# What low-code machine learning platforms work best for manufacturing or supply chain companies that want demand forecasting without building custom models from scratch?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For a piece on forecasting tools, I looked specifically at manufacturing, supply chain, and adjacent industrial reviewers to see which low-code ML platforms they use for demand and inventory forecasting without hand-building models. Filtering the<a class="a a--md" elv="true" href="https://www.g2.com/categories/low-code-machine-learning-platforms"> </a><a class="a a--md" elv="true" href="https://www.g2.com/categories/low-code-machine-learning-platforms">low-code machine learning platforms</a> reviews to those industries and forecasting use cases, here's what surfaced:</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/sas-sas-viya"><strong>SAS Viya</strong></a> - The most-mentioned forecasting tool in this cut, with reviewers in banking, energy, and utilities describing forecasting and optimization on real industry data. How much tuning did the forecasts need before they were trustworthy?</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/alteryx"><strong>Alteryx</strong></a> - A supply chain reviewer described using its predictive tools on historical sales, supplier lead times, and seasonal trends to forecast inventory demand without external software, run daily on a schedule. Has anyone pushed it past inventory into broader demand planning?</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/qlik-predict"><strong>Qlik Predict</strong></a> - A logistics director called out datalake automation and real-time streaming with a no-code approach that cut repetitive work. Does the real-time angle actually help for demand forecasting, or is it more for monitoring?</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/dataiku"><strong>Dataiku</strong></a> - Comes up from energy and industrial reviewers for building forecasting and analytics pipelines in a low-code way. How well did it handle messy operational data from the plant floor?</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For the manufacturing and supply chain folks, which of these gave you forecasts you could actually plan against, and how much historical data did you need before the predictions were useful?</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p>

##### Post Metadata
- Posted at: 26 days ago
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;In industrial reviews, SAS Viya is often used for demand and inventory forecasting because of its in-memory processing that quickly handles large histories. Forecast quality depends on input quality, so the key is upfront budgeting prep time.&lt;/p&gt;

##### Comment Metadata
- Posted at: 11 days ago
- Author title: Marketer





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