{"id":11977,"date":"2026-09-14T10:17:20","date_gmt":"2026-09-14T15:17:20","guid":{"rendered":"https:\/\/innovapps.net\/?p=11977"},"modified":"2026-09-14T10:24:31","modified_gmt":"2026-09-14T15:24:31","slug":"demand-forecasting-sap-without-ibp","status":"publish","type":"post","link":"https:\/\/innovapps.net\/en\/blog\/demand-forecasting-sap-without-ibp\/","title":{"rendered":"Demand forecasting in SAP without IBP: why your planner is still in Excel (and how to get out)"},"content":{"rendered":"<style> .ia-art{--navy:#204671;--navy-deep:#16314F;--turq:#24BFCF;--white:#FAFDFF;--blue:#1E8FC9;--grad:linear-gradient(135deg,#1E8FC9,#24BFCF);--ink:#22303f;--muted:#5b6b7b;--line:#e2e9f0;--cool:#f4f8fb;font-family:'Lato',system-ui,sans-serif;color:var(--ink);font-size:1.0625rem;line-height:1.75;max-width:760px;margin:0 auto} .ia-art *,.ia-art *::before,.ia-art *::after{border-radius:0;box-sizing:border-box} .ia-art h1,.ia-art h2,.ia-art 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.ia-scn-n{font-family:'Ubuntu',system-ui,sans-serif;font-size:.78rem;font-weight:700;letter-spacing:.14em;text-transform:uppercase;color:var(--turq);display:block;margin-bottom:.2rem} .ia-scn p{margin:0} .ia-ctablock{background:var(--navy);color:var(--white);padding:2rem 1.75rem;margin:3rem 0;display:flex;align-items:center;gap:1.5rem;flex-wrap:wrap;border-bottom:5px solid var(--turq)} .ia-ctablock p{margin:0;flex:1 1 260px;font-size:1.05rem;color:var(--white)} .ia-ctablock p b{color:#fff} .ia-cta{display:inline-block;background:var(--grad);color:#fff!important;font-family:'Ubuntu',system-ui,sans-serif;font-weight:700;font-size:1rem;padding:.95rem 1.9rem;border-bottom:0!important;white-space:nowrap;transition:filter .18s,transform .18s} .ia-cta:hover{filter:brightness(1.09);transform:translateY(-2px);color:#fff!important} .ia-cta:focus-visible{outline:3px solid var(--white);outline-offset:3px} .ia-ph{background:#FFF6D6;border-left:5px solid #E0A800;padding:.9rem 1.1rem;font-size:.93rem;color:#5b4a12;margin:1.5rem 0} .ia-ph b{color:#5b4a12} .ia-faq h3{border-left:3px solid var(--turq);padding-left:.75rem} @media (max-width:640px){.ia-art h2{padding-left:.7rem}.ia-ctablock{padding:1.5rem 1.25rem}.ia-cta{width:100%;text-align:center;white-space:normal}} @media (prefers-reduced-motion:reduce){.ia-art *{transition:none!important}} .ia-tbl{overflow-x:auto;margin:1.7rem 0;border:1px solid var(--line);-webkit-overflow-scrolling:touch} .ia-tbl table{border-collapse:collapse;width:100%;min-width:540px;font-size:.95rem} .ia-tbl th,.ia-tbl td{text-align:left;padding:.72rem .95rem;border-bottom:1px solid var(--line);vertical-align:top;line-height:1.55} .ia-tbl thead th{font-family:'Ubuntu',system-ui,sans-serif;background:var(--navy);color:var(--white);font-weight:700;border-bottom:0} .ia-tbl tbody tr:nth-child(even){background:var(--cool)} .ia-tbl tbody tr:last-child td{border-bottom:0} .ia-art figure{margin:0} .ia-art img{max-width:100%;height:auto;display:block;border:1px solid var(--line)} @media (max-width:640px){.ia-tbl table{font-size:.88rem}} <\/style>\n<div class=\"ia-art\">\n<p class=\"ia-lede\">Audit how planning actually works in a mid-sized manufacturer running SAP and the same spreadsheet turns up almost every time. It isn&#8217;t there because nobody knows SAP. It&#8217;s there because the standard makes it hard to compare methods and awkward to keep the plan where it belongs. This article is about closing that gap without buying SAP IBP.<\/p>\n<h2>Demand forecasting in SAP: the short answer<\/h2>\n<div class=\"ia-cap\">\n<p>Yes, you can do <strong>demand forecasting in SAP<\/strong> without buying SAP IBP. SAP ECC and S\/4HANA ship consumption-based forecasting (transactions <strong>MP30<\/strong>, <strong>MP38<\/strong>, <strong>MP31<\/strong>, <strong>MP33<\/strong>, with the model set in the <em>Forecasting<\/em> view of the material master) and S&amp;OP planning to save and compare plan versions. The limit isn&#8217;t a missing calculation engine: it&#8217;s that the engine works material by material, with little comparative visibility and no screen where a planner can see several models side by side and decide. That gap is what Excel fills today.