0 17.4164 0.594141 18 1.32891 18H16.6676C17.4023 18 18 17.4164 18 16.7027V1.29727C18 0.580078 17.4023 0 16.6676 0ZM5.34023 15.3387H2.66836V6.74648H5.34023V15.3387ZM4.0043 5.57578C3.14648 5.57578 2.45391 4.8832 2.45391 4.02891C2.45391 3.17461 3.14648 2.48203 4.0043 2.48203C4.85859 2.48203 5.55117 3.17461 5.55117 4.02891C5.55117 4.87969 4.85859 5.57578 4.0043 5.57578ZM15.3387 15.3387H12.6703V11.1621C12.6703 10.1672 12.6527 8.88398 11.2816 8.88398C9.89297 8.88398 9.68203 9.97031 9.68203 11.0918V15.3387H7.01719V6.74648H9.57656V7.9207H9.61172C9.9668 7.2457 10.8387 6.53203 12.1359 6.53203C14.8395 6.53203 15.3387 8.31094 15.3387 10.6242V15.3387Z\" fill=\"#12329E\"\/><\/g><defs><clipPath id=\"clip0_3889_4253\"><rect width=\"18\" height=\"18\" fill=\"white\"\/><\/clipPath><\/defs><\/svg><\/a><a class=\"oh-expert-card__cta oh-expert-card__cta--contact\" href=\"mailto:contact@one-hive.com\" target=\"_blank\" aria-label=\"Contacter Etienne Pethelaz\"><svg aria-hidden=\"true\" width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M4 20C3.45 20 2.97917 19.8042 2.5875 19.4125C2.19583 19.0208 2 18.55 2 18V6C2 5.45 2.19583 4.97917 2.5875 4.5875C2.97917 4.19583 3.45 4 4 4H20C20.55 4 21.0208 4.19583 21.4125 4.5875C21.8042 4.97917 22 5.45 22 6V18C22 18.55 21.8042 19.0208 21.4125 19.4125C21.0208 19.8042 20.55 20 20 20H4ZM12 13L4 8V18H20V8L12 13ZM12 11L20 6H4L12 11ZM4 8V6V18V8Z\" fill=\"#12329E\"\/><\/svg><\/a><\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-768a8549 e-con-full oh-summary e-flex e-con e-child\" data-id=\"768a8549\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-491c9d9b elementor-widget elementor-widget-heading\" data-id=\"491c9d9b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\"><a href=\"#classification\">Classification, product linking and historical data cleansing<\/a><\/span>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-10608e92 elementor-widget elementor-widget-heading\" data-id=\"10608e92\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\"><a href=\"#previsions\">Statistical forecasting, Machine Learning and Best Fit in Anaplan<\/a><\/span>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-14b72ea elementor-widget elementor-widget-heading\" data-id=\"14b72ea\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\"><a href=\"#promotions\">Promotions, events and product lifecycle management<\/a><\/span>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c877fec elementor-widget elementor-widget-heading\" data-id=\"c877fec\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\"><a href=\"#ajustement\">Forecast adjustments and scenario planning<\/a><\/span>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a96398f elementor-widget elementor-widget-heading\" data-id=\"a96398f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\"><a href=\"#revue\">Demand review and alignment with S&amp;OP<\/a><\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-724f457 e-con-full oh-rs-partage e-flex e-con e-child\" data-id=\"724f457\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3d369704 elementor-widget elementor-widget-heading\" data-id=\"3d369704\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\">Share on<\/span>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element 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10.1328 10.5224 8.86992 11.3562 8.03616L13.1231 6.26922C14.1435 5.24883 15.8023 5.24883 16.8227 6.26922L17.2343 6.68087C18.2547 7.70126 18.2547 9.36006 17.2343 10.3805L17.2361 10.377ZM10.0166 14.6801L14.215 10.4816C14.5499 10.1467 14.5499 9.60425 14.215 9.26761C13.8801 8.93271 13.3376 8.93271 13.001 9.26761L8.80255 13.4661C8.46765 13.801 8.46765 14.3434 8.80255 14.6801C9.13745 15.015 9.67991 15.015 10.0166 14.6801ZM12.0853 12.8573L10.6462 14.2963C10.6131 14.4568 10.5346 14.6103 10.4108 14.7341L8.64208 16.5028C8.30194 16.843 7.74901 16.843 7.40888 16.5028L6.99723 16.0912C6.6571 15.751 6.6571 15.1981 6.99723 14.858L8.76592 13.0893C8.87755 12.9777 9.01186 12.9061 9.15489 12.8678L10.6218 11.4008C9.63107 10.8688 8.36823 11.0206 7.53447 11.8544L5.76578 13.623C4.74539 14.6434 4.74539 16.3022 5.76578 17.3226L6.17743 17.7343C7.19782 18.7547 8.85662 18.7547 9.87701 17.7343L11.644 15.9673C12.4847 15.1266 12.6312 13.8515 12.0853 12.8573Z\" fill=\"white\" \/>\n<\/g>\n<\/g>\n<defs>\n\n<rect width=\"23\" height=\"23\" fill=\"white\" \/>\n\n\n<rect width=\"13\" height=\"13\" fill=\"white\" transform=\"translate(5 