{"id":2708,"date":"2025-02-04T11:47:05","date_gmt":"2025-02-04T10:47:05","guid":{"rendered":"https:\/\/tenglerconsulting.com\/?p=2708"},"modified":"2026-02-03T10:27:16","modified_gmt":"2026-02-03T09:27:16","slug":"developing-demand-planning-capabilities-with-ai","status":"publish","type":"post","link":"https:\/\/tenglerconsulting.com\/en\/developing-demand-planning-capabilities-with-ai\/","title":{"rendered":"Developing demand planning capabilities with AI"},"content":{"rendered":"

[vc_row section_type=”fullwidth” height_ratio=”60″ padding_top_multiplier=”” padding_bottom_multiplier=”” rc_bg_type=”image” rc_bg_image=”2756″ columns_gap=”none”][vc_column][\/vc_column][\/vc_row][vc_row section_type=”fullwidth” padding_top_multiplier=”3x” padding_bottom_multiplier=”4x” columns_gap=”60″][vc_column width=”2\/3″]

Developing Demand Planning Capabilities with AI<\/span><\/span><\/h1>
<\/div>[\/vc_column][vc_column width=”1\/3″][\/vc_column][\/vc_row][vc_row section_type=”fullwidth” padding_top_multiplier=”” padding_bottom_multiplier=”” columns_gap=”60″][vc_column]
<\/span><\/div>[\/vc_column][\/vc_row][vc_row section_type=”fullwidth” padding_top_multiplier=”3x” padding_bottom_multiplier=”2x” columns_gap=”60″][vc_column width=”1\/3″][vc_column_text css=”” text_style=”small-text”]01<\/span>[\/vc_column_text]

The path to cost efficiency: FCA – RESPONSIVENESS – COST EFFICIENCY<\/span><\/h3>[\/vc_column][vc_column width=”2\/3″][vc_column_text css=””]More than ever, businesses face the big challenge of having to increase revenue while keeping costs under control. In the current economic situation, cost efficiency is as crucial as revenue growth.<\/p>\n

Energy costs, labor-related costs, bureaucratic hurdles, and global competition are putting significant pressure on European industries, which have failed to adapt organizationally and process-wise in recent years. While the USA experiences rapid productivity growth through AI, advanced sensors, and bioengineering, Europe struggles with minimal productivity gains due to lack of vision and innovation (more on LinkedIn<\/a>).<\/p>\n

Like a weather forecast, slight inaccuracies in demand predictions can have major consequences. By refining forecasts, just as meteorologists adjust their models, organizations improve their responsiveness, avoid costly mistakes, and maintain better cost control. This higher forecasting accuracy (FCA) paves the way for greater efficiency overall. However, traditional approaches to improving FCA have proven costly, time-consuming, and often ineffective. To address these limitations, the integration of artificial intelligence (AI) into supply chain (SC) planning emerges as a transformative solution.<\/p>\n

In the following chapters, we want to focus on FCA optimization through AI-assisted planning. We will show how companies can significantly improve their planning processes and minimize costs by leveraging AI, while maintaining high service levels.<\/span>[\/vc_column_text][\/vc_column][\/vc_row][vc_row section_type=”fullwidth” padding_bottom_multiplier=”2x” columns_gap=”60″][vc_column width=”1\/3″][vc_column_text css=”” text_style=”small-text”]02<\/span>[\/vc_column_text]

Typical approaches to FCA improvement<\/span><\/h3>[\/vc_column][vc_column width=”2\/3″][vc_column_text css=””]Improving FCA has historically been approached through manual efforts and traditional planning methods. While these efforts may yield marginal improvements, they often involve significant additional workload for sales teams and planning analysts.<\/p>\n

Investing more time and\/or resources into manual forecasting<\/strong>
\nThis conventional approach relies heavily on manual forecast creation and adjustments. Big part of commercial organization (sales managers, commercial directors, commercial heads, \u2026) is participating in the forecasting process, where everyone provides input, but these efforts result in complex process and often fail to add value. As seen in figure 1, with the assistance of sales managers a baseline FC is created with 70% FCA, then with the inputs of the Sales Directors FCA increases by 2%, but when the Commercial Head adjusts the forecast to align with the budget, this results in a negative forecast value add (FVA) of 7%, because the forecast is influenced by bias.<\/p>\n

These adaptations come at the cost of extensive manual planning, adding unnecessary complexity and resource consumption and often result in negative forecast value add.<\/p>\n

Using statistical forecasting as a baseline<\/strong>
\nStatistical forecasting methods provide a structured foundation for sales predictions, reducing the impact and the need for subjective inputs. However, these approaches remain costly and share the drawbacks of manual planning: lengthy processes and significant efforts required from sales teams.[\/vc_column_text][\/vc_column][\/vc_row][vc_row section_type=”fullwidth” columns_gap=”60″][vc_column width=”1\/3″][\/vc_column][vc_column width=”2\/3″]