<\/p>\n<\/p><\/div>\n<p>One figure to size up why it matters: in industrial and B2B manufacturing, typical SKU-level forecast error runs between <strong>20% and 40%<\/strong> MAPE \u2014<em>Mean Absolute Percentage Error<\/em>, the average percentage gap between what you forecast and what actually happened\u2014 and each additional month of horizon usually adds <strong>2\u20135 points<\/strong> of error (<a href=\"https:\/\/umbrex.com\/resources\/company-analysis\/supply-chain-logistics\/forecast-accuracy-by-product\/\" target=\"_blank\" rel=\"noopener nofollow\">Umbrex<\/a>).<\/p>\n<figure class=\"ia-fig\"> <img loading=\"lazy\" src=\"https:\/\/innovapps.net\/wp-content\/uploads\/three-ways-demand-forecasting-sap-excel-mp30-multi-model.png\" alt=\"Three ways to run demand forecasting in SAP compared: parallel Excel, standard MP30\/MP38, and a multi-model monitor on standard S&amp;OP\" width=\"1200\" height=\"675\" loading=\"lazy\" decoding=\"async\" \/><figcaption>Three ways to forecast in SAP: the same history, three different ways to turn it into a plan MRP can use.<\/figcaption><\/figure>\n<h2>1. The uncomfortable diagnosis: your planner doesn&#8217;t use Excel out of laziness<\/h2>\n<p>Audit the real planning process in a mid-sized company running SAP and the finding is usually the same. There&#8217;s a file. It has a tab per product family, last year&#8217;s sales, a growth percentage negotiated in a meeting, and manual adjustments only the person who made them understands. And it works just well enough that nobody questions it.<\/p>\n<p>That file doesn&#8217;t exist because the planner doesn&#8217;t know SAP. It exists because in Excel they can do three things the standard makes hard: <strong>see several years of history at once<\/strong>, <strong>try more than one calculation method<\/strong> and <strong>change their mind without raising a development request<\/strong>. It&#8217;s a pattern that repeats across mid-sized industrial companies with SAP in production, whatever the country or the sector.<\/p>\n<p>The problem with parallel Excel isn&#8217;t the spreadsheet. It&#8217;s that the plan <strong>doesn&#8217;t stay in the system<\/strong>: no comparable versions, no auditable plan vs. actual, no way to know which method got last quarter right, and all the knowledge living in one person. It&#8217;s the same pattern we described when <a href=\"https:\/\/innovapps.net\/en\/blog\/mrp-planning-sap-agribusiness\/\">planning the next campaign in Excel<\/a> in agribusiness.<\/p>\n<p>And that person is getting harder to find. Demand for supply chain profiles outstrips supply by <strong>6:1<\/strong>, rising to <strong>9:1<\/strong> in forecasting roles, and only <strong>8%<\/strong> of companies say they have enough internal talent (<a href=\"https:\/\/www.sdcexec.com\/professional-development\/hiring\/article\/22959590\/scope-recruiting-demand-planning-in-2026-why-the-job-has-changed-faster-than-the-talent-pool\" target=\"_blank\" rel=\"noopener nofollow\">Supply &amp; Demand Chain Executive, 2026<\/a>). A process that depends on a file and on whoever maintains it is an operational risk, not a method.<\/p>\n<h2>2. What SAP standard really gives you, and where it gets stuck<\/h2>\n<p>Let&#8217;s be fair to the standard: SAP does have forecasting. What it has is <strong>consumption-based forecasting, material by material, with very little conversation<\/strong>.<\/p>\n<p><strong>What exists:<\/strong><\/p>\n<ul>\n<li><strong>MP30<\/strong> runs the forecast for one material; <strong>MP38<\/strong> runs it in bulk; <strong>MP31\/MP33<\/strong> handle maintenance and review.