The case for Demand Planner
In today’s dynamic business environment, having an accurate demand plan is crucial for companies to effectively manage their supply chain, optimize inventory levels, and meet customer demands. When it comes to forecasting, there are three primary approaches to consider: relying solely on sales forecasts, relying solely on statistical forecasts, or adopting a hybrid model that combines both approaches, often referred to as Sales & Operations Planning (S&OP). Each approach has its own benefits and drawbacks, but the key is to select one and measure its historical accuracy to continuously improve and optimize your demand plan for the future.
Option 1 – Sales forecast
The first approach, relying solely on sales forecasts, involves leveraging the knowledge and insights of the sales team to predict future demand. Sales forecasts are typically based on factors such as market trends, customer feedback, and sales pipeline data. This approach allows for a more qualitative assessment of demand and takes into account factors that might not be captured in statistical models. Sales forecasts can be particularly useful when launching new products or operating in highly volatile or niche markets where historical data may not be as reliable.
Option 2 – Statistical forecast
The second approach, relying solely on statistical forecasts, involves using historical data to identify patterns and trends that can be used to predict future demand. Statistical forecasting models utilize advanced statistical techniques and algorithms to analyze historical sales data, seasonality, trends, and other relevant factors. This approach is particularly useful when dealing with large datasets and stable demand patterns. Statistical forecasts offer a more quantitative and data-driven approach, which can be beneficial in industries with a high degree of demand stability and predictability.
Option 3 – Hybrid Model
The third approach, the hybrid model, or S&OP, combines both sales and statistical forecasts. This approach recognizes that both qualitative insights from the sales team and quantitative analysis from statistical models are valuable inputs for accurate demand planning. By combining the two, companies can leverage the strengths of each approach and mitigate their respective weaknesses. S&OP enables a more holistic view of demand, incorporating both market knowledge and historical data analysis. This approach is especially useful when there is a need to balance the accuracy of statistical models with the expertise and insights of the sales team.
The non-option
Now, why is it not an option to pick one of these approaches and evaluate the historical accuracy of your demand plan? The answer lies in the age-old adage: “Whatever is measured, is improved.” By selecting one approach and consistently measuring its accuracy against actual demand, you can identify any discrepancies and areas for improvement. This evaluation process allows you to fine-tune your demand planning methodology, refine your forecasting techniques, and optimize your inventory levels. It also helps you identify any biases or limitations in your chosen approach, enabling you to make informed adjustments to improve the accuracy of your demand plan.
Furthermore, by evaluating the historical accuracy of your demand plan, you can identify patterns and trends in your forecasting errors. This analysis provides valuable insights into the factors that influence demand and helps you better understand the drivers behind forecast accuracy. Armed with this knowledge, you can make more informed decisions and take proactive measures to mitigate risks and uncertainties in the future.
Summing it up
In a nutshell choosing one approach for your demand planning, be it sales forecasts, statistical forecasts, or a hybrid model, is essential for evaluating the historical accuracy of your demand plan. The continuous measurement and improvement of your forecasting methodology are critical for optimizing your supply chain, managing inventory levels, and meeting customer demands. Remember, whatever is measured, is improved. So, pick your approach, measure its accuracy, and embark on a journey of continuous improvement in demand planning.