How data driven Demand Planning actually delivers

Effective demand planning isn’t just about making better forecasts—it’s about understanding how and why demand behaves the way it does. Every business deals with uncertainty: one product sells predictably every month, another sees random spikes, and a third fades faster than expected. Treating them all the same leads to missed opportunities, excess inventory, and endless firefighting.

A more structured approach is needed—one that combines segmentation, advanced forecasting, and product lifecycle management. Segmenting demand by product type, customer behavior, or forecastability helps focus planning efforts where they add the most value. Leveraging external data and causal factors allows organizations to move beyond reactive forecasting and make proactive, data-driven decisions. And by integrating lifecycle management, planners can ensure new product launches and phase-outs align seamlessly with business objectives.

Let's deep dive how these pillars together to outline a complete framework for modern demand planning designed to help enterprises create more accurate forecasts, optimize inventory, and strengthen their supply chain performance.

Demand segmentation & classification 

Demand segmentation is a crucial element of effective demand planning because it recognizes that one size does not fit all. 

Different products, customer segments, and sales channels behave in unique ways. That means your approach to forecasting, inventory management, and service levels also needs to vary.  

By segmenting demand, you can create more tailored and accurate forecasts, which in turn improves supply chain performance and allows you to focus your efforts where they pay off most. Here's how demand segmentation and classification can help: 

Forecastability and what to focus on 

Not every product is easy to forecast, or even possible to forecast at all. High forecastable products have stable consistent demand patterns. Simple statistical methods are usually enough to get these right. Low forecastable products, on the other hand, have erratic demand that's tough to predict. These might need more complex models, or in some cases, they may be nearly impossible to forecast. Instead of spending too much time on these, you might rely on exception-based management. 

Key categories for segmentation 

Some products or customers carry more weight in your business and need more precise planning attention: 

• High-volume or high-value products: Forecasting errors for these categories can have a high financial impact. For example, if you get the forecast wrong for a high-volume product, you could end up with stockouts, lost sales, or too much inventory. A few high value products becoming obsolete can be worse than having ten times as many low-value products becoming obsolete, so make sure to look at value as well when segmenting demand.  

• Key customers: Certain customer segments might represent a big part of your company's revenue. Segmenting based on key customers allows you to review these most important ones easily with salespeople.  

Handling low-volume or intermittent demand 

One of the toughest parts of demand planning is dealing with low-volume or intermittent demand products. These don't have enough historical data for a reliable baseline, and their demand patterns are often irregular. 

• Focus on category A products: Prioritize your efforts on A-class products (high value/high volume). These are critical to the business and where accurate forecasting can make the biggest difference. 

• Exception management: For products with irregular or low demand, instead of using traditional forecasting methods, focus on exception management. Monitor and flag these items when demand deviates from the norm, so you can react quickly without spending too much time on trying to predict the unpredictable. 

Advanced data sources, leading indicators, outside-in planning and causal forecasting 

Advanced demand planning goes beyond relying solely on historical data and sales input. It involves adjusting predictions based on specific factors that drive changes in the demand.  

If your organization is ready for it, leveraging advanced data sources can help fine-tune forecasts, but keep in mind that these methods require sophisticated software and aren't the best starting point for most companies. 

Key factors that impact the forecast 

Market intelligence data: Data from industry analysts, consumer sentiment surveys, or market research firms provides a broader view of demand trends. This lets you see beyond your internal data and understand how the market is moving. 

Macroeconomic indicators: Things like inflation rates, GDP growth, employment statistics, and consumer spending reports can all influence buying behavior. Keeping these in mind helps you anticipate changes in demand before they hit your sales numbers. 

Often-talked-about-but-barely-seen-in-practice data sources 

Social media data: Platforms like Twitter, Instagram, and Facebook offer real-time insights into customer behavior, product reviews, and trends. While it's talked about a lot, it's still rare to see companies fully integrate this into their demand planning process and the value is questionable.  

Weather data: Weather patterns can have a big impact on demand for products like clothing, food, and outdoor gear. For example, retailers might adjust forecasts for winter gear based on predictions of a colder-than-usual season. The difficulty here is that you're basing your forecast on a forecast (namely the weather forecast). 

Leading indicators and data providers 

Economic leading indicators: Indices like the Consumer Price Index (CPI) and Gross Domestic Product (GDP) provide insights into the overall health of the economy, helping you anticipate changes in demand. 

Data providers: Providers like Nielsen, GfK, Kantar, S&P Global (also took over IHS Markit, a former intelligence player), Euromonitor, and Statista offer data on market trends, consumer behavior, and sales forecasts, which companies can integrate into their demand planning processes. 

