How do you incorporate seasonality, promotions, and other demand drivers into your demand planning process?

In addition to the obvious drivers of demand such as marketing campaigns, many consumer brands are heavily influenced by organic drivers. For example, a product may experience sudden spikes and troughs in demand not only due to internal marketing efforts, but also due to viral word-of-mouth effects. Content creators and social media platforms such as TikTok, Reddit, and Instagram are financially incentivized to massively amplify and accelerate extreme sentiments (both positive and negative), including for consumer products. As a result, companies must plan ahead to understand whether they want to place bets for certain inventory, especially if they suspect upcoming cultural events (eg: Barbie movie) will drive unusual content creation.

You should also consider distribution strategy - like new market, channel expansion, depth of penetration and product assortment.

Demand drivers refer to a set of factors that significantly impact the demand for a product or service. These factors can vary from changes in consumer preferences and demographics to shifts in income levels, economic fluctuations, and technological advancements. Businesses can leverage their understanding of these demand drivers to adjust their strategies, align better with their customers' needs, and outperform their competitors. By staying informed about the various demand drivers, businesses can stay ahead of the curve and ensure their long-term success.

In my experience, the answer to determining what are the demand drivers depends on the time horizon, local market, type of product and the final user of the information. Example: If the horizon is short term and the product has a high price elasticity, the level of discounts and promotional events are important drivers. If the horizon is long term and the user is in the marketing area, for instance, macroeconomic data such as inflation, price adjustments, GDP, consumption, etc., are essential drivers. Markets that suffer from periodic natural events such as floods and storms need to consider this data as an important driver. The professional needs to have the knowledge to discern which drivers are important for a given demand output.

Demand drivers are factors influencing customer demand: 1. Internal Demand Drivers: ? Pricing: Changes in price can affect demand levels. ? Marketing: Promotions and advertising can boost demand. ? Inventory: Stock levels and availability can impact sales. 2. External Demand Drivers: ? Seasonality: Seasonal trends (e.g., winter clothing sales in colder months) affect demand. ? Economic Conditions: Economic factors, such as a recession, influence consumer spending. ? Competition: Actions by competitors can shift demand patterns. Recognizing these drivers helps businesses anticipate and adjust their demand planning.

Demand drivers are important because they are the factors that influence the demand for a product or service. By understanding the demand drivers, businesses can better anticipate the needs of their customers and adjust their strategies accordingly. Some common demand drivers include changes in consumer preferences, economic conditions, and demographic shifts. By keeping a close eye on these drivers, businesses can stay ahead of the curve and remain competitive in their respective markets.

Also, diversity in using these demand influencers across various production industries can be observed, as fast moving FMCG goods which are affordable by many people would have prominent demand rates and in Automotive industries like Car, bike etc., forecasting of the FGs might not be completely relied on the historical sales as these products are considered as single time luxuries by many people. Incorporation of these customised drivers at the right place in the process of arriving at the final approved consensus demand helps in increasing the forecast accuracy figures.

Drivers are certainly very important to be tracked and incorporated as it's not just about demand planning but also sending right or better as accurate demand signals to the supply side as well and well maybe to other business processes like IBP or financial planning. Well drivers will definitely help to get a more accurate forecast which will certainly improve accuracy. The accurate forecast will be appreciated by the supply team as well, enabling better allocation of inventory, reducing lost sales, better inventory management and safety stock planning to improve customer service levels. Customer satisfaction is the ultimate goal which will drive profitability and sustainable management of resources.

Demand drivers are essential because there is no system with the capability of predicting them, that's were the demand planner skills take place for them to understand the products, seasonality and other inputs from marketing or sales so that they can enhance or enrich the forecast.

Identifying demand drivers involves analyzing various factors that influence consumer behavior and purchase decisions. These factors can include changes in demographics, economic conditions, new products or services, marketing campaigns, and competition. One way to identify demand drivers is to conduct market research by gathering data on consumer preferences, behaviors, and attitudes. Another method is to analyze sales data and trends to identify patterns and correlations. By understanding the demand drivers, businesses can develop effective marketing strategies, optimize pricing strategies, and make informed decisions about product development and innovation.

Identifying demand drivers relies heavily on business acumen, and the utmost clarity from the leadership of the company. One segmentation is in terms of organic drivers and in-organic drivers. Organic drivers refers mostly to external factors such as category growth, word of mouth, and seasonality, while in-organic drivers refers mostly to levers the businesses controls, such as new products, marketing campaigns, and distribution deals. For more nascent businesses, in-organic likely represents the bulk of demand volatility, because the business is in growth mode. Therefore, planners need to rely more heavily on collaborating with the teams responsible for activating and managing those levers to identify what moves the needle

Na minha experiência é necessário colabora??o interdepartamental, ajuste continuo e utilizar os históricos e análises preditivas para entender como os diferentes drivers afetaram a demanda.

In addition to statistics and technology to identify and track demand drivers. Understanding real life events that could change your demand is key. These should come from typically from your sales teams via your S&OP process. But could also come from any other stake holder in the process.