5.5)\" \/>\n\n<\/defs>\n<\/svg>\n<\/span><\/a><\/li><\/ul><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6c95aec6 e-con-full oh-expertise-x__body e-flex e-con e-child\" data-id=\"6c95aec6\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-37826417 elementor-widget elementor-widget-text-editor\" data-id=\"37826417\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>An effective demand planning model starts with sound business design decisions. Historical data quality, calculation granularity, product segmentation and the level of automation all determine forecast reliability. In this article, we explore the key features of a demand planning model in Anaplan, along with the architecture and configuration choices that make it easier for teams to adopt.<\/p><h2 id=\"classification\" class=\"anchor-target\">1. Classification, product linking and historical data cleansing<\/h2><p>The first critical element is the quality of the sales signal. Unprocessed historical data introduces bias into every downstream calculation. This is one of the most underestimated areas of investment during the early stages of a project. We configure product linking features to maintain historical continuity across product code changes, product substitutions and cannibalisation. This approach is essential when product portfolios evolve regularly.<\/p><p> <\/p><p>We model plausibility ranges using configurable dynamic thresholds. These identify outliers and trigger automatic corrections. Promotional effects are captured and separated from the baseline sales signal.<\/p><p> <\/p><p>We also incorporate the reconstruction of lost sales during stockouts. Unfulfilled sales are estimated to avoid underestimating actual demand. The result is a linked, cleansed historical dataset that can be used directly for statistical forecasting.<\/p><p> <\/p><p>In our experience, product lifecycle management and ABC\/XYZ segmentation are fundamental pillars of the model. ABC measures each product&#8217;s contribution in terms of volume and revenue. XYZ assesses demand volatility using the coefficient of variation in sales. These segments inform data cleansing rules and help prioritise planner alerts. They also guide sales signal modelling, including the choice of statistical algorithms, the use of Machine Learning and the need for manual review. Without well-calibrated segmentation, maintaining the model quickly becomes unmanageable as the product portfolio grows.<\/p><h2 id=\"previsions\" class=\"anchor-target\">2. Statistical forecasting, Machine Learning and Best Fit in Anaplan<\/h2><p>The value of a forecasting engine depends primarily on how appropriately it is configured. It is not simply a matter of using more complex models. Statistical models cover most forecasting requirements, typically around 80% of SKUs depending on the portfolio. They account for seasonality and trends through moving averages, exponential smoothing and model families such as ETS and ARIMA.<\/p><p>At OneHive, our <a href=\"https:\/\/one-hive.com\/en\/expertises\/supply-chain-expertise\/demand-planning\/\">demand planning<\/a> consultants configure Anaplan Forecaster according to demand profiles, product segments and the performance indicators being monitored. They adapt the parameters to the specific characteristics of each portfolio to improve forecast accuracy. A standard implementation of the forecasting engine is therefore not enough to guarantee reliable results.<\/p><p> <\/p><p>Machine Learning is used for segments where it can deliver measurable improvements. Its application is not limited to erratic demand patterns. It can also incorporate explanatory variables such as promotions, cannibalisation, calendar effects and specific product characteristics. Depending on the context, a dedicated model can deliver a 3 to 5 percentage point improvement in Forecast Accuracy, subject to confirmation against the results of the relevant project.<\/p><p> <\/p><p>We configure the Best Fit functionality at node level, according to client requirements. Anaplan runs several model families in parallel and automatically selects the best-performing candidate. The selection metric can be configured using indicators such as MAPE, WAPE, RMSE or bias. RMSE is our default starting point.<\/p><p> <\/p><p>We also use ABC\/XYZ segmentation to guide model selection. An A\/X product with stable demand should not be treated in the same way as a C\/Z product with volatile demand. Demand planning teams retain the ability to manually override the selected model when business circumstances require it.