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