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

  • 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

[‘o Acar, A. Zafer; Yilmaz, Behlül; Kocaoglu, Batuhan (2014-06-16). “DEMAND FORECAST, UP-TO-DATE MODELS, AND SUGGESTIONS FOR IMPROVEMENT AN EXAMPLE OF A BUSINESS” (PDF). Journal of Global Strategic Management. 1 (8): 26–26. doi:10.20460/JGSM.2014815650.
ISSN 1307-6205.
o ^ Adhikari, Nimai Chand Das; Domakonda, Nishanth; Chandan, Chinmaya; Gupta, Gaurav; Garg, Rajat; Teja, S.; Das, Lalit; Misra, Ashutosh (2019), Smys, S.; Bestak, Robert; Chen, Joy Iong-Zong; Kotuliak, Ivan (eds.), “An Intelligent
Approach to Demand Forecasting”, International Conference on Computer Networks and Communication Technologies, Singapore: Springer Singapore, vol. 15, pp. 167–183, doi:10.1007/978-981-10-8681-6_17, ISBN 978-981-10-8680-9, retrieved 2023-04-27
o ^
Ivanov, Dmitry; Tsipoulanidis, Alexander; Schönberger, Jörn (2021), Ivanov, Dmitry; Tsipoulanidis, Alexander; Schönberger, Jörn (eds.), “Demand Forecasting”, Global Supply Chain and Operations Management: A Decision-Oriented Introduction to the Creation
of Value, Cham: Springer International Publishing, pp. 341–357, doi:10.1007/978-3-030-72331-6_11#doi, ISBN 978-3-030-72331-6, retrieved 2023-04-27
o ^ “Demand Forecasting: An Industry Guide”. Demand Caster.
o ^ “The Advantages of Demand Forecasting”.
Small Business – Retrieved 2023-04-27.
o ^ Diezhandino, Ernesto (2022-07-04). “Importance and Benefits of Forecasting Customer Demand”. Keepler | Cloud Data Driven Partner. Retrieved 2023-04-27.
o ^ Hamiche, Koussaila; Abouaïssa, Hassane;
Goncalves, Gilles; Hsu, Tienté (2018-01-01). “A Robust and Easy Approach for Demand Forecasting in Supply Chains”. IFAC-PapersOnLine. 16th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2018. 51 (11): 1732–1737. doi:10.1016/j.ifacol.2018.08.206.
ISSN 2405-8963.
o ^ Jump up to:a b Wilkinson, Nick (2005-05-05). Managerial Economics: A Problem-Solving Approach (1 ed.). Cambridge University Press. doi:10.1017/cbo9780511810534.008. ISBN 978-0-521-81993-0.
o ^ Sukhanova*, E.I.; Shirnaeva, S.Y.;
Zaychikova, N.A. (2019-03-20). “Modeling And Forecasting Financial Performance Of A Business: Statistical And Econometric Approach”. The European Proceedings of Social and Behavioural Sciences. Cognitive-Crcs: 487–496. doi:10.15405/epsbs.2019.03.48.
S2CID 159058405.
o ^ Roodman, Gary M. (1986). “Exponentially smoothed regression analysis for demand forecasting”. Journal of Operations Management. 6 (3–4): 485–497. doi:10.1016/0272-6963(86)90019-7.
o ^ Ngan, Chun-Kit, ed. (2019-11-06). Time
Series Analysis – Data, Methods, and Applications. IntechOpen. doi:10.5772/intechopen.78491. ISBN 978-1-78984-778-9. S2CID 209066704.
o ^ Johnston, Richard G. C.; Brady, Henry E. (2006). Capturing Campaign Effects. Ann Arbor: University of Michigan
Press. ISBN 978-0-472-02303-5.
o ^ Hyndman, R.J., Koehler, A.B (2005) ” Another look at measures of forecast accuracy”, Monash University.
o ^ Hoover, Jim (2009) “How to Track Forecast Accuracy to Guide Process Improvement”, Foresight: The International
Journal of Applied Forecasting.
o ^ You can find an interesting discussion here.
o ^ Martin, Dominik; Spitzer, Philipp; Kühl, Niklas (2020). “A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs”.
Proceedings of the 53rd Annual Hawaii International Conference on System Sciences. doi:10.5445/IR/1000098446.
Photo credit:’]