<\/p><p> <\/p><p>We continuously measure forecast quality through backtesting. These analyses are based on validation periods defined in advance. Forecast Accuracy dashboards identify deviations and help teams refine model parameters over time.<\/p><h2 id=\"promotions\" class=\"anchor-target\">3. Promotions, events and product lifecycle management<\/h2><p>Promotional planning is an area of business design that is often underestimated. Before any configuration begins, the promotional mechanics must be clearly defined: offer type, discount depth, duration, channel, customer and the category of products concerned. A promotion on a fast-moving SKU does not have the same effect as a clearance campaign or a new product launch. When poorly modelled, these activities distort the statistical baseline and forecasts for subsequent campaigns.<\/p><p> <\/p><p>At OneHive, we build dedicated modules to separate promotional uplift from the baseline sales signal. Bulk integration of promotional activities relies on configurable rules. The model proposes forecast volumes based on the type of promotion, channel, customer and product category. Execution tracking then makes it possible to compare actual sales against forecasts and measure the performance of each event.<\/p><p> <\/p><p>We also feed promotional data back into Machine Learning models. This approach captures halo effects, cannibalisation and promotional uplift. It also accounts for changes in demand before and after a promotional event. Forecast accuracy improves as the promotional history becomes richer.<br\/><\/p><p> <\/p><p>We manage the product lifecycle directly within the model. Product launches can be configured manually or automatically. Two approaches are possible: applying a volume to a launch curve or using a comparable product to reconstruct historical demand and calculate a baseline forecast. Cannibalisation mechanisms can be configured between SKUs. End-of-life products are managed using dedicated decline curves to progressively reduce planned volumes.<\/p><h2 id=\"ajustement\" class=\"anchor-target\">4. Forecast adjustments and scenario planning<\/h2><p>The granularity at which the sales signal is managed is one of the most important design decisions. Working at too detailed a level multiplies manual activities without adding value. Working at too aggregated a level hides useful demand signals. We configure top-down and bottom-up approaches according to business needs: some adjustments are made at a detailed level, while others are performed at an aggregated level. Combining both approaches within the same model is essential for an efficient review process.<br\/><\/p><p> <\/p><p>We configure multiple scenarios, including baseline, optimistic and pessimistic. Each scenario incorporates business assumptions relating to pricing, marketing budgets and supply constraints. Differences between scenarios are displayed in absolute or relative terms, with contributions broken down by segment. The analysis metric is configurable (kg, units, cases, revenue or euros), allowing teams to quickly assess the impact of their assumptions on demand and service levels.<\/p><h2 id=\"revue\" class=\"anchor-target\">5. Demand review and alignment with S&#038;OP<\/h2><p>We structure a shared validation process that connects local teams with a centralised view. Bottom-up forecasts submitted by countries or business units are consolidated and compared with the top-down perspective derived from global objectives. Differences are made visible and provide the basis for consensus decisions during Demand Review meetings. For our clients, this structure significantly reduces the time required to prepare these meetings. On some projects, the time needed to complete the <a href=\"https:\/\/one-hive.com\/en\/resources\/articles\/sop-anaplan-les-meilleures-pratiques-darchitecture-et-de-modelisation\/\">S&#038;OP<\/a> cycle has been reduced by more than 30%.<br\/><\/p><p> <\/p><p>Consolidation covers multiple planning horizons, from short-term operational requirements to long-term budgeting. It incorporates the financial perspective, including revenue forecasting and margins, and supports the development of a consistent overall plan, from the Demand Plan to the Supply Plan and ultimately the Financial Plan. Gaps between commercial ambitions and operational realities are identified, measured and translated into decisions and actions.