Demand Planning & Forecasting Best Practices

Today’s global marketplace is much smaller and interconnected thanks to technological advances. Your typical consumer is both capricious and impatient in their buying habits. Their demand for a product can change dramatically in the short time between product design and rollout. How does this impact demand planning and demand forecasting which are by nature inexact fields?

There is no such thing as a 100% accurate forecast. The aim is to reduce the margin of error in the estimates to optimize supply efficiency and ultimately revenue. Though the future is uncertain, continually incorporating demand management best practices can improve accuracy and accountability. 

Demand Planning vs Demand Forecasting

These two terms are often used interchangeably but describe different parts of supply chain management. Demand forecasting specifically refers to the systematic and scientific process of estimating future demand for a product. 

Demand planning is forecasting consumer demand and applying that data to tailoring the most profitable mix of goods to be sold while factoring in the organization’s capacity and constraints. Demand forecasting is a key component of the demand planning part of the management strategy. 

Best Practices for Demand Planning & Forecasting

Measure Forecast Accuracy at All Levels

We have already established that demand planning and forecasting are very imprecise and nuanced fields. That means that each forecast produced could benefit from fine-tuning. An organization should evaluate its forecasts for improvement and accountability, particularly during the S&OP review process. During this process, collecting insights from stakeholders such as customers and marketing teams will help to enrich baseline forecasts.
It is also important to track the FVA(forecast value-added) metric, which evaluates each component of the forecast process, to identify what is of value and what can be eliminated.

Analyze Your Supply Chain

Demand sensing, the process of collecting downstream data, and applying it to supply chain decisions, can positively impact the accuracy of forecasts and consequently revenue. It is crucial to collect end-user and point of sale data to understand what is happening further down in the supply chain. Often, these are huge data sets that need to go through exponential smoothing models to effectively forecast demand. This top-down analysis is instrumental in showing in real-time the efficacy of the demand plan and the necessary improvements. Analyzing the supply chain will also inform contingency plans which go hand in hand with an organization’s demand plan. 

Remember the 80/20 Rule

The 80/20 rule refers to the proportionality that most organizations find to be true. 80% of their revenue comes from 20% of their products. This developed into the ABC classification (Pareto analysis) which is a framework of different codes to forecast product demand based on importance.

In most cases, type A items are important, high-volume products, type B refers to the medium-volume items while the items lagging in demand are type C. This model enables you to focus forecasts on the 20-30% of products that are high-performing and fewer resources to the other groups unless exceptions are observed. A good demand planning software will assign the relevant codes and rank them according to importance. This helps to optimize the forecast process and avoid wasting resources on non-performers 

The recurring theme of demand planning and forecasting best practice seems to be the consistent collection of data, its evaluation and review to tweak your forecasts and plans to the optimum. Of course, to process these various data sets one needs a robust and effective demand planning software which is future proof and can support you for years to come.