\r\n\t
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<\/div>[vc_column_text css=”” text_style=”small-text”]Figure 1: The potential issue of negative forecast value add[\/vc_column_text][\/vc_column][\/vc_row][vc_row section_type=”fullwidth” padding_bottom_multiplier=”2x” columns_gap=”60″][vc_column width=”1\/3″][\/vc_column][vc_column width=”2\/3″][vc_column_text css=””]These adaptations come at the cost of extensive manual planning, adding unnecessary complexity and resource consumption and often result in negative forecast value add.<\/p>\n

Using statistical forecasting as a baseline<\/strong>
\nStatistical forecasting methods provide a structured foundation for sales predictions, reducing the impact and the need for subjective inputs. However, these approaches remain costly and share the drawbacks of manual planning: lengthy processes and significant efforts required from sales teams[\/vc_column_text][\/vc_column][\/vc_row][vc_row section_type=”fullwidth” padding_bottom_multiplier=”2x” columns_gap=”60″][vc_column width=”1\/3″][vc_column_text css=”” text_style=”small-text”]03<\/span>[\/vc_column_text]

A new approach for optimal results: differentiated planning with AI support<\/span><\/span><\/h3>[\/vc_column][vc_column width=”2\/3″][vc_column_text css=””]To overcome the challenges of high complexity and manual effort, companies must focus on two key elements:<\/p>\n

First, a differentiated approach to planning<\/strong> based on specific product and customer characteristics. The key principle lies in situational optimization: tailoring forecasting and planning strategies according to the nature of the products, demand volatility, and business criticality. Determining the right forecasting approach is crucial and can be easily done with Supply Chain Segmentation<\/a>.\u00a0 Often, unbiased algorithms perform better than the planner. When considering whether a specific segment should be planned by a machine or a human or a combination of both, we recommend that you follow the simple principle:<\/p>\n

Choose your battles wisely: where the machine performs better than the planner, let the machine do the job!<\/p><\/blockquote>\n

The second element is using AI as an advanced algorithm. <\/strong>It handles independently numerous decisions (data cleansing, the selection of suitable forecasting methods, the identification of both internal and external indicators to improve FCA on the lowest planning level, etc.) and automates the planning process.<\/p>\n

Below is a simplified example of product groups with distinct characteristics and suitable planning approaches.<\/p>\n

Make-to-Stock (MTS) Products<\/strong>
\nMTS products are stable and predictable and do not require detailed manual forecasts, therefore when assisted by AI-enabled automated planning, reliable statistical forecasts can be generated, ensuring efficient resource allocation without any human intervention.<\/p>\n

Products with High Strategic Importance<\/strong>
\nProducts with high volatility and strategic significance require advanced AI-supported algorithms with human oversight. AI systems incorporate external data sources, market trends, and other influencing factors to improve accuracy. Human expertise remains critical for final review and control. Sales managers and\/or demand planning analysts play a specialized role, focusing on validating financial figures and providing the final sign-off.<\/p>\n

Products with Low Strategic Importance<\/strong>
\nFor products that are customized but lack strategic relevance, businesses should avoid excessive planning efforts. This category includes mostly products with low and volatile demand which are very difficult to plan, whether with AI or manually. In such cases, the cost of improving FCA often exceeds the financial benefits. The principle here is to optimize resources by minimizing unnecessary forecasting efforts.[\/vc_column_text][\/vc_column][\/vc_row][vc_row section_type=”fullwidth” padding_bottom_multiplier=”2x” columns_gap=”60″][vc_column width=”1\/3″][vc_column_text css=”” text_style=”small-text”]04<\/span>[\/vc_column_text]

Path to implementation: people, processes, and tools<\/span><\/h3>[\/vc_column][vc_column width=”2\/3″][vc_column_text css=””]Achieving this level of optimization requires a fundamental shift in the way businesses approach planning and forecasting. Three key enablers must be addressed:<\/p>\n

People<\/strong>
\nSpecialized roles are essential for implementing AI-supported planning. Demand planning analysts and sales experts must develop expertise in managing advanced forecasting tools and interpreting AI-driven insights.<\/p>\n

Processes<\/strong>
\nDifferentiated planning processes must be implemented, categorizing products and customers based on their characteristics. Supply chain Segmentation is crucial to ensuring that planning strategies are appropriately tailored.<\/p>\n

Tools<\/strong>
\nform the backbone of this approach, but at the same time it is crucial to find the best-fit tool in the emerging market of software providers. Tailored AI algorithms can analyze large datasets, identify patterns, and provide actionable insights to enhance FCA while reducing manual effort.[\/vc_column_text][\/vc_column][\/vc_row][vc_row section_type=”fullwidth” padding_bottom_multiplier=”2x” columns_gap=”60″][vc_column width=”1\/3″][vc_column_text css=”” text_style=”small-text”]05<\/span>[\/vc_column_text]

Conclusion<\/span><\/h3>[\/vc_column][vc_column width=”2\/3″][vc_column_text css=””]AI-supported optimization represents a game-changing solution for improving forecast accuracy and optimizing customer deliveries. By adopting a differentiated approach to planning \u2014 tailored to product characteristics, demand volatility, and strategic importance \u2014 businesses can achieve situational optimization that reduces costs and drives revenue growth.<\/p>\n