<\/p><h2>Conclusion<\/h2><p>An effective demand planning application is not built around an algorithm alone. It relies on three foundations: reliable, cleansed historical data; forecasting models configured for each demand profile; and structured management of commercial events and the product lifecycle.<br\/><\/p><p> <\/p><p>Anaplan provides the technical framework needed to bring these components together in a consistent, auditable application connected to the S&#038;OP process. The value delivered then depends on the quality of the business design, the precision of the configuration and how effectively the process is embedded in teams&#8217; day-to-day practices. A high-performing model is therefore about more than its features. It must also be understood, used and maintained by the teams responsible for managing demand.<br\/><\/p><p> <\/p><p>In practice, we consistently observe a progressive improvement in forecast accuracy over the 6 to 12 months following implementation. The most successful projects achieve Forecast Accuracy levels above 90% at S&#038;OP granularity, with a bias below 5%. This progress does not simply reflect an immediate improvement in statistical performance. It also demonstrates the gradual adoption of the model by teams and the cumulative benefits of historical data cleansing mechanisms.<br\/><\/p><p> <\/p><p>Are you building or improving your demand planning model in Anaplan? <a href=\"https:\/\/one-hive.com\/en\/contact-us\/\">Get in touch with our experts<\/a>.<\/p><div> <\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-56742f49 e-con-full oh-section-equipe oh-bg--oxford-blue e-flex e-con e-parent\" data-id=\"56742f49\" data-element_type=\"container\" data-e-type=\"container\" data-theme=\"dark\">\n\t\t\t\t<div class=\"elementor-element elementor-element-45f5a7ca elementor-widget__width-initial elementor-widget elementor-widget-oh-heading\" data-id=\"45f5a7ca\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"oh-heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<header 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font-weight:500;\n}\n#uc_onehive_stacking_cards_elementor_5a8a2b14 .uc-message{\n  font-weight:500;\n}\n.elementor-repeater-item-w4kz6 {\n  padding-top: calc(1 * var(--card-top-offset))!important;\n}\n\n\n<\/style>\n\n<div id=\"uc_onehive_stacking_cards_elementor_5a8a2b14\" data-editor=\"no\" data-errors=\"false\" data-show-section=\"false\" class=\"ue_stacking_cards_widget uc_creative_buttons  \"   dir=\"ltr\">\n  <div class=\"ue_cards_wrapper uc-items-wrapper\">\n    <div class=\"ue_stacking_card_item elementor-repeater-item-w4kz6 ueitem-1\" data-source=\"normal\" data-id=\"\">\n                          <div class=\"ue_card_content ue_card_content--contact-left\" >\n    \n                  <div class=\"ue_contact_panel ue_contact_panel--left\">\n                      <a class=\"ue_contact_btn ue_contact_btn--linkedin\" href=\"https:\/\/www.linkedin.com\/in\/etienne-pethelaz-434300145\/\" target=\"_blank\" rel=\"noopener noreferrer\" aria-label=\"LinkedIn de Etienne Pethelaz\">\n              <span class=\"ue_contact_icon\" aria-hidden=\"true\">\n                <svg viewBox=\"0 0 24 24\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" focusable=\"false\">\n                  <path d=\"M4.98 3.5A2.48 2.48 0 1 1 2.5 1a2.48 2.48 0 0 1 2.48 2.5ZM.5 8.5h4V23h-4Zm7 0h3.82v1.97h.05c.53-1.02 1.83-2.05 3.74-2.05 3.99 0 4.74 2.6 4.74 5.99V23h-4v-6.62c0-1.58-.03-3.62-2.21-3.62-2.21 0-2.55 1.72-2.55 3.5V23h-4Z\" fill=\"currentColor\"\/>\n                <\/svg>\n              <\/span>\n            <\/a>\n                                <a class=\"ue_contact_btn ue_contact_btn--email\" href=\"mailto:contact@one-hive.com\" aria-label=\"Contacter Etienne Pethelaz par e-mail\">\n              <span class=\"ue_contact_icon\" aria-hidden=\"true\">\n                <svg viewBox=\"0 0 24 24\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" focusable=\"false\">\n                  <path d=\"M4 4h16a2 2 0 0 1 2 2v12a2 2 0 0 1-2 2H4a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2Zm0 4v10h16V8l-8 5Z\" fill=\"currentColor\"\/>\n                <\/svg>\n              <\/span>\n            <\/a>\n                  <\/div>\n            <div class=\"ue_content_left\">\n                <h1 class=\"ue_sc_title\">Etienne Pethelaz<\/h1>        <div