Forecast and Advanced Forecast Dashboards

Forecasting plays a pivotal role in the supply chain industry. It is an essential component of all supply chain related organisations. It can make or break an organisations ability to remain profitable. If properly implemented it contributes significantly to a company’s competitiveness, reduces cost, maintains and contributes to increasing customer base. For simplicity purpose, here we have categorised forecasting into 2 main categories, these categories are Sales Forecast and Demand Forecast which some also refer to as Net Requirement Forecast. Sales forecast is an essential part of the cyclical demand review activity where product managers and the sales and marketing department have critical inputs. Inputs from product managers and the sales and marketing department provide the supply chain function with some visibility as to the anticipated sales for the a given period, depending on the practice employed by your organisation the period could range from 3 months to 18 or 24 months. It is essential to planning production or procurement capacity that meets forecasted demands. However, with thousands of SKUs and big volumes of data involved, reviewing each SKU one at a time and determining respective sales forecast quantity to be applied becomes a daunting task. A solution to this overwhelming task is to implement Forecast Regression Modelling. Applying Regression modelling to sales forecast saves a lot of time, and from a mathematical perspective is more accurate than manual sales forecast extrapolation. When Regression analysis has been completed, sales forecast output data can then be tweaked to accommodate known variables, such as new customers, lost customers, product phase out and promotional campaigns.     


On the other hand, demand forecast which is also known as net requirement forecast is a follow up from the sales forecast activity and focuses on how much quantity of stock should be ordered or produced. Demand forecast cover a period of 12 to 24 months depending on the practice your organisation may choose to adopt. Demand forecast is an essential part of short to medium term capacity planning. In demand forecast extrapolation, current stock on hand, current back orders, sales forecast quantities, and pipeline inventory are taken into consideration.


Depending on your industry, there are various forecast methods that can be applied. Time Series and Causal Modelling are 2 of the commonly used forecast methods. Other forecasting techniques are the Panel Approach, The Delphi method, and Scenario Planning. However, we will refrain from discussing these methods as they are more used in qualitative forecasting rather than quantitative forecasting. In its simple form, Time Series forecasting technique applies data extrapolation that isolates known variables and attempts to predict the future given the past. It is one of the most commonly used techniques in forecasting within the supply chain industry.


Causal Modelling applies more depth in understanding the strength or weakness of the relationship between variables and then attempts to factor in those variables that the strength of their relationship is high. Applying Regression model helps makes using the Causal Model less complex.  In Time Series method of forecasting Moving Average and Exponential Smoothing can be applied in understanding causes and relationships of variables. If you are thinking of implementing or increasing the effectiveness of your current forecast dashboards, by all means get in touch with us today and we will help you do just that.

Supply Chain Consulting


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