Demand planning vs demand forecasting
Demand planning is a holistic process that uses demand forecasting. However, it goes beyond predicting demand by integrating it with supply chain strategies, production planning, inventory management, and distribution. Demand forecasting is a subset of demand planning that focuses only on predicting future customer demand based on historical data, market trends, and external factors.
Demand forecasting provides the data and insights that drive the demand planning process. Demand planning uses forecasts to make actionable decisions about production, inventory levels, and resource allocation. For example, a company may predict that consumer demand will go up by 20% based on market research. This is demand forecasting. Demand planning goes further to use this forecast to adjust inventory levels, increase production levels, and make arrangements for timely delivery.
Step-by-step guide to effective demand planning
A well-structured demand planning process not only predicts future demand but also helps in aligning various aspects of your business:
Step 1: Define your objectives
Before starting the demand planning process, it's important to define the objectives and scope of your planning efforts. This will ensure all stakeholders are aligned and working toward the same goals.
Identify what you want to achieve with your demand planning (e.g. do you want to reduce stockouts, optimize inventory levels, or enhance customer satisfaction?).
Determine short-term, medium-term, and long-term forecasts based on your business's needs (e.g., monthly, quarterly, yearly).
Focus on critical performance indicators (KPIs) like inventory turnover, forecast accuracy, and lead time.
Step 2: Collect and analyze historical data
Demand planning starts with accurate data. Historical sales data, along with any relevant market data, form the foundation of your demand forecast.
Collect data on sales trends, seasonality, promotions, and other factors that influence demand.
Look for recurring patterns in sales that can help you predict future demand (e.g., holiday spikes or weather-related fluctuations).
Consider including external data such as economic indicators, competitor actions, and market trends.
Step 3: Create and review demand forecasts
Next, create the demand forecasts for the chosen periods (monthly, quarterly, or yearly). This step may involve working with various stakeholders, including sales, marketing, and finance teams, to refine the forecasts.
Based on historical data and chosen methods, generate demand forecasts for the coming months or years.
Work with sales, marketing, and supply chain teams to ensure the forecast is aligned with business expectations and any planned promotions or product launches. Look out for any expected changes, such as new product launches, marketing campaigns, or shifts in customer preferences.
Step 4: Align findings with supply chain and inventory plans
Based on demand forecasts, calculate the optimal inventory levels needed for each product. Factor in lead times, safety stock, and production cycles. Share your demand forecasts with suppliers to ensure they can meet your inventory requirements.
Step 5: Monitor and adjust forecasts regularly
Demand planning is not a static process. You will need to continually update the process as new data comes in. Regular monitoring and adjustments ensure that your demand plan remains relevant and accurate. Compare your actual sales with forecasts to measure accuracy and identify areas of improvement.
Use KPIs like forecast accuracy, inventory turnover, and customer service levels to track the success of your demand planning efforts.
Be prepared to make adjustments for changes in the market, customer behavior, or external factors.
Step 6: Use technology and tools
Using the right technology can streamline the demand planning process and improve accuracy. There are several tools and software platforms that can support demand forecasting, inventory management, and collaboration. Invest in tools like SAP Integrated Business Planning (IBP) or Oracle Demantra for accurate forecasting and seamless integration across departments.
Machine learning tools can improve accuracy by analyzing large datasets and providing real-time insights.
You can also integrate demand planning with automated inventory replenishment systems to ensure timely procurement.
Challenges with demand planning
Here are some common challenges in demand planning and strategies to address them:
- Inaccurate data
Accurate demand planning depends on high-quality, consistent data. Incomplete, outdated, or inaccurate data can lead to poor forecasts and operational inefficiencies. To solve this, you want to use tools that capture real-time data from sales, inventory, and external sources.
- Changing consumer preferences
Consumer preferences and purchasing habits can shift quickly, making it difficult to predict demand accurately. Use market research, social media analytics, and customer feedback to stay informed about changes in consumer behavior. Be flexible and quickly adapt to shifts in customer demand.
