Question

Given an opportunity to acquire more information about demand planning, demonstrate the ability to employ an integrated systems of techniques to plan for future demand.

Understand the differentiation between demand planning, demand forecasting, and demand management activities.

Evaluate, select, and learn how to develop forecast models and understand various measures of accuracy and bias.

Test data for any seasonal component and calculate seasonal index

The question is listed above and the three listed below are the enablers. Have to discuss each one or how would you do it.

Answer 1

Demand planning can be defined as the function that is used to ensure the operations are timely, efficient and cost effective. Demand planning works best when we have the information and data such as qualitative and quantitative are available. Demand planning is defined as “using forecasts and experience to estimate demand for various items at various points in the supply chain”.

Demand planning and forecasting are not stand-alone processes. They are to be integrated into other aspects of operations to provide the end-product. One of the processes is S&OP. The difference between demand planning and demand forecasting is that demand forecasting is the result of demand panning. The manufacturing division of a business unit should know which product they are manufacturing first and then they can decide on how much quantities the product should be manufactured. Hence, the difference between the demand forecasting and demand planning.

Demand management has a set of processes, methodologies and requirements for companies that produce goods and services. Demand management results show the policies and programs to fluctuate and change the demand as well as fluctuate the competition available to users and consumers.

Demand management is also a stand-alone process and also the one that is integrated with S&OP.

There are three basic types:

• qualitative techniques,
• time series analysis projection
• causal models.

The first method uses qualitative data and information about special events. This method may or may not take the past data into consideration.

The second, on the other hand, focuses on patterns and pattern changes based on the time series that has happened already. Hence, this method is dependent on historical data.

The third uses highly refined and specific information about relationships between system elements and is powerful to take special events into account. As with time series analysis and projection techniques, the past is important to causal models.

These differences imply that the same type of forecasting technique is not applicable to forecast sales at all stages of the life cycle of a product—for example, a technique that relies on historical data would not be useful in forecasting the future of a totally new product that has no history.

Seasonal Indices:

Seasonal indices can be used to deseasonalise and smoothen the time plot data. That means seasonal fluctuations or patterns can be removed from the data and forecasts can be made with the future data values.