Demand Planning Is Essential. Here’s Why.

Demand planning is a business-critical activity that attempts to predict what consumers will want and ensure there will be adequate supply to meet demand.

In a perfect world, no one needs a plan. On the demand side, you know exactly what, when, and why your customer will want a product. On the supply side, you always have the perfect supply to meet that demand. But in the real world, circumstances change. Customers’ desires shift unpredictably. New entrants with competing products capture market share. Viral social media trends shape demand in unexpected ways. Demand planning’s objective is to account for this uncertainty by making assumptions about what customers will want in the future and ensuring that there will be enough supply to meet it. Read on for an in-depth look at demand planning and why it’s important.

Forecasting: The Basis of the Demand Plan 

The first step in demand planning is to develop a forecast of future demand. The supply chain of any organization comprises numerous activities. Often the lead times—the latency between the initiation of a process and its completion—of these activities is longer than the expected order lead times for customers. Consequently, many supply chain activities must be planned based on an estimate of demand or a forecast. A forecast is an estimation of demand based on assumptions about the market and commercial actions.

For instance, a manufacturer of consumer electronics, such as smartphones, has a complex supply chain that involves sourcing raw materials, manufacturing components, assembling the final product, and shipping it to retailers. Each activity has its lead time, ranging from several weeks for sourcing raw materials to several days for manufacturing a component. However, customers’ expected order lead times are much shorter, usually, only a few days or even hours if the product is available for immediate purchase.

To ensure that the smartphone is produced and delivered to customers on time, the manufacturer must rely on a forecast of demand for the product. This forecast is based on assumptions about the market, such as consumer preferences, purchasing habits, and competitor activity.

What Happens When the Forecast is Wrong?

Demand forecasts are generally never 100% accurate. However, the further the forecast deviates from reality (actual demand), the more supply chain risk increases. Thus, there are trade-offs associated with forecasting. Over-forecast and you may maximize sales and customer service levels, but it may also lead to increased costs due to maintaining additional inventory. Under-forecast and you keep inventory levels and associated costs low, but it may lead to decreased profitability due to lost sales from stockouts.

For example, a retail company relying on accurate sales forecasts to manage inventory levels may suffer if the forecasted demand was 1,400 units, but the actual demand was 1,000. In this case, the additional inventory ties up valuable storage space and capital that could have been invested elsewhere. Conversely, if the model predicts low demand of 600 units but the actual demand is 1,000 units, the company risks stockouts, resulting in decreased profitability due to lost sales.

While the above examples use singular numbers to represent an estimate of demand, demand forecasts are generally represented as a range of outcomes rather than one number. For more information on demand forecating, read our article on The Difference Between Forecasting and Demand Planning.

Aligning Supply with Forecasted Demand

After generating the forecast, the next step is collaborating with the supply chain planning function to estimate the risks and costs associated with supporting it. For example, in the case of a seasonal product, like beach balls, the supply chain may need to plan for additional inventory, transportation, and labor costs to cope with the higher demand during the peak season.

The organization finalizes the demand plan using the forecast, estimated risks, and costs. This plan outlines what the organization anticipates selling based on the supply chain’s ability to support it and the associated cost. Regularly reviewing and updating the demand plan to reflect market conditions and the ability of the supply chain to support it is essential.

Why the Transformation of Demand Planning Is Critical

In the not-so-recent past, demand was relatively stable. Companies could rely on what a customer bought and when to predict what he or she would buy and when in the future. But in today’s complex and fast-paced economy, demand is anything but stable. More data about customer behavior and the market are available than ever. Traditional forecasting methods rely heavily on historical sales data and manual adjustments to update the forecast and struggle to make fast and consistently accurate predictions in such a complex and volatile landscape. The result is organizations losing their ability to respond to an ever-changing market.

To overcome these challenges, organizations increasingly realize the need to transform their demand planning processes to be more data-driven, agile, and collaborative. This may entail adopting advanced analytics and machine learning techniques to leverage real-time data to make faster, more informed decisions. It also may entail collaborating more effectively with internal stakeholders like commercial, finance, and procurement teams or more closely with partners like suppliers and logistics providers to ensure the continuity of supply and demand. Whatever strategies or technologies organizations deploy, it’s become increasingly clear: enterprises need better, faster demand planning capabilities.

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