Supply chain demand planning is the crystal ball of the business world. Imagine if every time you went grocery shopping, you knew exactly how much milk, bread, and eggs you'd need for the week ahead. That's what demand planning does for companies, but on a much larger scale.
At its core, demand planning is all about predicting what customers will want and when they'll want it.
Sounds like a guessing game, right? Yes… But not really.
Companies develop processes that forecast future customer demand for its products or services. They analyze historical sales data, market trends, and even factors like the weather to forecast future demand for their products. The goal is to ensure that they have the right amount of inventory on hand to meet customer demand without excess inventory or stockouts.
Why is Demand Planning Important?
Imagine a company that makes mugs, tumblers and coolers that keep things cold or warm for an incredibly long time. If they don't accurately predict how many people will want the next cooler, they could end up with either too many coolers sitting unsold in warehouses or not enough to meet demand, leaving customers frustrated.
How Do Brands Demand Plan?
It starts with data, technology and expertise.
Savvy brands collect mountains of data from sales figures to customer feedback to social media chatter. They’ll then use proprietary or third-party technology to crunch all that data to identify patterns and trends. This allows them to make more accurate predictions about future demand.
But demand planning isn’t just about algorithms and spreadsheets. Human expertise is crucial too. Demand planners are like the conductors of the supply chain orchestra, interpreting data, factoring in things like promotions or new product launches, and making informed decisions about how much inventory to keep on hand.
Key Steps to Achieving Effective Demand Planning
Achieving effective demand planning involves several key steps:
- Data Collection: Gather relevant data sources, including historical sales data, market trends, customer feedback and external factors like economic indicators or seasonality.
- Data Analysis: Analyze the collected data using statistical methods, forecasting models, and software to identify patterns, trends, and seasonality in customer demand.
- Demand Forecasting: Use the insights from data analysis to generate a demand forecast for each product or service, estimating future demand levels over a specified time horizon.
- Collaborative Planning: Collaborate with cross-functional teams, including sales, marketing, operations, and finance, to gather insights and validate the demand forecast based on their expertise and market knowledge.
- Scenario Planning: Conduct scenario planning to assess the potential impact of different factors, such as promotions, new product launches, or supply chain disruptions, on future demand levels.
- Inventory Optimization: Use the demand forecast to optimize inventory levels, ensuring that the right amount of inventory is available to meet customer demand while minimizing excess inventory and stockouts.
- Continuous Monitoring and Adjustments: Monitor actual sales performance against the demand forecast on an ongoing basis and make adjustments to the forecast and inventory plans as needed based on changing market conditions and business priorities.
Demand Planning Methods
There are a number of demand planning methods brands can rely on. Some will hone in on a single method while others might employ multiple.
- Qualitative Methods: These methods rely on expert judgment, market knowledge, and subjective assessments to forecast demand. Qualitative methods include market research, surveys, expert opinion polling, and Delphi* method (consensus-building technique among experts).
- Time Series Analysis: This method involves analyzing historical sales data to identify patterns, trends, and seasonality in demand. Time series analysis techniques include moving averages, exponential smoothing, and time series decomposition.
- Causal Models: Causal models incorporate external factors or causal variables that influence demand, such as economic indicators, marketing activities, or competitor actions. Regression analysis is a common technique used to build causal models.
- Predictive Analytics and Machine Learning: Advanced statistical techniques and machine learning algorithms can analyze large datasets to identify complex patterns and relationships in demand data. These methods can offer more accurate forecasts and adapt to changing market dynamics.
- Collaborative Planning, Forecasting, and Replenishment (CPFR): CPFR is a collaborative approach to demand planning that involves sharing information and coordinating forecasting efforts between trading partners, such as suppliers and retailers, to improve forecast accuracy and supply chain efficiency.
- Demand Sensing: Demand sensing uses real-time data sources, such as point-of-sale data, social media analytics, and sensor data, to detect changes in demand patterns quickly and adjust forecasts accordingly. This approach enables companies to respond rapidly to fluctuations in customer demand.
- Scenario Planning: Scenario planning involves creating multiple scenarios or "what-if" situations to assess the potential impact of different factors, such as changes in market conditions, customer behavior, or supply chain disruptions, on future demand levels.
*The Delphi method is particularly useful in situations where there is uncertainty or limited historical data available for forecasting, as it leverages the collective wisdom and judgment of knowledgeable individuals to inform decision-making. It can also help mitigate biases and promote objectivity by allowing experts to provide input anonymously and independently.
But… Demand Planning Isn't Foolproof
Unexpected events can throw a wrench in even the best-laid plans. That's where agility comes in. Brands need to be able to adjust their plans on the fly, whether it's ramping up production to meet unexpected demand or scaling back when sales aren't meeting expectations.
Supply chain demand planning is a delicate balance of data, technology and human judgment, all aimed at ensuring that brands have the right products in the right place at the right time.