<\/li>\n<li>The model (constant, trend, seasonal, seasonal with trend, automatic) is set in the <strong>Forecasting view<\/strong> of <a href=\"https:\/\/innovapps.net\/en\/blog\/master-data-rules-sap\/\">the material master<\/a> (<a href=\"https:\/\/community.sap.com\/t5\/enterprise-resource-planning-q-a\/forecasting-stock-levels\/qaq-p\/10083005\" target=\"_blank\" rel=\"noopener nofollow\">SAP Community<\/a>).<\/li>\n<li><strong>SOP \/ flexible S&amp;OP<\/strong> lets you save plan versions and compare time series, in both ECC and S\/4HANA.<\/li>\n<\/ul>\n<p><strong>Where it gets stuck<\/strong>, according to what consultants and key users report in the SAP Community:<\/p>\n<div class=\"ia-tbl\">\n<table>\n<thead>\n<tr>\n<th>Friction<\/th>\n<th>What happens in practice<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Mandatory run per material<\/td>\n<td>With MRP type VV, total consumption-based forecasting is mandatory and MP30\/MP38 must be run at a frequency defined material by material (<a href=\"https:\/\/community.sap.com\/t5\/enterprise-resource-planning-q-a\/mrp-forecast-based-planning-with-a-complete-example\/qaq-p\/8643211\" target=\"_blank\" rel=\"noopener nofollow\">SAP Community<\/a>)<\/td>\n<\/tr>\n<tr>\n<td>Rigid rounding<\/td>\n<td>The rounding threshold is fixed at 1 for MP30\/MP38\/MM02, which distorts low-volume items (<a href=\"https:\/\/community.sap.com\/t5\/enterprise-resource-planning-q-a\/forecast-mp30-for-materials-only-integer\/qaq-p\/10043163\" target=\"_blank\" rel=\"noopener nofollow\">SAP Community<\/a>)<\/td>\n<\/tr>\n<tr>\n<td>Unplanned requirements that aren&#8217;t consumed<\/td>\n<td>The UnplRq generated aren&#8217;t consumed by sales orders; they have to be copied by hand into MD61\/PIR and leave duplicate lines in MD04 (<a href=\"https:\/\/community.sap.com\/t5\/enterprise-resource-planning-q-a\/forecasting-with-mrp-type-pd\/qaq-p\/11512241\" target=\"_blank\" rel=\"noopener nofollow\">SAP Community<\/a>)<\/td>\n<\/tr>\n<tr>\n<td>No model comparison<\/td>\n<td>There&#8217;s no screen where a planner sees nine methods for the same material and plant and picks one on the evidence<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/div>\n<p>And there&#8217;s a strategic direction worth putting on the table: SAP states that SOP and flexible S&amp;OP are <strong>not strategic tools for S\/4HANA<\/strong> and that their intended successor is SAP IBP (<a href=\"https:\/\/learning.sap.com\/courses\/exploring-advanced-production-planning-with-sap-s-4hana-pp-ds\/understanding-principles-and-tools-for-demand-planning-1\" target=\"_blank\" rel=\"noopener nofollow\">SAP Learning<\/a>). That doesn&#8217;t mean they stop working today; it means SAP&#8217;s roadmap for advanced planning points at a different product.<\/p>\n<figure class=\"ia-fig\"> <img loading=\"lazy\" src=\"https:\/\/innovapps.net\/wp-content\/uploads\/demand-forecast-flow-sap-history-to-mrp.png\" alt=\"Demand forecast flow in SAP, from consumption and sales history to MRP, with the two points where parallel Excel appears today marked with a dashed outline\" width=\"1200\" height=\"675\" loading=\"lazy\" decoding=\"async\" \/><figcaption>The path from forecast to MRP. Dashed outlines mark the two points where parallel Excel slips in today.<\/figcaption><\/figure>\n<h2>3. Before you change anything: find out whether your forecast error is normal<\/h2>\n<p>This is the section that decides which problem you actually have, and therefore what to do next. Without a baseline, any discussion about tools is a discussion about opinions.<\/p>\n<p>The reasoning is simple. Calculate your real error over the last closed months and compare it with the typical band for your sector:<\/p>\n<ul>\n<li><strong>If you&#8217;re inside the band<\/strong>, your problem isn&#8217;t the calculation: it&#8217;s the process. The number is fine, but it lives in a file, has no versions and depends on one person. What you need is to get it into SAP, not a better statistical engine.