class=\"ue_sc_desc\"><h3 class=\"oh-bio-item__role\">Senior Consultant<\/h3><div class=\"oh-bio-item__bio\"><p data-pm-slice=\"1 1 []\">A graduate of Arts et M\u00e9tiers ParisTech with a Master\u2019s degree in Mechanical Engineering, Etienne supports OneHive\u2019s clients in their Supply Chain transformation and planning optimization projects. As a certified Anaplan Model Builder, he specializes in Demand Planning, Supply Planning, S&amp;OP, and inventory optimization, combining functional expertise with technical capabilities. With experience across a wide range of Supply Chain and operations strategy projects, he helps organizations improve their planning processes, enhance operational performance, and implement tailored digital solutions to support business transformation.<\/p>\n<\/div><\/div>\t          <\/div>\n\t          <figure>\n                      <img decoding=\"async\" class=\"ue_sc_img\" src=\"https:\/\/one-hive.com\/wp-content\/uploads\/2026\/10\/Etienne_PETHELAZ_OneHive_paysage-1.png\" alt=\"Image description\"  src=\"https:\/\/one-hive.com\/wp-content\/uploads\/2026\/10\/Etienne_PETHELAZ_OneHive_paysage-1.png\" alt=\"\" title=\"\">\n            \t\t<\/figure>\n            \n      \n      \n  <\/div>\n<\/div>\n\n\n  <\/div>\n<\/div>\n<!-- end OneHive Stacking Cards -->\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2852007 e-con-full e-flex e-con e-parent\" data-id=\"2852007\" data-element_type=\"container\" data-e-type=\"container\" data-theme=\"light\">\n\t\t\t\t<div class=\"elementor-element elementor-element-31097897 elementor-widget elementor-widget-oh-featuring\" data-id=\"31097897\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"oh-featuring.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<section class=\"oh-featuring\" data-oh-featuring><div class=\"oh-featuring__heading\"><p class=\"oh-categories\">Our latest news<\/p><\/div><div class=\"oh-featuring__slider swiper\" role=\"region\" aria-label=\"Contenus mis en avant\"><div class=\"swiper-wrapper\" role=\"list\"><article class=\"oh-featuring__card swiper-slide\" role=\"listitem\">\n    <div class=\"oh-featuring__card-body\">\n      <header class=\"oh-featuring__meta\">\n                  <p class=\"oh-featuring__meta-line\">Cas Client<\/p>\n              <\/header>\n\n              <img loading=\"lazy\" width=\"562\" height=\"320\" src=\"https:\/\/one-hive.com\/wp-content\/uploads\/2026\/10\/Format-Card-Sodiaal-1.png\" class=\"oh-featuring__thumb wp-post-image\" alt=\"\" loading=\"lazy\" decoding=\"async\" srcset=\"https:\/\/one-hive.com\/wp-content\/uploads\/2026\/10\/Format-Card-Sodiaal-1.png 562w, https:\/\/one-hive.com\/wp-content\/uploads\/2026\/10\/Format-Card-Sodiaal-1-300x171.png 300w\" sizes=\"auto, (max-width: 562px) 100vw, 562px\" \/>      \n      <div class=\"oh-featuring__content\">\n                  <p class=\"oh-featuring__date oh-subtitle\">Industrial planning with Anaplan<\/p>\n        \n        <h3 class=\"oh-featuring__card-title\">\n          <a href=\"https:\/\/one-hive.com\/en\/case-studies\/industrial-planning-transforming-yoplaits-planning-with-anaplan\/\">Yoplait - Sodiaal<\/a>\n        <\/h3>\n\n                  <p class=\"oh-featuring__excerpt\">Discover how Yoplait optimizes industrial planning with Anaplan, combining production scheduling, inventory deployment and fresh dairy constraints.<\/p>\n        \n              <\/div>\n    <\/div>\n\n    <div class=\"oh-featuring__card-footer\">\n      <a class=\"oh-btn oh-btn--primary-light oh-btn--icon-only oh-btn--icon-svg-arrow\" href=\"https:\/\/one-hive.com\/en\/case-studies\/industrial-planning-transforming-yoplaits-planning-with-anaplan\/\" aria-label=\"Lire Yoplait - Sodiaal\"><\/a>\n    <\/div>\n  <\/article><article class=\"oh-featuring__card swiper-slide\" role=\"listitem\">\n    <div class=\"oh-featuring__card-body\">\n      <header class=\"oh-featuring__meta\">\n                  <p class=\"oh-featuring__meta-line\">Article \u2022 10 min<\/p>\n              <\/header>\n\n              <img loading=\"lazy\" width=\"1024\" height=\"407\" src=\"https:\/\/one-hive.com\/wp-content\/uploads\/2026\/09\/Articles-experts_HEADER-DESKTOP_1280x509_04-1024x407.png\" class=\"oh-featuring__thumb wp-post-image\" alt=\"\" loading=\"lazy\" decoding=\"async\" srcset=\"https:\/\/one-hive.com\/wp-content\/uploads\/2026\/09\/Articles-experts_HEADER-DESKTOP_1280x509_04-1024x407.png 