Learning SeriesGuide to Demand Planning

Demand Planning Vs. Demand Forecasting

Introduction

The terms demand planning and demand forecasting are often used interchangeably. Though they are unmistakably linked in the supply chain management process, they are not the same thing. One (forecasting) is an essential function of the other (demand planning).

Demand planning is a process; and accurate forecasts are the results of an effective demand planning process. Demand planning is itself an essential function of the S&OP (Sales and Operations planning) process. Accuracy is essential across the board, and the research data science strategy a company uses to arrives at these numbers is a vital part of the demand planning process.

The following article will define demand planning and forecasting, explain the key differences and similarities between them, and demonstrate how forecast accuracy is essential to demand planning and to the overall S&OP and supply chain planning and management strategies.

What Is Demand Planning?

Demand planning is the process of forecasting demand for a product (or service) and executing an operational strategy across supply chain in order to meet it. Demand planning is a critical business planning function: It tells you where you’re headed, and when, and how to adjust and automate operations and finances accordingly. Dynamic, highly accurate forecasts inspire confident demand plans, which enable teams to work together in order to meet future demand targets within budget and hit revenue goals.

What Is Demand Forecasting?

In the context of supply chain management, forecasting refers to process of predicting demand or predicting sales (which are two in the same). Effective demand planning necessitates highly accurate demand forecasts that should deliver on agreed strategies.

Demand Planning & Forecasting: Best Practices

Accurate forecasts are incredibly hard to come by, let alone rely on. Demand planners must have in place the processes and know-how in order to calculate accurate forecasts. This depends on a company’s ability to:

  1. Understand the full scope and variety of factors that have historically impacted sales, including consumption-driven demand signals, market trends, and brand (own) and competitive marketing and trade investments.
  2. Collect both internal and external historical data on these factors, which should involve strategic data partnerships with companies like Nielsen. Important data includes:
    • Historical shipments and consumption data at both chain and store levels
    • Own and competitive marketing and trade investments.
    • Weather, seasonality, ingredient and health trends, online search behavior, social media mentions and other variables that impact brand
  3. Have at the ready the technological infrastructure and demand and/or supply planning software that’s readily able to ingest the sheer magnitude of data. Businesses employ a variety of forecasting methods and utilize a number of planning tools in the process. Some demand planners prefer the “old school” method of solely using spreadsheets (like Excel) to forecast demand, which can be effective. More and more, however, demand planners are opting for a forecasting tool — also known as a demand planning platform — that can handle massive data sets.
  4. Create forecasting models that overlap the various data sets and statistical factors to calculate future demand.
  5. Continuously iterate these models so that they become more and more accurate, eventually resulting in machine learning models that use Artificial Intelligence to calculate demand predictions in real time.

Why Accurate Forecasts Are Critical to Supply Chain

Demand planners tasked with calculating sales forecasts have a lot of weight on their shoulders. When demand predictions are highly accurate, distribution, inventory, and manufacturing teams will be able to have a clear picture of what they need to do in order to meet demand targets. When demand predictions are inaccurate, the domino effect can be crushing to business:

  • Customers service levels decrease
  • Working capital rises
  • Obsolescence and out-of-stocks rise
  • Transfer and repositioning costs go up
  • Less effective negotiating power with suppliers
  • Efficiencies associated with Line capacity and Capex decrease

However, when demand planners have the right tools with which to build accurate sales forecasts, the benefits are incredible. The entire supply chain benefits, including distribution, inventory, and manufacturing. But this is only true when data is centralized across supply chain. Having one source of truth is essential to demand planning, the S&OP process, and the overall health of business and supply chain. Without it, data is siloed and non-dynamic.