Demand management is the supply chain management function that equates the capacity of the supply chain with customers’ requirements (Nummelin, Sulankivi, Kivinemi, & Koppinen, 2011). Provided that the underlying process is efficient, management can match both demand and supply and execute the plan with minimal disruptions. An efficient and adaptable demand management process makes a business more proactive to anticipated demand, and more reactive to unanticipated demand. To reduce costs and ensure consistent planning, it is important to reduce demand variability. Therefore, an important component of demand management is finding ways to reduce demand variability and improve operational flexibility.
Importance of demand management
Developing an effective demand management strategy is necessary for businesses to optimize the supply chain process. It helps in the convergence of demand and supply functions. Developing an effective demand management strategy leads to:
- reduction of costs,
- enhancement of revenue and,
- efficient streamlining of operations.
According to Basnet and Wisner (2012) demand management, as well as the concept of demand per se, have not been well understood by the widespread dissemination of supply chain concepts. Furthermore, many businesses have failed to realize that achieving supply chain coordination is not possible without adequate knowledge of demand management.
Demand planning process
Contemporary businesses devise their demand planning process based on the inputs obtained from their sales and marketing teams. However, they also take into consideration the risk factors before devising a plan. The goal of demand planning process is to make use of available resources in an optimum way in order to meet the market demand.
Feldman (2001) was one of the earliest pioneers of demand planning processes in modern businesses. According to him, there are four basic steps in the process:
- Demand planning
- Supply planning
Bonde and Hvolby (2005) elucidated these four steps in the context of
Modelling is the first step of demand management
This step requires the demand planning team of a business to obtain the necessary data from other teams related to sales and demand drivers such as market trends, innovation and social media strategy. This data is then either analysed manually or entered into a demand planning software, where it undergoes:
- Model review: the current demand model is reviewed to check whether it is still valid or not.
- Reality assessment, test and simplification: the team checks if demand indicators are valid or not. The validity is established on the basis of statistics and logic.
- Mathematical modelling: it involves creating equations to check the impact of the demand, based on historical data.
- Data collection and preparation: the data is entered into the software for devising a demand planning strategy.
Next step is forecasting
This step, according to Bonde and Hvolby (2005) involves four functions:
- Final set up: modifying the demand planning software as per the reviewed indicators.
- Normalization of data: for example, if a business has introduced a new marketing campaign to increase sales, it has to continue the campaign to maintain the increased sales. Otherwise, the previous sales data should be considered.
- Quantitative forecasting: in this, the software generates the forecasted output.
- Addition of causal factors: here demand impact values are added to the normalized forecast.
The next steps are demand planning and supply planning
These steps involve creating the actual demand and supply plan. It is completed in four stages.
- Review the forecasted data.
- Modify demand indicators as per recent data.
- Develop a new demand plan.
- Review flaws before being put the plan into action. The review is done on the basis of:
- whether the planned forecast exploits the market potential to the fullest and,
- optimal utilization of resources.
There are many applications that aid in developing the demand and supply plan proposed by Bonde and Hvolby (2005). Relex Solutions and Dynasys offers some of these applications.
Also, it is important to align the demand and supply to increase flexibility and reduce variability. Similarly, the development of a contingency plan is a vital component of the strategic demand management process. This is done to respond to significant internal or external events that disrupt the balance of supply and demand. Furthermore, it helps to determine how to react in a shutdown, or interruptions in the supply of raw materials. Thus, it will be more profitable to plan reaction procedures prior to possible disasters.
Efficient demand management is important for an efficient supply chain
The strategies mentioned above have a considerable impact on the process of demand management. Lack of accurate information is among some of the challenges faced in demand alignment process of a supply chain. Consequently, it leads to inefficient customer service, poor stock rotation, and high obsolescence rate aggravated by the wide diversity of products. Employment of proper demand management technique lowers the number of invoice disputes by bettering the problems of missed delivery dates and incomplete orders. Finally, it also ensures improved asset utilization and facility rationalization, thereby leading to lower running costs. Thus, managing proper demand management is necessary for the efficient management of
- Basnet, C., & Wisner, J. (2012). Nurturing internal supply chain integration. Operatiokns and Supply Chain Management: An International Journal, 5(1), 27–41.
- Bonde, H. and Hvolby, H. (2005). The demand planning process. Journal on Chain and Network Science, 5.
- Feldman, B. (2001). Can you manage your product demand. Advisor.com. April 2001, http://doc.advisor.com/doc/07554.
- Nummelin, J., Sulankivi, K., Kivinemi, M., & Koppinen, T. (2011). Managing Building Information and Client Requirements in Construction Supply Chain – Constructor’s View. In CIB 2011. Sophia Antipolis, France.
- An overview of the annual average returns and market returns (2000-2005) - October 22, 2020
- Introduction to the Autoregressive Integrated Moving Average (ARIMA) model - September 29, 2020
- The stakeholder theory of Corporate Social Responsibility - September 21, 2020