demand forecasting

 

  • SPEC takes into account temporal shifts (prediction before or after actual demand) or cost-related aspects and allows comparisons between demand forecasts based on business
    aspects as well.

  • [6] The use of an accurate demand forecasting model can result in significant decreases in operational costs for businesses, since less safety stock is required to be held.

  • The process that generates the data for all periods t that fall in quarter q is given by: the datum for period β base demand at the beginning of the time series horizon •
    τ the linear trend per quarter • the multiplicative seasonal factor for the quarter • e a disturbance term Stage 3: Data Collection[edit] Once the type of model is specified in stage 2, the data and the method of collecting data must be specified.

  • This method is mostly used in situations when there is minimal data available for analysis such as when a business or product has recently been introduced to the market.

  • The type of model that is chosen to forecast demand depends on many different aspects such as the type of data obtained or the number of observations, etc.

  • [9] In this stage it is important to define the type of variables that will be used to forecast demand.

  • The subset of data points may not be observable or feasible to determine but can be a practical method for adding precision to the demand forecast model.

  • [8] Forecasting demand can be broken down into seven stage process, the seven stages are described as: Stage 1: Statement of a theory or hypothesis[edit] The first step to
    forecast demand is to determine a set of objectives or information to derive different business strategies.

  • Conversely, when deciding on the desired forecasting model, the available data or methods to collect data need to be considered in order to formulate the correct model.

  • However, demand forecasting is known to be a challenging task for businesses due to the intricacies of analysis, specifically quantitative analysis.

  • Growth – By having an accurate understanding of future forecasts, companies can gauge the need for expansion within a timeframe that allows them to do so cost effectively.

  • Demand forecasting refers to the process of predicting the quantity of goods and services that will be demanded by consumers at a future point in time.

  • Examples of qualitative and quantitative assessments are: Qualitative assessment[edit] • Unaided judgment • Prediction market • Delphi technique • Game theory • Judgmental
    bootstrapping • Simulated interaction • Intentions and expectations survey • jury of executive method Quantitative assessment[edit] • Discrete event simulation • Extrapolation • Group method of data handling (GMDH) • Reference class forecasting
    • Quantitative analogies • Rule-based forecasting • Diffusion of innovation • Neural networks • Data mining • Conjoint analysis • Causal models • Segmentation • Exponential smoothing models • Box–Jenkins models • Hybrid models Others[edit]
    Others include: a. moving average Time series projection methods o Moving average method o Exponential smoothing method o Trend projection methods b. leading indicator Causal methods o Chain-ratio method o Consumption level method o End use
    method o Leading indicator method

  • [1] More specifically, the methods of demand forecasting entail using predictive analytics to estimate customer demand in consideration of key economic conditions.

  • [15] Another metric to consider, especially when there are intermittent or lumpy demand patterns at hand, is SPEC (Stock-keeping-oriented Prediction Error Costs).

  • Demand forecasting may be used in resource allocation, inventory management, assessing future capacity requirements, or making decisions on whether to enter a new market.

  • The method for omitting these variables is described below: Stage 5: Checking the Accuracy of the Model[edit] Calculating demand forecast accuracy is the process of determining
    the accuracy of forecasts made regarding customer demand for a product.

  • In order to forecast demand, estimations of a chosen variable are used to determine the effects it has on demand.

  • For example, a manager may wish to find what the optimal price and production amount would be for a new product, based on how demand elasticity affected past company sales.

  • In this situation, a business may consider MASE (Mean Absolute Scaled Error) as a key performance indicator to use.

  • These decisions are generally associated with the concepts of capacity, market targeting, raw material acquisition and understanding vendor contract direction.

  • In relation to the example provided in the first stage, the model should show the relationship between demand elasticity of the market and the correlation it has to past company
    sales.

  • This should enable managers to make an informed decisions regarding the optimal price and production levels for the new product.

  • Stage 7: Forecasting[edit] The final step is to then forecast demand based on the data set and model created.

  • There are several forms of forecast error calculation methods used, namely Mean Percent Error, Root Mean Squared Error, Tracking Signal and Forecast Bias.

  • Cross-sectional data used in demand forecasting usually depicts a data point gathered from an individual, firm, industry, or area.

  • Meeting goals – Most successful organisations will have pre-determined growth trajectories and long-term plans to ensure the business is operating at an ideal output.

 

Works Cited

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o ^ You can find an interesting discussion here.
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Photo credit: https://www.flickr.com/photos/seven_of9/5995136822/’]