Increasing profitability and achieving recognition among the target audience by fostering customer satisfaction are crucial goals for any for-profit business. Demand planning (DP), a set of practices for forecasting customer demand, is among the competency areas that promote success in reaching both goals. The purpose of this response is to reflect on the positive impacts of collaborative forecasting and assessing DP contributors’ effectiveness on companies’ success in revenue generation.
The so-called collaborative forecasting process is the first best practice that deserves discussion. In the context of DP, collaboration refers to bringing together the perspectives of diverse internal participants, such as product management, project planning, customer service, and sales professionals (Kamal, 2013). In this approach, demand forecast inputs from large groups of customers are also considered valuable (Kamal, 2013).
Generally, this practice can affect profitability by increasing demand forecasts’ accuracy. Collaborative DP processes require analyzing data peculiar to sales history collectively and at diverse levels of the organizational hierarchy. This can create more opportunities for applying critical thinking and correcting initial and one-dimensional conclusions. Specifically, the inclusion of sales personnel in data collection for DP might prevent the production of unrealistic and flawed demand projections since these employees are capable of generalizing their actual observations of customer behavior. The collaborative approach supports the precision of forecasts, which inevitably leads to fewer excess inventory mistakes and lower overhead costs, thus increasing profitability.
To continue, the customer-reported data in collaborative forecasting significantly contributes to the approach’s profitability-related effects. As per Kamal (2013), the practice centers on collecting data from large customer groups in an electronic format. The element’s specific value may include businesses’ improved ability to capture unpredictable demand variability patterns stemming from fashion trends that grow progressively, the popularization of sustainable consumption philosophies in the target audience, or similar factors. Therefore, attention paid to data from customers, including feedback and future purchase plans, also adds to internal forecasts’ accuracy, enabling businesses to concentrate on the most profitable products or services in production volume decisions.
The second best practice is linked with the first one and involves assessing each DP process participant’s individual effectiveness. To achieve this, Kamal (2013) recommends evaluating the accuracy of predictions and data from individual contributors, including sales representatives, marketers, and organizational planning professionals. This practice is conducive to greater profitability by promoting the identification of contributors who might require additional training or guidance in order to improve forecasting abilities. Aside from that, data from individual evaluations will reveal specific professionals who need to work under supervision or whose predictions/calculations should be rechecked by others to prevent unintentional mistakes.
If based on the correctly identified DP proficiency levels of the contributors, the process of peer approval will eventually increase demand forecasts’ resulting effectiveness and applicability to the real-world market situation. As opposed to accepting DP-related information from employees without assessing its quality and tracking its effectiveness, the practice in question enhances profit generation by means of motivating more thoroughly analyzed forecast additions.
The quality of analysis eventually increases predictions’ effectiveness for a certain consumer situation, promoting increases in the number of production units where they are feasible and timely diversification decisions. All of this impacts profitability in a favorable way.
Finally, both collaborative forecasting and the practice of tracking individual contributors’ effectiveness will support organizations’ profit-making ability. They can do so by creating the conditions for producing more accurate and unbiased demand forecasts. In collaborative work, the emphasis is on considering multiple perspectives to understand the big picture of what a business should do instead of making decisions based on one-dimensional information. Contributor evaluation supports the early detection of contributions that need thorough critical assessments.
Kamal, J. (2013). Best practice demand planning meets unprecedented demand volatility. SDCExec. Web.