What are the key drivers and indicators of demand variability in your industry?

1 Demand drivers

Demand drivers are factors that influence the level and pattern of demand for products or services. These drivers can be internal or external, and can vary by industry, market, segment, or product. Seasonality is a common driver, which involves cyclical or periodic fluctuations in demand due to weather, holidays, events, or other recurring patterns. Trends are long-term changes in demand due to shifts in consumer preferences, tastes, lifestyles, or behaviors. Promotions are short-term spikes or drops in demand due to marketing campaigns, discounts, offers, or incentives. Competition involves changes in demand due to the actions or reactions of competitors. Finally, innovation is the introduction or adoption of new products, services, technologies, or processes that create new needs or solve existing problems.

2 Demand indicators

Demand indicators are data sources or signals that help measure and monitor the demand drivers and their effects on demand variability. These indicators can be quantitative or qualitative, and come from various sources, such as sales data, market data, customer data, social media data, and economic data. For example, sales data can include historical or current information on volume, value, frequency, or distribution; market data can cover the size, growth, share or structure of a market; customer data can be related to customer behavior, preferences, satisfaction, loyalty or retention; social media data can include online conversations, reviews, ratings, mentions or sentiments; and economic data can refer to macroeconomic or microeconomic conditions like GDP, inflation, unemployment income or consumer confidence.

3 Demand forecasting

Demand forecasting is the process of estimating the future demand for your products or services based on demand drivers and indicators. This can help you optimize inventory levels, production capacity, and supply chain management, as well as align your marketing and sales strategies with the demand opportunities and challenges. Additionally, demand forecasting can help you anticipate and respond to fluctuations and uncertainties with agility and resilience, ultimately enhancing customer service and satisfaction by meeting or exceeding their expectations and needs.

4 Demand forecasting methods

Demand forecasting methods are the techniques or tools used to generate demand forecasts and can be divided into two categories: qualitative and quantitative. Qualitative methods rely on the judgment, intuition, or opinion of experts, stakeholders, or customers, and are useful when little or no historical data is available. Examples include surveys, focus groups, Delphi method, and scenario analysis. On the other hand, quantitative methods depend on the mathematical or statistical analysis of historical or current data and are beneficial when sufficient and reliable data is present. Examples include time series analysis, regression analysis, causal models, and machine learning.

5 Demand forecasting challenges

Demand forecasting challenges arise from various sources, such as data quality, data complexity, model selection, model validation, and model adaptation. Poor data quality can be caused by a lack of availability, accuracy, consistency, completeness, or relevance. Data complexity can be due to the high volume, variety, velocity, or variability of the data. Model selection can be difficult when trying to choose the most appropriate or effective forecasting method or model for the data and context. Model validation can also be challenging when assessing the accuracy, reliability, or validity of the forecasting method or model. Finally, model adaptation may prove difficult when attempting to update or adjust the forecasting method or model to reflect changes or uncertainties in demand drivers or indicators.

6 Demand forecasting best practices

Demand forecasting best practices are the guidelines or recommendations you should follow to overcome demand forecasting challenges and improve performance and outcomes. These can vary by industry, market, segment, or product, but some general ones are to define objectives, scope, and horizon; collect and analyze demand drivers and indicators; choose and apply forecasting methods or models; evaluate and compare forecasts with the actual demand; and communicate and collaborate with internal and external stakeholders.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?