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  • Demand Forecasting | Sales Forecasting | Business Forecasting

    According to APICS (American Production and Inventory Control Society) Dictionary,


    Forecast is an estimate of future demand. A forecast can be constructed using quantitative methods, qualitative methods, or a combination of methods, and a forecast can be based on extrinsic (external) or intrinsic (internal) factors. 


    Although the term forecast can be applied to many things such as Weather forecast, economic growth forecast and more, here we are going to focus on forecast and forecasting as used in businesses and again related particularly to the forecast of products sold to their customers. It is used in the practice of Customer Demand Planning in every day business forecasting for manufacturing, distribution and retail businesses. The discipline of business forecasting, also sometimes referred to as supply chain forecasting, embraces both statistical forecasting and judgemental forecasting using a consensus process.
    Sales Forecasting or Demand Forecasting is the business function that attempts to predict sales and use of products so they can be purchased or manufactured in appropriate quantities in advance. In Supply chain management forecasts are used to make sure that the right product is at the right place at the right time. Accurate forecasts will help manufacturers, distributors and retailers reduce excess inventory and therefore increase profit margin. Accurate sales forecasting will also help them meet consumer demand.

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    Sales and Demand Forecasting Methods and Techniques


    To Improve Forecast Accuracy

    The forecasting methods and techniques for predicting sales requirements or customer demands fall into three major categories. Businesses use them (some of them typically through use of good software) with an objective of improving forecast accuracy.
    Judgemental (or Qualitative) Forecasting Methods: Judgemental Forecasting Methods rely on human intuition rather than statistical analysis of historical data. Judgmental forecasting methods incorporate intuitive judgements, opinions and subjective probability estimates. Examples of judgemental forecasting methods include:

    Time Series Forecasting Methods: Methods that patterns (typically cyclical, random, seasonal and trend patterns) and extrapolate them into the future. Time Series Forecasting Methods are also called Statistical, Mathematical or Quantitative methods. The most common Time Series Forecasting Methods include:

    We have included a number of articles on our website on the subject of Time Series Forecasting Methods. They include:

    Multivariate Forecasting Techniques (Causal / econometric forecasting methods): Methods that capture the relationship between the dependent variable (the variable to be forecasted) and independent (explanatory) variables. Multivariate models can also include Time Series components. These methods use the assumption that it is possible to identify the underlying factors that might influence the variable that is being forecast. For example, sales of umbrellas might be associated with weather conditions. If the causes are understood, projections of the influencing variables can be made and used in the forecast.
    Examples of Multivariate methods are:


    Forecast accuracy a measurement of forecast usefulness, often defined as the average difference between the forecast value to the actual value
    Forecast bias Tendency of a forecast to systematically miss the actual demand (consistently either high or low).
    Forecast error the difference between actual demand and forecast demand, stated as an absolute value or as a percentage.
    Forecast horizon the period of time into the future for which a forecast is prepared.
    Forecast interval or Forecast Period the time unit for which forecasts are prepared, such as week, month, or quarter.
    Forecast management the process of making, checking, correcting, and using forecasts. It also includes determination of the forecast horizon.
    Forecast mean absolute percentage of error (FMAPE) the absolute error divided by actual demand for  periods, where absolute error is the variation between the actual demand and the forecast for the period expressed as a positive value (without regard for sign).
    For a very good presentation on the subject of measuring forecast accuracy please click the link below.
    Mechanics of calculating forecast accuracy

    (Collaborative Planning, Forecasting, and Replenishment – From Wikipedia, the free encyclopaedia)
    Collaborative Planning, Forecasting and Replenishment (CPFR) is a concept that aims to enhance supply chain integration by supporting and assisting joint practices. CPFR seeks cooperative management of inventory through joint visibility and replenishment of products throughout the supply chain. Information shared between suppliers and retailers aids in planning and satisfying customer demands through a supportive system of shared information. This allows for continuous updating of inventory and upcoming requirements, making the end-to-end supply chain process more efficient. Efficiency is created through the decrease expenditures for merchandising, inventory, logistics, and transportation across all trading partners. For some useful information on CPFR and further links please visit:
    Collaborative Planning, Forecasting & Replenishment (CPFR‚ ®) Committee
    The biggest challenge in the area of forecasting for any business is how to improve the accuracy of its sales / demand forecasts. Low forecast accuracy means higher inventories, higher costs and lower customer service. Improving forecast accuracy can lead to lower inventories, lower costs and higher customer service. While improving forecast accuracy is not an easy task, below, we have provided links to a number of articles which you may find useful.
    Nine Golden Rules of forecasting
    Improving Forecast Accuracy by Selecting Right Forecasting Level
    The Forecasting Canon – Nine Generalization to Improve Forecast Accuracy
    Pop Goes the Forecast