1024w, https:\/\/one-hive.com\/wp-content\/uploads\/2026\/09\/Articles-experts_HEADER-DESKTOP_1280x509_04-300x119.png 300w, https:\/\/one-hive.com\/wp-content\/uploads\/2026\/09\/Articles-experts_HEADER-DESKTOP_1280x509_04-768x305.png 768w, https:\/\/one-hive.com\/wp-content\/uploads\/2026\/09\/Articles-experts_HEADER-DESKTOP_1280x509_04.png 1280w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/>      \n      <div class=\"oh-featuring__content\">\n                  <p class=\"oh-featuring__date oh-subtitle\">October 19, 2026<\/p>\n        \n        <h3 class=\"oh-featuring__card-title\">\n          <a href=\"https:\/\/one-hive.com\/en\/resources\/articles\/sop-process-designing-an-anaplan-application-that-delivers-real-value\/\">S&#038;OP process: designing an Anaplan application that delivers real value<\/a>\n        <\/h3>\n\n                  <p class=\"oh-featuring__excerpt\">Learn how to structure trade-offs, define the right planning granularity and make better decisions with Anaplan.<\/p>\n        \n              <\/div>\n    <\/div>\n\n    <div class=\"oh-featuring__card-footer\">\n      <a class=\"oh-btn oh-btn--primary-light oh-btn--icon-only oh-btn--icon-svg-arrow\" href=\"https:\/\/one-hive.com\/en\/resources\/articles\/sop-process-designing-an-anaplan-application-that-delivers-real-value\/\" aria-label=\"Lire S&#038;OP process: designing an Anaplan application that delivers real value\"><\/a>\n    <\/div>\n  <\/article><article class=\"oh-featuring__card swiper-slide\" role=\"listitem\">\n    <div class=\"oh-featuring__card-body\">\n      <header class=\"oh-featuring__meta\">\n                  <p class=\"oh-featuring__meta-line\">Cas Client<\/p>\n              <\/header>\n\n              <img loading=\"lazy\" width=\"321\" height=\"183\" src=\"https:\/\/one-hive.com\/wp-content\/uploads\/2026\/01\/Cas-Client_Cosme\u0301tique-beaute\u0301_CARD-DESKTOP_321x183.png\" class=\"oh-featuring__thumb wp-post-image\" alt=\"\" loading=\"lazy\" decoding=\"async\" srcset=\"https:\/\/one-hive.com\/wp-content\/uploads\/2026\/01\/Cas-Client_Cosme\u0301tique-beaute\u0301_CARD-DESKTOP_321x183.png 321w, https:\/\/one-hive.com\/wp-content\/uploads\/2026\/01\/Cas-Client_Cosme\u0301tique-beaute\u0301_CARD-DESKTOP_321x183-300x171.png 300w\" sizes=\"auto, (max-width: 321px) 100vw, 321px\" \/>      \n      <div class=\"oh-featuring__content\">\n                  <p class=\"oh-featuring__date oh-subtitle\">Supply Chain Planning<\/p>\n        \n        <h3 class=\"oh-featuring__card-title\">\n          <a href=\"https:\/\/one-hive.com\/en\/case-studies\/supply-chain-planning-transformation-in-the-cosmetics-industry\/\">Perfume and cosmetics group<\/a>\n        <\/h3>\n\n                  <p class=\"oh-featuring__excerpt\">Transformation of supply chain planning operations at a major French perfumes and cosmetics group, in partnership with Argon &amp; Co<\/p>\n        \n              <\/div>\n    <\/div>\n\n    <div class=\"oh-featuring__card-footer\">\n      <a class=\"oh-btn oh-btn--primary-light oh-btn--icon-only oh-btn--icon-svg-arrow\" href=\"https:\/\/one-hive.com\/en\/case-studies\/supply-chain-planning-transformation-in-the-cosmetics-industry\/\" aria-label=\"Lire Perfume and cosmetics group\"><\/a>\n    <\/div>\n  <\/article><\/div><\/div><\/section>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-57e8c92d e-con-full e-flex e-con e-parent\" data-id=\"57e8c92d\" data-element_type=\"container\" data-e-type=\"container\" data-theme=\"light\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1e7a8ca2 elementor-widget__width-initial elementor-widget elementor-widget-oh-faq\" data-id=\"1e7a8ca2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"oh-faq.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<section class=\"oh-faq\" data-oh-faq><div class=\"oh-faq__layout\"><div class=\"oh-faq__intro\"><h2 class=\"oh-faq__title\">FAQ<\/h2><p class=\"oh-faq__subtitle oh-subtitle\">Discover answers to key questions about our services and approach.<\/p><\/div><div class=\"oh-faq__items\" role=\"list\"><article class=\"oh-faq__item\" role=\"listitem\"><h3 class=\"oh-faq__question\"><button type=\"button\" class=\"oh-faq__toggle\" aria-expanded=\"false\" aria-controls=\"oh-faq-a-511347874-0\" id=\"oh-faq-q-511347874-0\"><span class=\"oh-faq__question-text\">What granularity should you use when configuring a demand planning model in Anaplan?