<\/li>\n<li><strong>If you&#8217;re well above the band<\/strong>, you do have a method problem. And the cheapest way out isn&#8217;t buying a more expensive engine: it&#8217;s <strong>running several models against your own history and keeping the one that deviates least<\/strong> \u2014 exactly the approach in section 6.<\/li>\n<\/ul>\n<p>That&#8217;s why the sector benchmark matters: a forecast with 30% error is neither good nor bad in the abstract. In apparel it would be excellent; in stable FMCG, a serious problem.<\/p>\n<div class=\"ia-tbl\">\n<table>\n<thead>\n<tr>\n<th>Sector \/ demand type<\/th>\n<th>Typical MAPE<\/th>\n<th>Source<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>FMCG \/ stable-demand staples (A\/X SKUs)<\/td>\n<td>10\u201325% (25\u201335% on promotions)<\/td>\n<td><a href=\"https:\/\/umbrex.com\/resources\/company-analysis\/supply-chain-logistics\/forecast-accuracy-by-product\/\" target=\"_blank\" rel=\"noopener nofollow\">Umbrex<\/a><\/td>\n<\/tr>\n<tr>\n<td>Consumer packaged goods (CPG)<\/td>\n<td>15\u201325% acceptable<\/td>\n<td><a href=\"https:\/\/imperiascm.com\/blog\/mape-and-supply-chain-forecasting-how-to-measure-and-enhance-accuracy\" target=\"_blank\" rel=\"noopener nofollow\">Imperia SCM<\/a><\/td>\n<\/tr>\n<tr>\n<td>Industrial \/ project-driven B2B<\/td>\n<td>20\u201340% at SKU level (better at family level)<\/td>\n<td><a href=\"https:\/\/umbrex.com\/resources\/company-analysis\/supply-chain-logistics\/forecast-accuracy-by-product\/\" target=\"_blank\" rel=\"noopener nofollow\">Umbrex<\/a><\/td>\n<\/tr>\n<tr>\n<td>Fashion \/ apparel (seasonal, short cycle)<\/td>\n<td>35\u201360%<\/td>\n<td><a href=\"https:\/\/umbrex.com\/resources\/company-analysis\/supply-chain-logistics\/forecast-accuracy-by-product\/\" target=\"_blank\" rel=\"noopener nofollow\">Umbrex<\/a><\/td>\n<\/tr>\n<tr>\n<td>Small brands ($5\u201320M)<\/td>\n<td>25\u201335% on core SKUs<\/td>\n<td><a href=\"https:\/\/izba.co\/thought-leadership\/mape-for-demand-forecasting\" target=\"_blank\" rel=\"noopener nofollow\">Izba<\/a><\/td>\n<\/tr>\n<tr>\n<td>Cross-industry benchmark (monthly median)<\/td>\n<td>~85% accuracy<\/td>\n<td>APQC, via <a href=\"https:\/\/xorosoft.com\/forecast-accuracy-statistics\/\" target=\"_blank\" rel=\"noopener nofollow\">Xorosoft<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/div>\n<p>Two nuances that separate a planner with judgement from one who just watches a number:<\/p>\n<ul>\n<li><strong>MAPE punishes intermittent demand unfairly.<\/strong> On low-volume or sporadic items \u2014spare parts, long-tail formats\u2014 a small absolute error produces a huge percentage. For those, WAPE (<em>Weighted Absolute Percentage Error<\/em>, which weights error by volume instead of averaging percentages) or unit-based metrics are more honest.<\/li>\n<li><strong>Horizon matters as much as method.<\/strong> Each additional month of horizon typically adds 2\u20135 points of WAPE (<a href=\"https:\/\/umbrex.com\/resources\/company-analysis\/supply-chain-logistics\/forecast-accuracy-by-product\/\" target=\"_blank\" rel=\"noopener nofollow\">Umbrex<\/a>). Comparing your 1-month error with another company&#8217;s 6-month error tells you nothing.<\/li>\n<\/ul>\n<p>The practical conclusion: <strong>measuring deviation per model and per closed month is more useful than chasing an ideal MAPE<\/strong>. What makes money isn&#8217;t being right; it&#8217;s knowing which of your methods is least wrong for your type of item.<\/p>\n<h2>4. What the error costs: the argument management actually understands<\/h2>\n<p>&#8220;Improve the forecast&#8221; is a sentence that doesn&#8217;t get budgets approved. The cost of the error does.<\/p>\n<ul>\n<li>Stockouts cut annual revenue by <strong>2% to 5%<\/strong>, and excess inventory absorbs <strong>20% to 30% of working capital<\/strong> (<a href=\"https:\/\/www.toolsgroup.com\/blog\/cost-of-stockouts-vs-overstock\/\" target=\"_blank\" rel=\"noopener nofollow\">ToolsGroup<\/a>).