The successful implementation of this strategy requires a combination of specialized expertise, process differentiation, and advanced AI tools. Studies show that almost 60% of buyers regret at least one software purchase made in the past one and a half years, while one out of two of those are facing increased costs as a result of their decision.[1][\/vc_column_text][\/vc_column][\/vc_row][vc_row section_type=”fullwidth” padding_bottom_multiplier=”2x” columns_gap=”60″][vc_column width=”1\/3″][vc_column_text css=”” text_style=”small-text”]06<\/span>[\/vc_column_text]

Why TenglerConsulting?<\/span><\/h3>[\/vc_column][vc_column width=”2\/3″][vc_column_text css=””]We at TenglerConsulting help you to define the relevant requirements for the software and guide you through the selection process to successful implementation. Companies that embrace this approach will gain a significant competitive edge, ensuring efficient and cost-effective supply chain operations in an increasingly challenging market environment. We are here to guide you through this new environment from idea to implementation while making sure that your strategy aligns with this transformation.<\/p>\n

 <\/p>\n

 <\/p>\n

Source:<\/strong>
\n[1] Gartner, 2025: \u201c2025 Software Buying Trends Report\u201d<\/a>\u00a0 [Accessed: 7 January 2025].[\/vc_column_text][\/vc_column][\/vc_row][vc_row section_type=”fullwidth” padding_bottom_multiplier=”2x” rc_link_color=”primary-1″ rc_link_hover_color=”black” columns_gap=”60″][vc_column width=”1\/3″][vc_column_text css=”” text_style=”small-text”]08<\/span>[\/vc_column_text]

Further insights<\/span><\/h3>[\/vc_column][vc_column width=”2\/3″][vc_column_text css=””]Our series \u201cAI in SC planning\u201d focuses on how to create value in your supply chain, therefore stay tuned for the upcoming articles! In the meantime, check our\u00a0insights<\/a> for more information on supply chain optimization.[\/vc_column_text][\/vc_column][\/vc_row][vc_section disable_element=”yes” el_id=”author”][vc_row section_type=”fullwidth” padding_top_multiplier=”” padding_bottom_multiplier=”” columns_gap=”60″][vc_column]
<\/span><\/div>[\/vc_column][\/vc_row][vc_row section_type=”fullwidth” padding_top_multiplier=”3x” padding_bottom_multiplier=”3x” columns_gap=”60″ equal_column_height=”equal”][vc_column width=”1\/3″ full_height=”yes”]
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\"\"<\/div><\/div>\t<\/div>\r\n\t<\/div>\r\n<\/div>\r\n[\/vc_column][vc_column width=”2\/3″ full_height=”yes”][vc_row_inner height_ratio=”50″][vc_column_inner full_height=”yes”][vc_column_text css=”” el_class=”special-link”]Ready to strengthen your supply chain\u2019s sustainability through a stronger integration?<\/strong> Contact us at office@tenglerconsulting.com<\/a> to discuss how we can develop a planning framework\u2014supporting sustainable, efficient, and resilient supply chain practices made to your business needs.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner height_ratio=”50″][vc_column_inner vertical_content_position=”bottom” full_height=”yes”][vc_column_text css=”” text_style=”small-text”]About the author<\/strong>[\/vc_column_text]
<\/div>

Daria Reshetniak<\/span><\/h3>[vc_column_text css=””]T: +43 1 234 56 78
\nE: daria.reshetniak@tenglerconsulting.com<\/a>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][\/vc_section][vc_section el_id=”contact”][vc_row section_type=”fullwidth” padding_top_multiplier=”” padding_bottom_multiplier=”” columns_gap=”60″][vc_column]
<\/span><\/div>[\/vc_column][\/vc_row][vc_row section_type=”fullwidth” padding_top_multiplier=”3x” columns_gap=”60″][vc_column width=”3\/4″]

Are you ready to create value with AI? Contact us at\u00a0office@tenglerconsulting.com<\/a>\u00a0or connect with us on\u00a0LinkedIn<\/a>.<\/span><\/h3>[\/vc_column][vc_column width=”1\/4″][\/vc_column][\/vc_row][\/vc_section]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

Discover how integrating Artificial Intelligence (AI) into your demand planning processes revolutionizes forecast accuracy, drives cost efficiency, and boosts responsiveness. Read here how AI-powered forecasting, alongside a differentiated planning approach, helps you streamline operations while maintaining high service.<\/p>\n","protected":false},"author":5,"featured_media":2756,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36,35,3],"tags":[],"class_list":["post-2708","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-02-planning","category-03-supply-chain-excellence","category-05-supply-chain-trends","grve-entry-item","grve-blog-item"],"yoast_head":"\nAI in demand planning | Tengler Consulting<\/title>\n<meta name=\"description\" content=\"AI in demand planning for optimizing forecast accuracy, efficiency, and cost reduction in the supply chain.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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