Causal forecasting and data-driven adjustments 

Causal forecasting ties changes in demand to specific causes. Unlike traditional methods, which are often reactive, causal forecasting is proactive and it helps you adjust forecasts based on specific events or company initiatives, like: 

1. Price changes: If there's a discount or price drop, you can expect a temporary spike in demand, which should be reflected in the forecast. 
2. Promotions and marketing: Advertising campaigns, promotions, or new product launches can cause short-term demand surges. You'll need to adjust your plans to account for these. 
3. Competitor actions: A competitor launching a new product or running a big promotion can shift market share and affect your own demand. You don't want to factor this into your historical baseline, but it's something you should consider for future forecasts. 
4. External events: Political, social, or economic disruptions can lead to major spikes or dips in demand. These aren't always predictable, but categorizing them will allow you to simulate scenarios in an advanced planning environment.  

Manual adjustments vs. data-driven signals 

Manual adjustments: Traditionally, demand planners manually adjust forecasts based on their experience and input from other departments, like sales and marketing. For example, you might adjust the forecast for an upcoming promotion or holiday season. 

System adjustments: More advanced systems, however, can predict the effect of a promotion on their own. Instead of manually creating the spike, you can upload a calendar of upcoming events, and the system will adjust the forecast automatically based on what it has seen in the past with such events (which you manually labeled after an alert was generated, or for which you also uploaded an event calendar). 

Product lifecycle planning/Portfolio management 

Managing a product's lifecycle is crucial for keeping your demand plans accurate and aligned with real-world changes. Let's dive into some of the key components involved: 

Marketing plans and new product introductions 

Marketing efforts are usually most intense during the product introduction phase. For demand planners, aligning with marketing is essential to ensure the supply chain can respond to the initial demand spikes. Marketing campaigns, promotions, and advertising can significantly influence demand, and sometimes the forecasts from marketing are overly optimistic. 

Key considerations: 

• Forecasting impact: Marketing initiatives often cause temporary spikes in demand, so it's important to factor these into your short-term forecast. Estimating the right ramp-up is crucial. 

• Tracking performance: Use actual sales data to see how well the market is responding to marketing efforts and adjust your forecasts accordingly. 

• Cannibalization: Be aware that new product launches can eat into the sales of existing products. Make sure to account for this in your plan. 

Phase-in and phase-out of products 

Managing the phase-in and phase-out process is one of the trickiest parts of portfolio management. 

Phase-in considerations: When a new product is introduced, base your initial forecasts on the performance of similar products. Adjust for current market conditions, customer feedback, and any promotions that might be in place. Since trends and seasonality can take time to develop, it's smart to use patterns from the old product to help guide the forecast. 

Phase-out considerations: Closely monitor products that are being phased out to avoid overstocking and minimize write-offs. Set a phase-out date in your demand plan and stop generating new forecasts after that point, taking current inventory levels into account. 

Managing packaging changes and versioning 

Sometimes, updates to product packaging require the creation of new SKUs, even if the product inside stays the same. This adds complexity to the demand planning process because you'll need to manage the transition between old and new versions. 

-> Forecast based on historical data: Obviously, if you've had similar packaging updates before, look at historical sales data to guide your forecast for the new version. This will help you anticipate any changes in demand. 

Aligning product lifecycle management with S&OP processes 

The Sales & Operations Planning (S&OP) process provides the cross-functional alignment needed to make informed decisions about new product introductions, portfolio reviews, and phase-out strategies. 

S&OP's role in product lifecycle management: 

• Consensus on new product launches: S&OP meetings are where initial demand forecasts and purchase quantities for new products are established.  

• End-of-life planning: S&OP processes ensure businesses can accurately forecast demand for products in their decline stage, helping them make data-driven decisions about when to discontinue. 
  
  

More on:

Demand segmentation and classification: 

Arkieva on segmenting demand and exception management 

A video of BSH with Lora Cecere and their forecastable and non-forecastable categories of demand. (watch from automatic starting point until min 4:17)

Advanced data sources:

An example of BSH in a webinar with Supply Chain Insights by Lora Cecere on their usage of leading indicators and outside-in planning 

Interesting webinar by Solventure on leading indicators (you have to fill out a form to get the recording!) 

Product lifecycle management and new product introductions: 

Phase-in phase-out planning by Arkieva (more from an inventory planning perspective rather than pure demand planning): https://blog.arkieva.com/phase-in-phase-out-planning/  

Product lifecycle management by Slimstock: https://www.slimstock.com/blog/product-lifecycle-management/  

New Product Forecasting & Planning Benchmark Report: Shorting Lifecycles, Forecasting Becomes Harder by IBF: https://demand-planning.com/2017/12/18/new-product-forecasting-planning-benchmark-report-lifecycles-shorten-forecasting-becomes-harder/