Incorporating demand drivers into your demand planning process involves analyzing factors that impact demand such as market trends, customer behavior, economic conditions, and seasonality. By identifying and monitoring these drivers, you can create accurate demand forecasts and adjust inventory levels accordingly. This helps to optimize supply chain operations, minimize stockouts, and improve customer satisfaction. It's important to use data-driven insights and collaborate with cross-functional teams to ensure that your demand planning process is effective and aligned with your business goals.

I would say keep it as simple as possible. Selecting, testing, cleaning the data exhaustively and using a suitable software. Managers really like to add as many drives as possible. It is easier to sell a plan where seemingly all variables have been considered. But this is false. Complexity is not welcome in these cases. In my experiences I have seen things that are not even worth mentioning. An interesting case was a very well prepared study, presented at a meeting of a demand planning committee, which demonstrated the sales relationship between vehicle and soft drink in Brazil. Even if the math matched, it clearly wouldn't be an adherent model that could withstand the effects of time. Drivers must explain and not justify outputs.

Incorporate seasonality, promotions, and demand drivers by analyzing historical data and market trends. Utilize statistical modeling and machine learning techniques to identify and quantify the impact of these factors on demand patterns, informing strategic decisions in demand planning. Continuously refine forecasting models to adapt to changing market conditions and optimize inventory management strategies.

In my experience, few options other than mentioned by fellow authors are 1. Capture Lag-Based Forecast Accuracy 2. Capture risks, opportunities and inputs from various stakeholders separately, this will establish accountability 3. Track and Monitor S&OP adherence of various steps in Demand Review, from statistical forecast to Consensus Demand Plan including inputs from Sales and Marketing team 4. Capture Constrained Demand post feedback from Supply Review, this will help analyze the fulfillment gap between consensus vs constrained demand

Forecast Value Add (FVA) is another good metric to evaluate if the steps or contributors in your process are improving the forecast. Keep those that are, but don’t be afraid to eliminate from the process those that do not add real value or that make the forecast worse.

Too often we use lagging KPIs to determine Planning performance. The usual ones are Casefill or Inventory holding. I challenge planning professionals to think independently of these results. I work in this order: 1. What was the plan? 2. What were our assumptions? 3. What was our intention/goal? This process is critical because you must fix what is incorrect in your Forecasting rationale; discussions; granularity of forecast; assumptions of promotional take-off, etc. Do not let the FC be driven by the result. One of my favourite measures is BIAS m-1/m-3; and tracking instances of 2 or 3 consecutive periods of positive or negative bias. This is a direct interrogation of your unit's ability to course-correct effectively.

To measure & improve demand planning performance, we can start by analyzing past sales data & identifying trends & patterns. You can also use statistical forecasting techniques to predict future demand. Once we have a baseline forecast, we can monitor actual sales against forecasted sales & adjust our plan accordingly. It's important to involve cross-functional teams in the process, such as sales, marketing & operations, to ensure a holistic approach & alignment. We can also continuously review & optimize our demand planning processes to identify areas for improvement & implement best practices.Regularly reviewing & adjusting our strategy can help increase forecast accuracy, reduce inventory costs & ultimately improve customer satisfaction.

Adicionaria 2 pontos: 1) identificar causas raiz de erros ou desvios n?o é perca de tempo e sim ganhar tempo, pois agiremos na preven??o e n?o em apagar incêndios 2) treinar os times enfatizando a importancia do desenvolvimento contínuo das habilidades no planejamento de demanda, com investimentos em treinamentos sobre novas ferramentas e metodologias de previs?o e planejamento.

Based on how the supply network is designed, demand aggregation & disaggregation plays an important role. While many companies are very good at forecasting demand at national level or key account level, when it comes to distribution centers after disaggregation it fails miserably, causing service issues and inventory imbalance in the network.

In addition to this, in my opinion, a holistic view of the entire supply chain is crucial. Demand planning does not function in isolation. Understanding the interconnectedness of various elements of the supply chain can lead to more integrated and effective demand planning. This includes collaborating with suppliers and considering their capacity and lead times in your demand planning process.

Incorporating seasonality, promotions & other demand drivers into the demand planning process is crucial for accurate forecasting.We analyze historical sales data & identify trends & patterns to forecast future demand & consider external factors like holidays, weather, & economic conditions that can influence demand. Promotions are planned in advance & integrated into the demand forecast. We use statistical methods such as time-series analysis & regression to account for seasonal variation. We regularly review & update the demand plan to reflect changing market conditions & consumer preferences. By incorporating these into the demand planning process, we can ensure optimal inventory levels, minimize stockouts & improve customer satisfaction

Incorporate seasonality, promotions, and demand drivers by analyzing historical data and market trends. Utilize advanced forecasting methods and predictive analytics to model the impact of these factors, ensuring accurate demand projections and effective inventory management. Continuously monitor and adjust strategies to adapt to changing demand dynamics and maximize profitability.

Historical Data Analysis: Analyze historical sales data to identify patterns and trends associated with seasonality, promotions, and other drivers. This analysis helps quantify the impact of these factors on demand and provides a baseline for future forecasting.