Demand forecasting is defined as an approach used for analyzing future demand in comparison to the previous ones. The purpose of demand forecasting is to apply future planning and decision in the domain of finance, logistics, operation, and sales. Companies use a qualitative method of forecasting to analyse and evaluate the opinion of experienced staff rather than focusing on numerical values (Dwyer, et al., 2012). These methods are used for predicting any short term or internal forecasting on the basis of summative feedback of departmental heads. On the other hand, the quantitative forecasting technique deals with numerical data focus on the projection of trends on the basis of historical figures of the business. This method of forecasting is consistent and useful for long term scenario planning of the company.
The opinion of the experts in the company helps to forecast the internal parameter of an organization, whereas quantitative data in the form of customer surveys are used for reflecting the sales forecast (Frechtling, 2012). Small scale companies prefer qualitative forecasting method as it is simple in nature and cost-effective. On the other hand, lean manufacturing and large scale organizations are proficient in using quantitative forecasting.
Quantitative forecasting techniques in logistics
The quantitative forecasting technique is defined as the process of conducting forecasting on the basis of numerical data derived from the company’s history (Fleischmann, et al., 2012). There are mainly two types of forecasting techniques; time series model and associative model.
The Time series model helps to analyze one specific parameter of forecasting over a period of time.
For example, a business can forecast the quantity of raw materials to order with time series analysis of the previous five years.
Moving average time series forecasting method
Moving average deals with the normal average value which is considered as the basic calculation for forecasting. It allows us to remove the oldest values from the data and add new values. This makes the average move over time. Moving averages method can be used to reflect seasonality in demand.
For example, businesses can use week-by-week sales data to predict sale for the coming week using moving averages.
Experimental smoothing deals with the weight of the components during the year. It also focuses on the importance of the parameters.
For example, as the newer data emerges, the older data becomes less important. This makes them less “weighted” than the new data. This method is very similar to moving averages. Therefore it is equally popular among retail firms to forecast demand, sales and expected profit margin (Ghiani, et al., 2013).
Lastly, trend projection focuses on graphical representation which provides an overview of the future trend by analyzing the least-square value. This method requires data for a longer time period. The main defining element of this method is that it assumes that all the factors that played a role in past trends will continue to be influential in the future too. New companies can use long term data of similar firms in the market to forecast trends.
Quantitative forecasting also helps to establish the relationship between forecasted variables.
Qualitative forecasting techniques in logistics
The qualitative forecasting method is characterized as the approach of analysis of data gathered from the opinions of an expert or experienced professionals in an organization. The qualitative forecasting method focuses on summative approaches for undertaking the forecasting process (Punch, 2013).
Opinion of experts
The opinion of the departmental heads is recorded in the presence of the third party which is accumulated for forecasting. The opinion of the staff is recorded in the closed room in a single manner for assessing the validity and reliability of their statement.
The principle of the Delphi method aims to validate the forecast. The estimates are to be reviewed until the consensus is reached.
For example, a small scale business can forecast the amount of inventory it needs to hold for the next two months. The results are analyzed by all experts or department heads in a group discussion. Group discussions are held until they reach a consensus.
Qualitative forecasting techniques are used for identifying any inter-organizational issue that might disrupt the regular business process (Montgomery, et al., 2015). Market research and market analysis are also considered as an important forecasting technique that is used to identify the customers’ demand and trend to deliver the product in that dimension. This technique also assists in performing the operation of logistics by preparing the product or service according to that dimension.
Difference between qualitative and quantitative forecasting techniques
There are several differences between qualitative and quantitative forecasting techniques and their use in supply chain management or logistics. Qualitative forecasting deals with the opinion of managers or customers survey which helps to get an overview of forecasted information. Whereas quantitative forecasting deals with numerical data. Qualitative forecasting deals with summative data as it is considered less accurate than that of the quantitative analysis. Qualitative forecasting is considered biased as the data is collected manually, whereas quantitative data relies upon previous performance records (Guest, et al., 2012). Qualitative forecasting is applicable for short term whereas quantitative is applicable for long term decisions.
Therefore, both qualitative and quantitative forecasting method is used for demand forecasting which has become crucially important in the context of managing the logistics. The quantitative forecasting technique concerns numerical data that focuses on the projection of customer trends towards other parameters of the business whereas qualitative forecasting techniques are used through gathering experts’ opinions for forecasting any figure and undertake any strategy related to performing a business function.
- Anderson, D. et al., 2012. Quantitative Methods for Business (Book Only). 1 ed. London: Cengage Learning.
- Dwyer, L., Gill, A. & Seetaram, N. e., 2012. Handbook of research methods in tourism: Quantitative and qualitative approaches. London: Edward Elgar Publishing.
- Fleischmann, B., van Nunen, J., Speranza, M. & Stähly, P. e., 2012. Advances in distribution logistics (Vol. 460). 1 ed. London: Springer Science & Business Media.
- Frechtling, D., 2012. Forecasting tourism demand. 1 ed. London: Routledge.
- Ghiani, G., Laporte, G. & Musmanno, R., 2013. Introduction to logistics systems management. 1 ed. London: John Wiley & Sons.
- Guest, G., Namey, E. & Mitchell, M., 2012. Collecting qualitative data: A field manual for applied research. 1 ed. New Delhi: Sage.
- Montgomery, D., Jennings, C. & Kulahci, M., 2015. Introduction to time series analysis and forecasting. 1 ed. London: John Wiley & Sons.
- Punch, K., 2013. Introduction to social research: Quantitative and qualitative approaches. 1 ed. New Delhi: sage.
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