- No cross-functional collaboration
Demand planning requires input from multiple departments, such as sales, marketing, finance, and supply chain. Miscommunication or siloed operations can lead to conflicting forecasts and plans. To solve this, you want to foster communication between departments through regular meetings and shared goals. Invest in software that facilitates real-time collaboration across teams.
Tips for successful demand planning
Here are some actionable tips that will help make your demand planning effective:
- Regularly update historical sales data and ensure that it reflects any recent market or customer behavior changes. Gather data from multiple sources, including sales, marketing, and supply chain systems, to get a comprehensive view.
- Demand planning is not a one-department job. Sales, marketing, supply chain, and finance teams need to collaborate for accurate forecasting and alignment with business objectives. Hold regular meetings with cross-functional teams to share insights and make adjustments.
- Invest in demand planning software or ERP systems that integrate forecasting, inventory, and supply chain management.
- Regularly assess your demand planning process and make adjustments based on past performance and lessons learned.
Demand planning software
Demand planning software uses advanced algorithms, statistical analyses, and data integration to predict future demand for products and services more accurately. When selecting a demand planning software, consider features that allow for highly accurate demand forecasts, reliable supply chain analytics, effective budgeting, and predictive analysis. Here are a few demand planning software you can invest in:
Anaplan
Anaplan is a top-rated demand planning solution that offers powerful forecasting capabilities. Key features include:
- Hyperblock technology for real-time performance contextualization
- Ability to forecast future outcomes for faster, confident decision-making
- Connected strategy and planning across departments
SAP Integrated Business Planning
SAP's Integrated Business Planning solution is a comprehensive supply chain management tool that includes robust demand planning features.
Key features include:
- Real-time cloud-based platform
- Support for demand response, supply planning, and inventory management
- Supply chain analytics for informed decision-making
Netstock
Netstock is an inventory planning software used by over 2,000 customers globally. Its key strengths include:
- Comprehensive inventory optimization
- Automated replenishment recommendations
- Integration with various ERP systems
Workday Adaptive Planning
Workday Adaptive Planning (formerly Adaptive Insights) is a financial planning and reporting tool that also offers robust demand planning capabilities. Key features include:
- Automated budgeting and forecasting
- Financial reporting capabilities
- Integration with third-party products like Salesforce
Final thoughts
By implementing robust demand planning processes, you can better prepare for future sales and optimize your operations to meet customer needs more effectively. This leads to higher profitability, competitiveness, and overall business performance.
Frequently Asked Questions (FAQs)
What are the 3 major activities of demand planning?
The three major activities of demand planning include
- Collection of data: The goal is to gather comprehensive data that provides insights into past performance and future trends.
- Forecasting and prediction: Once the data is collected and analyzed, the next major activity is to generate forecasts and predictions. Here, the goal is to predict future demand accurately, considering both internal and external factors.
- Planning and implementation: Here, you want to ensure that the forecasted demand translates into effective operational decisions across the supply chain. This can look like aligning supply chain operations like production schedules and inventory levels with the predicted demand.
What are the 4 steps of the demand planning process?
Four major steps of the demand planning process include:
- Gathering and collecting data
- Forecasting and making predictions based on collected data
- Planning and implementing findings to improve operations
- Continuously review and make adjustments to maintain accuracy and relevance.
What is an example of demand planning?
Tastykake, a popular bakery brand, faced significant challenges with its demand planning process in 2005. Tastykake transformed its approach by
- first gathering historical sales data, market research findings, and competitor analysis.
- Developing preliminary forecasts based on this data, integrating both internal and external factors.
- Implementing a formal forecasting process using SAP software, which helped align supply chain operations with anticipated demand.
- Optimizing inventory levels, production schedules, and distribution strategies based on the new forecasts.
- Regularly reviewing actual sales performance against forecasts allowing them to refine their models over time.
In just two years, the average error rate dropped from 15% to 8% enabling better decision-making.