<\/span><span class=\"oh-faq__icon\" aria-hidden=\"true\"><svg width=\"13\" height=\"13\" viewBox=\"0 0 13 13\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path class=\"oh-faq__icon-h\" d=\"M0 6.5H12.5\" stroke=\"#050022\" stroke-width=\"2\" \/><path class=\"oh-faq__icon-v\" d=\"M6.5 0L6.5 12.5\" stroke=\"#050022\" stroke-width=\"2\" \/><\/svg><\/span><\/button><\/h3><div class=\"oh-faq__answer\" id=\"oh-faq-a-511347874-0\" role=\"region\" aria-labelledby=\"oh-faq-q-511347874-0\" hidden><div class=\"oh-faq__answer-inner\"><p>Granularity depends on operational requirements. The calculation level is often more detailed than the review level: forecasts may be generated at SKU and point-of-sale level, then consolidated by product family or channel. Anaplan allows these levels to be adjusted dynamically without redesigning the model, making it easier for the planning process to evolve over time.<\/p><\/div><\/div><\/article><article class=\"oh-faq__item\" role=\"listitem\"><h3 class=\"oh-faq__question\"><button type=\"button\" class=\"oh-faq__toggle\" aria-expanded=\"false\" aria-controls=\"oh-faq-a-511347874-1\" id=\"oh-faq-q-511347874-1\"><span class=\"oh-faq__question-text\">Do you need to choose between statistical models and Machine Learning in Anaplan?<\/span><span class=\"oh-faq__icon\" aria-hidden=\"true\"><svg width=\"13\" height=\"13\" viewBox=\"0 0 13 13\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path class=\"oh-faq__icon-h\" d=\"M0 6.5H12.5\" stroke=\"#050022\" stroke-width=\"2\" \/><path class=\"oh-faq__icon-v\" d=\"M6.5 0L6.5 12.5\" stroke=\"#050022\" stroke-width=\"2\" \/><\/svg><\/span><\/button><\/h3><div class=\"oh-faq__answer\" id=\"oh-faq-a-511347874-1\" role=\"region\" aria-labelledby=\"oh-faq-q-511347874-1\" hidden><div class=\"oh-faq__answer-inner\"><p>No. The Best Fit approach allows both model families to be combined. Statistical models perform well with regular and seasonal demand patterns. Machine Learning adds value for erratic demand profiles or products with limited historical data. The best-performing model is automatically selected at node level, with the option to override the selection manually.<\/p><\/div><\/div><\/article><article class=\"oh-faq__item\" role=\"listitem\"><h3 class=\"oh-faq__question\"><button type=\"button\" class=\"oh-faq__toggle\" aria-expanded=\"false\" aria-controls=\"oh-faq-a-511347874-2\" id=\"oh-faq-q-511347874-2\"><span class=\"oh-faq__question-text\">How does Anaplan handle stockouts when calculating demand forecasts?<\/span><span class=\"oh-faq__icon\" aria-hidden=\"true\"><svg width=\"13\" height=\"13\" viewBox=\"0 0 13 13\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path class=\"oh-faq__icon-h\" d=\"M0 6.5H12.5\" stroke=\"#050022\" stroke-width=\"2\" \/><path class=\"oh-faq__icon-v\" d=\"M6.5 0L6.5 12.5\" stroke=\"#050022\" stroke-width=\"2\" \/><\/svg><\/span><\/button><\/h3><div class=\"oh-faq__answer\" id=\"oh-faq-a-511347874-2\" role=\"region\" aria-labelledby=\"oh-faq-q-511347874-2\" hidden><div class=\"oh-faq__answer-inner\"><p>Stockouts are identified in historical data and trigger a lost sales reconstruction mechanism. This correction estimates actual unmet demand and prevents the statistical sales signal from being biased downwards during affected periods. This is particularly important for fast-moving SKUs.<\/p><\/div><\/div><\/article><article class=\"oh-faq__item\" role=\"listitem\"><h3 class=\"oh-faq__question\"><button type=\"button\" class=\"oh-faq__toggle\" aria-expanded=\"false\" aria-controls=\"oh-faq-a-511347874-3\" id=\"oh-faq-q-511347874-3\"><span class=\"oh-faq__question-text\">What is the difference between a forecast, consensus and a plan in an Anaplan S&amp;OP cycle?<\/span><span class=\"oh-faq__icon\" aria-hidden=\"true\"><svg width=\"13\" height=\"13\" viewBox=\"0 0 13 13\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path class=\"oh-faq__icon-h\" d=\"M0 6.5H12.5\" stroke=\"#050022\" stroke-width=\"2\" \/><path class=\"oh-faq__icon-v\" d=\"M6.5 0L6.5 12.5\" stroke=\"#050022\" stroke-width=\"2\" \/><\/svg><\/span><\/button><\/h3><div class=\"oh-faq__answer\" id=\"oh-faq-a-511347874-3\" role=\"region\" aria-labelledby=\"oh-faq-q-511347874-3\" hidden><div class=\"oh-faq__answer-inner\"><p>The forecast is the statistical prediction or the forecast adjusted by demand planning teams. The consensus is the version collectively validated during the Demand Review, following alignment between bottom-up and top-down perspectives. The plan is the final decision, incorporating supply constraints and financial commitments. Anaplan makes it possible to track and compare all three levels continuously.