Historically, firms relied on manual forecasting – sales teams, planners and heuristics – especially where demand was stable or product portfolios small. As data and computing power grew, statistical time-series methods (moving averages, exponential smoothing, ARIMA) became standard for rigor and repeatability. Over the last decade, machine learning and AI introduced a third wave: algorithms that can ingest large, heterogeneous data sets (sales history, promotions, weather, events, web signals) and model nonlinear relationships that classical formulas miss. 

Academic and industry studies document several concrete advantages when machine learning is applied correctly: improved point forecasts, better handling of promotional and intermittent demand, and the ability to use external unstructured signals (e.g., search trends or social data) that classical models can’t digest easily [1][5]. That translates into fewer stockouts, lower safety stock, and faster decision cycles – i.e., tangible accuracy and efficiency gains. Comparative reviews of ML and deep-learning approaches show they can outperform baseline statistical models on complex retail and manufacturing datasets, though results vary by use case and data quality [1]. 

A concrete, public example which you might know of is Amazon’s work on automating forecasting at scale. Amazon and AWS have documented efforts to run ML forecasting for large product sets, automating model selection and producing rapid forecasts that feed replenishment and assortment decisions. These programmatic forecasting efforts are illustrative: they show benefits when a company has (a) large historical data, (b) a clean data pipeline and (c) capability to operationalize model outputs into supply-chain actions [2]. 

Despite the upside, AI for demand forecasting is not plug-and-play. Common, evidence-backed obstacles include: poor or fragmented data, integration gaps between AI and ERP/IMS systems, lack of explainability (which undermines planner trust), and skills/cost barriers to build and maintain models. Press coverage and surveys of enterprise AI projects repeatedly emphasize that data plumbing and change management are often harder (and costlier) than model development itself [3][4]. In short: AI amplifies both good and bad data – a weak data foundation often produces worse outcomes, not better ones.

AI adds the most value when: portfolios are large, demand drivers are many and non-linear (promotions, weather, events, online trends), historical plus external data are plentiful and reliable, and planning horizons are rather long-term than short-term. Conversely, classical mathematical models or experienced human judgment remain preferable when: demand is stable with strong seasonality and few external drivers; historical records are thin (new products); the business cannot commit to the data and integration investment; or rapid interpretability and transparent decision rules are mandatory. Many successful programs therefore adopt a hybrid approach: use ML where it demonstrably improves accuracy, keep classical models where they match performance with lower cost, and keep humans in the loop for exceptions. 

Business leaders who succeed with AI forecasting treat it as a change program, not a one-off analytics project. A recommended pragmatic path is: (1) run parallel models in a PoC or Pilot (ML vs. baseline) on a chosen category; (2) measure business KPIs (forecast error, stockouts, inventory days); (3) build pipelines and explainability features that planner teams can trust; (4) scale where ROI is clear. This staged approach should reduce risk and speed value capture.

In summary, AI can materially raise forecast accuracy and operational efficiency – but it’s not an unconditional upgrade. The real strategic question for executives is not “Can we use AI to improve our demand forecasts?” but “Where will AI in demand forecasting improve business outcomes enough to justify the people, data and systems work it demands?” This is a more complex question, to which each company has their own answer, and we are happy to support them finding. 

Quellen:

1. Douaioui, K., Oucheikh, R., Benmoussa, O., & Mabrouki, C. (2024). Machine Learning and Deep Learning Models for Demand Forecasting in Supply Chain Management: A Critical Review. Applied System Innovation (MDPI).
   www.mdpi.com/2571-5577/7/5/93
2. Amazon Web Services / Forbes (2021). Predicting The Future Of Demand: How Amazon Is Reinventing Forecasting With Machine Learning. (Practical example of ML forecasting at scale.)
   www.forbes.com/sites/amazonwebservices/2021/12/03/predicting-the-future-of-demand-how-amazon-is-reinventing-forecasting-with-machine-learning/
3. Axios (2023). Companies struggle to deploy AI due to high costs and confusion. (Press coverage of enterprise AI deployment barriers, data issues and pilots-to-production gap.)
   www.axios.com/2023/08/19/ai-corporate-barriers-cost-data
4. Thomson Reuters Institute (2023). AI and supply chains: The future is (almost) here. (Practical discussion of use cases and implementation hurdles.)
   www.thomsonreuters.com/en-us/posts/technology/ai-supply-chains/
5. Mediavilla, M.A. et al. (2022). Review and analysis of artificial intelligence methods for demand forecasting. (ScienceDirect — literature review comparing AI and traditional approaches.)
   www.sciencedirect.com/science/article/pii/S2212827122004036