<\/li>\n<li>Holding inventory costs <strong>20% to 30% of its value per year<\/strong> (<a href=\"https:\/\/aislestock.com\/stockout-cost\" target=\"_blank\" rel=\"noopener nofollow\">Aislestock<\/a>).<\/li>\n<li>For macro context: IHL Group puts global inventory distortion \u2014stockouts plus overstock\u2014 at <strong>$1.7 trillion in 2026<\/strong>, 6.2% of global retail sales, split 65.6% stockouts and 34.4% overstock (<a href=\"https:\/\/xorosoft.com\/stockout-statistics\/\" target=\"_blank\" rel=\"noopener nofollow\">via Xorosoft<\/a>). <em>It&#8217;s a retail figure: use it as the scale of the problem, not as your case.<\/em><\/li>\n<\/ul>\n<p>And on how sensitive the process is: &#8220;a relatively small change in forecast error or accuracy has a significant impact on supply cost and efficiency&#8221; (<a href=\"https:\/\/www.consultoria-sap.com\/2018\/10\/planificacion-demanda-sap.html\" target=\"_blank\" rel=\"noopener nofollow\">Consultoria-SAP<\/a>). In a food plant with short shelf life, or in pharma with batches, that impact shows up within the same week.<\/p>\n<figure class=\"ia-fig\"> <img loading=\"lazy\" src=\"https:\/\/innovapps.net\/wp-content\/uploads\/cost-of-stockouts-vs-overstock-sap.png\" alt=\"Forecast error cost balance: 2\u20135% of annual revenue lost to stockouts versus 20\u201330% of working capital tied up in excess inventory\" width=\"1200\" height=\"675\" loading=\"lazy\" decoding=\"async\" \/><figcaption>Running short and overshooting both have a price. And today, in many plants, the balance point is a spreadsheet.<\/figcaption><\/figure>\n<h2>5. SAP IBP: when it makes complete sense and when it&#8217;s too much<\/h2>\n<p>SAP IBP for demand is a serious product, and for complex supply chains it&#8217;s the right answer. The problem is sizing, not quality.<\/p>\n<p>What&#8217;s publicly known:<\/p>\n<ul>\n<li>It&#8217;s cloud, billed on <em>cost of goods per year<\/em> in blocks, and <strong>SAP doesn&#8217;t publish prices<\/strong> (<a href=\"https:\/\/us.fitgap.com\/products\/020807\/sap-integrated-business-planning\" target=\"_blank\" rel=\"noopener nofollow\">FitGap<\/a>; <a href=\"https:\/\/www.spotsaas.com\/product\/sap-integrated-business-planning\/pricing\" target=\"_blank\" rel=\"noopener nofollow\">spotsaas<\/a>).<\/li>\n<li>Demand Sensing, predictive analytics and network design are <strong>separate add-ons<\/strong>; some buyers report &#8220;discovering significant additional licence costs mid-implementation&#8221; (<a href=\"https:\/\/saplicensingexperts.com\/blog\/sap-ibp-licensing-guide\" target=\"_blank\" rel=\"noopener nofollow\">saplicensingexperts<\/a>).<\/li>\n<li>In verified reviews, average implementation time is around <strong>7 months<\/strong> and declared ROI around <strong>19 months<\/strong> (<a href=\"https:\/\/g2.com\/products\/sap-integrated-business-planning\/pricing\" target=\"_blank\" rel=\"noopener nofollow\">G2<\/a>).<\/li>\n<\/ul>\n<p><strong>IBP makes sense if:<\/strong> you plan across multiple plants and countries, you need demand sensing with daily signals, you have a dedicated planning team and a multi-year project budget.<\/p>\n<p><strong>It&#8217;s probably oversized if:<\/strong> your planning team is one or two people, your decision cycle is weekly, your real pain is &#8220;I want to compare methods and keep the plan in SAP&#8221;, and you need it solved this quarter rather than next fiscal year.<\/p>\n<p>Between &#8220;Excel&#8221; and &#8220;IBP&#8221; there&#8217;s a middle step almost nobody describes: <strong>calculating several statistical models on the history you already have in SAP, comparing their deviation over closed months, and saving the adopted version in standard S&amp;OP<\/strong>. It isn&#8217;t magic. It&#8217;s the work the planner already does by hand, done inside the system.