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How to Create an Accurate Demand Forecast

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    Can you predict the future? While many, from stock traders to meteorologists may claim to be able to, there’s really no crystal ball that can ensure accuracy. Still, businesses must forecast demand.  After all, demand projections are critical to customer satisfaction and much more. 

    While planning to meet future demand might appear straightforward, it really isn’t. It wasn’t even close to being a simple exercise under the best conditions before COVID-19 brought the planet to a standstill. 

    In a post-pandemic world, rapid change and many different variables can quickly complicate or even derail your entire operation. As such, it’s vital to invest significant time and resources to overcome one of the most critical challenges in supply chain management. 

    What is Demand Forecasting?

    At its most basic, demand forecasting is a method of predicting future demand for a product or service. The accuracy of the projections depends on large volumes of quality data, methods for calculations, and available expertise. If your data is clean and reliable, you stand a better chance of making accurate predictions.

    To ensure accuracy, supply chain managers often use different types of sales forecasts for different demand forecasting methods. They also explore various external factors that can potentially influence the outcome.

    What Are the Different Types of Demand Forecasting?

    As alluded to above, demand planning often relies heavily on historical sales data. Other factors include geopolitics, seasonal demand, market and economic trends, and these days COVID-19 related challenges. 

    Different types of demand forecasts include the following:

    • Short-term forecasts (which usually provide the highest level of  accuracy)
    • Long-term forecasts (for inventory management)

    Short-Term Forecast

    Demand planning that covers a whole year is considered a short-term forecast. This projection can be internal and external, with the latter looking at economic and market changes, consumer spending patterns, and more. 

    Long-Term Forecast

    Long-term forecasts project demand for up to four years. This, too, can be internal and external. However, internal demand forecasts concentrate on the capacity of internal supply chains and personnel. 

    This approach helps answer critical questions about available resources to meet seasonal demand. It’s vital because accurate forecasts will depend on both internal and external forecasts.

    Active Forecast vs. Passive Forecast

    We can also separate it into active and passive demand forecasts. For example, active forecasting is dynamic, while projections based on historical sales data are passive. 

    However, this only works well for established players in the market with consistent demand for their products. Active demand forecasts are vital for smaller players as they won’t have enough historical data to make accurate predictions.

    That said, even established corporate giants engage in active demand forecasting to better manage their inventory. This approach also helps enhance supply chain management protocols to boost market share.

    Quantitative Forecasting vs. Qualitative Forecasting

    Quantitative Forecasting

    Quantitative forecasting is what supply chain managers often do when they pour over historical data related to supply chain performance, customer demand, seasonal demand, and more. 

    However, it’s important to note that past events don’t exactly guarantee that qualitative projections are correct. Instead, they are essentially expert opinions on both external and internal predictions.

    Qualitative Forecasting

    Quantitative forecasting often uses both big data and machine learning for the supply chain management. 

    Leading quantitative forecasting methods include:

    • Barometric forecasting (using current data to project future demand through statistical analysis)
    • Econometric forecasting (fusing together demand data with external elements that can influence demand. This quantitative approach is highly sophisticated compared to some other methods listed here)
    • Exponential smoothing (using historical data as an input and seasonal variations in sales)
    • Trend projection (using historical data with growth patterns for short term projections)
    • Regression analysis (from simple to complex analysis using internal and external data)
    • Market research (leveraging data about market trends and opportunities)
    • The Delphi method (or expert method where an assembled panel of experts arrive at a substantial agreement.

    All the above lend themselves to demand planning platforms powered by machine learning. This approach helps save time and resources while updating their demand forecasts.

    Both quantitative and qualitative methodology come with their own sets of advantages and disadvantages. As such, the best approach for your business might be a mix of both.

    Step 1: Have a Clear Understanding of the Questions That Demand Answers

    Before you make a single prediction, it’s important to have in-depth knowledge of the questions that you must answer. This approach will help you determine which type of demand forecast(s) is right for your business.

    Your questions also communicate assumptions. You must derive these assumptions or starting values both carefully and statically and analyze them. Once evaluated and communicated, these assumptions will form the foundation of forecast quality.

    Your demand forecasting team members will also have the general context for building the initial forecast model. These assumptions should also include the following:

    • Size of the target market (reflecting the number of buyers)
    • Percentage of the target audience that will convert
    • Timing of purchases (including seasonal and economic cycles)
    • Frequency of repeat and/or replacement purchases

    A supply chain manager can’t do this alone. They must depend on the different teams’ collective knowledge and expertise to validate all assumptions before kicking off the projection process.

    Step 2: Set Clear Goals

    Before collecting and analyzing data, have a plan and prioritize what you decide to accomplish. After all, collecting data for the sake of data collection can be counterproductive.

    Step 3: Get All Stakeholders to Buy-In

    Once you’re ready to start rolling, it’s best to first get everyone involved. Making accurate projections requires a team effort. So, get your marketers, sales teams, engineers, and c-suites involved. 

    Step 4: Collect and Analyze Data

    The forecasting method (or methods) you choose will determine what type of data you must collect. The more data you gather, the better. During this exercise, it’s also vital to consider all the internal and external factors, including demand.

    Once you start analyzing the data, you will identify patterns and trends to make predictions. By adding qualitative analysis into the mix, you can plan and adjust your operations accordingly. 

    It’s both an art and a science when it comes to formulating a winning supply chain strategy.

    While it’s certainly a lot of work, it’s worth the effort to ensure that your supply chain functions like a well-oiled machine regardless of what’s going on in the world. The more you do it, the better you will get with experience.

    To learn more about our manufacturing, prototyping, and our supply chain management experience, reach out to our in-house expert, Connor!

     Connor Lamb Sales Manager (Midwest) 231-780-7354
    Written by Mary Iannuzzi

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