<\/p><\/div><\/div><\/article><article class=\"oh-faq__item\" role=\"listitem\"><h3 class=\"oh-faq__question\"><button type=\"button\" class=\"oh-faq__toggle\" aria-expanded=\"false\" aria-controls=\"oh-faq-a-511347874-4\" id=\"oh-faq-q-511347874-4\"><span class=\"oh-faq__question-text\">How can promotions be incorporated without distorting the statistical baseline?<\/span><span class=\"oh-faq__icon\" aria-hidden=\"true\"><svg width=\"13\" height=\"13\" viewBox=\"0 0 13 13\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path class=\"oh-faq__icon-h\" d=\"M0 6.5H12.5\" stroke=\"#050022\" stroke-width=\"2\" \/><path class=\"oh-faq__icon-v\" d=\"M6.5 0L6.5 12.5\" stroke=\"#050022\" stroke-width=\"2\" \/><\/svg><\/span><\/button><\/h3><div class=\"oh-faq__answer\" id=\"oh-faq-a-511347874-4\" role=\"region\" aria-labelledby=\"oh-faq-q-511347874-4\" hidden><div class=\"oh-faq__answer-inner\"><p>Promotions are separated from the sales signal before statistical calculations are performed. A dedicated module models incremental demand according to the type of event, channel and customer. The promotional forecast is then added back on top of the baseline. This separation maintains the reliability of the forecasting model outside promotional periods.<\/p><\/div><\/div><\/article><\/div><div class=\"oh-faq__cta-block\"><p class=\" oh-subtitle\">Didn\u2019t find what you were looking for?<\/p><a class=\"oh-btn oh-btn--secondary\" href=\"#\">Contact us<\/a><\/div><\/div><\/section>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a830dd5 elementor-widget__width-initial elementor-widget elementor-widget-oh-contact-card\" data-id=\"a830dd5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"oh-contact-card.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<section class=\"oh-contact-card\" style=\"--oh-contact-card-fallback:#050022;--oh-contact-card-bg-mobile:url(&#039;https:\/\/one-hive.com\/wp-content\/uploads\/2025\/10\/CARDS-CONTACT_MOBILE.png&#039;);--oh-contact-card-bg-desktop:url(&#039;https:\/\/one-hive.com\/wp-content\/uploads\/2025\/10\/CARDS-CONTACT.png&#039;);--oh-contact-card-bg-position:center right\"><div class=\"oh-contact-card__inner\"><h2 class=\"oh-contact-card__title\">Ready to transform your planning processes?<\/h2><p class=\"oh-contact-card__text oh-subtitle\">Let\u2019s explore how we can make your project a success.<\/p><a class=\"oh-contact-card__cta oh-btn oh-btn--primary-light\" href=\"https:\/\/one-hive.com\/nous-contacter\/\">Connect with our experts<\/a><\/div><\/section>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>From historical data cleansing to statistical forecasting and Machine Learning, explore the design and configuration choices behind an effective demand planning model in Anaplan.<\/p>\n","protected":false},"author":5,"featured_media":7016,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"expertise":[73,31],"sector":[65],"class_list":["post-7046","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-non-categorise"],"acf":[],"_links":{"self":[{"href":"https:\/\/one-hive.com\/en\/wp-json\/wp\/v2\/posts\/7046","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/one-hive.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/one-hive.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/one-hive.com\/en\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/one-hive.com\/en\/wp-json\/wp\/v2\/comments?post=7046"}],"version-history":[{"count":1,"href":"https:\/\/one-hive.com\/en\/wp-json\/wp\/v2\/posts\/7046\/revisions"}],"predecessor-version":[{"id":7048,"href":"https:\/\/one-hive.com\/en\/wp-json\/wp\/v2\/posts\/7046\/revisions\/7048"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/one-hive.com\/en\/wp-json\/wp\/v2\/media\/7016"}],"wp:attachment":[{"href":"https:\/\/one-hive.com\/en\/wp-json\/wp\/v2\/media?parent=7046"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/one-hive.com\/en\/wp-json\/wp\/v2\/categories?post=7046"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/one-hive.com\/en\/wp-json\/wp\/v2\/tags?post=7046"},{"taxonomy":"expertise","embeddable":true,"href":"https:\/\/one-hive.com\/en\/wp-json\/wp\/v2\/expertise?post=7046"},{"taxonomy":"sector","embeddable":true,"href":"https:\/\/one-hive.com\/en\/wp-json\/wp\/v2\/sector?post=7046"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}