<\/p>\n<h2>6. The multi-model approach: compare before you adopt<\/h2>\n<p>Here&#8217;s the change of method, and it&#8217;s more process than technology. Instead of configuring <em>one<\/em> model in the material master and trusting it, <strong>several are calculated in parallel<\/strong> for the same material and plant:<\/p>\n<ul>\n<li>simple average<\/li>\n<li>average adjusted by calendar days of the month<\/li>\n<li>3-period moving average<\/li>\n<li>6-period moving average<\/li>\n<li>weighted moving average<\/li>\n<li>simple exponential smoothing<\/li>\n<li>seasonal index by month<\/li>\n<li>linear trend<\/li>\n<li>linear trend with seasonality<\/li>\n<\/ul>\n<p>And each one shows its <strong>deviation over the months already closed<\/strong>. With that, the conversation shifts from &#8220;how much are we going to sell?&#8221; to &#8220;which method has been getting this family right?&#8221;. The planner compares, adopts the one that performs best, and <strong>the decision stays human<\/strong>: nobody lets the system set the plan on its own.<\/p>\n<p>It&#8217;s worth being blunt about this, because the market is full of promises of autonomous forecasting: <strong>this is not AI that forecasts by itself<\/strong>. It&#8217;s multi-model statistical calculation, transparent and auditable, with a human deciding. That it&#8217;s less flashy is exactly why it works in a mid-sized company.<\/p>\n<p>The rest is plumbing that matters: overlaying several years of history on one chart with trend and seasonality lines, saving <strong>multiple versions<\/strong> of the plan and comparing <strong>plan vs. actual<\/strong> by time series, and leaving the result in <strong>SAP&#8217;s standard S&amp;OP<\/strong> so MRP works from it.<\/p>\n<p>That is, specifically, what <strong>SiMPL<\/strong> does \u2014 <a href=\"https:\/\/innovapps.net\/en\/simpl\/\">Innova&#8217;s planning monitor<\/a>: it integrates the planning chain (MRP) into one interface where the planner sees the history, compares models by deviation and adopts the one they choose, with the plan saved in SAP&#8217;s standard S&amp;OP. It runs on <strong>SAP ECC and S\/4HANA<\/strong> (all editions except S\/4HANA Cloud Public Edition), is implemented in <strong>4\u20138 weeks<\/strong> and has <strong>no cost per user and no cost per company code<\/strong> \u2014 which means the planner, the plant manager and the operations director can all look at the same plan without changing the invoice.<\/p>\n<figure class=\"ia-fig\"><img loading=\"lazy\" src=\"https:\/\/innovapps.net\/wp-content\/uploads\/simpl-forecast-model-comparison-sap.png\" alt=\"SiMPL screen showing overlaid history and the table of nine forecast models with their deviation per closed month and adoption of the chosen model\" width=\"1200\" height=\"775\" loading=\"lazy\" decoding=\"async\" \/><figcaption>Comparing models, inside SAP: nine methods for the same material and plant, each with its deviation over the months already closed.<\/figcaption><\/figure>\n<p>No brochure: the part that changes your numbers is the method \u2014compare before you adopt\u2014. The app is how you do it without maintaining a file.<\/p>\n<h2>7. How to start without buying anything this week<\/h2>\n<div class=\"ia-scn\"><span class=\"ia-scn-n\">Step 01<\/span><\/p>\n<p><strong>Measure where you are.<\/strong> Calculate MAPE (and WAPE, for intermittent items) over your last six closed months, at family and at SKU level. Without a baseline, any improvement is an opinion.<\/p>\n<\/div>\n<div class=\"ia-scn\"><span class=\"ia-scn-n\">Step 02<\/span><\/p>\n<p><strong>Compare against your sector<\/strong>, not against the best case in the world. Use the table in section 3.<\/p>\n<\/div>\n<div class=\"ia-scn\"><span class=\"ia-scn-n\">Step 03<\/span><\/p>\n<p><strong>Inventory the spreadsheets.<\/strong> How many files there are, who maintains them, what happens if that person is away for two weeks.<\/p>\n<\/div>\n<div class=\"ia-scn\"><span class=\"ia-scn-n\">Step 04<\/span><\/p>\n<p><strong>Test two or three models by hand<\/strong> on a representative family and see which would have got it right. Done once, that exercise is usually what unblocks the decision \u2014 it&#8217;s where <a href=\"https:\/\/innovapps.net\/en\/blog\/driving-efficiency-arbomex-world-class-auto-parts-manufacturer-transforms-stock-management-in-sap-with-simpl-planning-monitor\/\">a real planning case with SiMPL<\/a> started.<\/p>\n<\/div>\n<div class=\"ia-scn\"><span class=\"ia-scn-n\">Step 05<\/span><\/p>\n<p><strong>Decide the step.<\/strong> If the answer is &#8220;I need to compare models and keep the plan in SAP&#8221;, your problem doesn&#8217;t require IBP. If it&#8217;s &#8220;I need multi-country demand sensing with daily signals&#8221;, it does.<\/p>\n<\/div>\n<h2>Frequently asked questions<\/h2>\n<div class=\"ia-faq\">\n<h3>Can I forecast demand in SAP without buying IBP?<\/h3>\n<p>Yes. ECC and S\/4HANA include consumption-based forecasting (MP30, MP38, MP31, MP33) and S&amp;OP planning to save plan versions and compare plan vs. actual. What the standard doesn&#8217;t offer comfortably is a screen where you compare several models by deviation before adopting one; that gap is filled either by an app on top of the standard or, as today, by Excel.<\/p>\n<h3>What MAPE is &#8220;good&#8221; in my sector?<\/h3>\n<p>It depends on how volatile your demand is. In stable FMCG, 10\u201325%; in consumer packaged goods, 15\u201325%; in industrial B2B, 20\u201340% at SKU level; in fashion, 35\u201360%. And a caveat: MAPE unfairly penalises low-volume or intermittent items, where WAPE is a more honest metric.<\/p>\n<h3>What are the limitations of MP30 and MP38?<\/h3>\n<p>The three most reported: a mandatory run material by material at a frequency defined in the master; a rounding threshold fixed at 1, which distorts low-volume items; and unplanned requirements that aren&#8217;t consumed by sales orders and have to be copied by hand into MD61\/PIR, leaving duplicate lines in MD04.<\/p>\n<h3>Is the forecast automatic?<\/h3>\n<p>No. The approach we argue for is multi-model and statistical: the system calculates several methods in parallel and shows the deviation of each one over the closed months, and <strong>the planner decides which model to adopt<\/strong>. The decision is human and is recorded as a plan version in SAP&#8217;s standard S&amp;OP.<\/p>\n<h3>What if we&#8217;re migrating to S\/4HANA?<\/h3>\n<p>Consumption-based forecasting and S&amp;OP exist in both environments, although SAP has stated that SOP\/flexible S&amp;OP are not strategic for S\/4HANA and that their intended successor is IBP. A planning monitor that runs on both ECC and S\/4HANA lets you solve the process today without constraining tomorrow&#8217;s platform decision \u2014 the same logic that applies to <a href=\"https:\/\/innovapps.net\/en\/blog\/easiest-clean-core-first-step-abap\/\">moving to S\/4HANA<\/a> without dragging the technical debt along.<\/p>\n<\/p><\/div>\n<div class=\"ia-ctablock\">\n<p><b>Want to see nine models compared against your own history?<\/b> We&#8217;ll look at it with your materials, not with sample data.<\/p>\n<p> <a class=\"ia-cta\" href=\"https:\/\/innovapps.net\/en\/schedule-demo\/?utm_source=blog&#038;utm_medium=post&#038;utm_campaign=simpl-demand-forecasting\">Book your SiMPL demo<\/a> <\/div>\n<\/div>\n<p><script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"BreadcrumbList\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/innovapps.net\/en\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Blog\",\"item\":\"https:\/\/innovapps.net\/en\/blog\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Demand forecasting in SAP without IBP\",\"item\":\"https:\/\/innovapps.net\/en\/blog\/demand-forecasting-sap-without-ibp\/\"}]},{\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"Can I forecast demand in SAP without buying IBP?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. 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